Research Progress on Advanced Characterization Methods for Hydration Interfaces in Wood Micro- and Nanochannels

Abstract

Wood–water interactions are a central focus in wood science, profoundly influencing wood’s physical, chemical, and mechanical properties. These interactions play a decisive role in wood processing, application, and durability. With scientific advancements, research has progressed from the macroscopic scale to fine microscopic levels, focusing on hydration within wood’s micro/nano channels. However, traditional methods are limited by wood’s complex hierarchical structure, making it difficult to accurately analyze water molecule behavior and the influence of interfacial microstructures in confined spaces. This paper reviews recent applications of advanced characterization methods in studying hydration interactions within wood’s micro/nano channels. It details the basic principles of methods such as nuclear magnetic resonance, Fourier transform infrared spectroscopy, and differential scanning calorimetry, along with their specific applications in characterizing wood–water interactions, moisture states, and cell-scale moisture distribution. This review offers new perspectives for understanding hydration in wood micro/nano channels. It reveals that interfacial confinement fundamentally alters the hydrogen bonding network and dynamic characteristics of water molecules, which is crucial for designing next-generation wood-based materials.

1. Introduction

Wood’s interaction with water is a core topic in wood science. Water is a unique polar molecule that can exist in three bulk phases: solid (ice), liquid, and gas (vapor). The solid phase has a rigid structure, while the liquid and gas phases are fluid [1]. More importantly, water forms dynamic, multi-dimensional networks through intermolecular hydrogen bonds. When interacting with wood’s micro- and nanoscale channels, water influences macroscopic moisture adsorption/desorption through phase transitions. Simultaneously, it interacts specifically with cellulose and other component surfaces via hydrogen bonding, profoundly regulating hydration within confined spaces. With the advancement of characterization methods, research has progressed from macroscopic descriptions (e.g., correlations between moisture content and dimensional change) to mechanistic analysis at micro/nanoscales. This includes hydration behavior in confined structures, molecular interactions at solid–liquid interfaces, and hydration mechanisms within wood’s natural micro/nanochannels such as cell lumens, pits, and microfibril interstices [2]. Understanding these hydration mechanisms is key to revealing the intrinsic relationship between wood’s moisture content, structure, and properties. However, wood’s complexity—its hierarchical porous structure, multiple interfaces, and confinement effects—makes this research challenging.

Macroscopic studies reveal overall patterns but struggle to answer core questions: how are water molecules adsorbed and diffused in nanoscale confined spaces, and how do interfacial microstructures regulate water behavior? Wood’s natural structure creates unique confinement environments where hydration exhibits complex characteristics. Water molecules first interact with biomass component surfaces (cellulose, hemicellulose, lignin) via hydrogen bonds, forming ordered or disordered adsorption layers. As moisture content changes, distinct hydration states evolve from monolayer to multilayer adsorption and capillary condensation. Confined by channel dimensions and interfacial properties, water molecules show significantly different diffusion kinetics, hydrogen bond networks, and dynamics compared to bulk free water [3]. Additionally, the charge properties of wood biopolymers, the coexistence of crystalline/amorphous regions, confinement from molecular chain packing, and unique structures like spindle-shaped cells create varied effects of adhesion, capillary forces, and gravity across different cellular structures [4]. These factors collectively influence the diffusion and dynamics of adsorption layers in wood, directly determining its moisture adsorption/desorption rates, swelling–shrinkage behavior, and susceptibility to biological degradation. Therefore, studying nanoscale hydration mechanisms in micro/nano channels is crucial to overcome the limitations of traditional research.

Traditional methods (e.g., gravimetric analysis, dimensional measurement) reveal macroscopic patterns but face bottlenecks: (1) they cannot precisely distinguish different water states (bound vs. free water) or capture nanoscale water dynamics (diffusion pathways, hydrogen bond network reconstruction); (2) due to wood’s hierarchical structure and “scale-dependent” hydration, they struggle to resolve how interfacial microstructures regulate hydration. This leaves the core question, “how does confined water behavior affects macroscopic wood properties”, inadequately answered. Advances in interfacial characterization methods provide new tools. Spectroscopic methods (e.g., FTIR, Raman) can resolve interfacial hydrogen bonding. Nuclear magnetic resonance methods (e.g., low-field NMR, MRI) enable quantitative characterization of water states and spatial distributions [5]; thermal analysis and diffraction methods (e.g., DSC, XRD) reveal correlations between water phase transitions and wood micro-structures [6,7]; and microscopic imaging methods (e.g., Atomic Force Microscopy, fluorescence microscopy) allow visualization of water–solid interfaces [8]. The application of these methods not only advances understanding of hydration mechanisms in wood’s micro/nanoscale channels but also provides a scientific basis for the innovative design of lignocellulosic functional materials (e.g., water-resistant modification, smart responsive materials).

This article systematically reviews the application of advanced characterization methods in studying hydration within wood micro/nano channels. First, it outlines the structural characteristics of these channels and the specificity of hydration, elucidating how confined environments regulate water molecule behavior. Furthermore, it details the basic principles of various advanced characterization methods and their specific applications in studying wood–water interactions, such as water state identification and interface mechanism analysis. Finally, it summarizes core findings, identifies key unresolved challenges, and highlights the field’s potential to advance wood science and expand applications in areas like water purification and bio-based functional materials, offering conceptual and methodological insights for further microscale research.

2. Structural Characteristics of Wood Micro/Nano Channels and Moisture State

Through long-term evolution, organisms have developed structures with a high degree of adaptability for interacting with water [9]. Wood, a natural polymer with cross-scale hierarchical porous structures, exemplifies this adaptability through its micro/nano channels and the state of moisture within them. Natural micro/nano channels in wood include tracheids, vessels, pits, and microfibril gaps. Artificial channels can be constructed via delignification, chemical modification, or drying [10]. The types and states of water in wood are closely related to channel structure. Channel size, morphology, and surface chemistry determine moisture adsorption, diffusion, and phase transition. Conversely, moisture affects channel stability and mechanical properties. This dynamic interaction shapes wood’s unique hydration characteristics, providing a key micro-structural basis for understanding its macroscopic properties.

2.1. Formation of Wood Micro/Nano Channel Structure

2.1.1. Formation of Natural Wood Micro/Nano Channels

Over 270 million years of evolution, trees have developed a highly optimized multi-scale structural system through natural selection. This system provides exceptional water transport efficiency, mechanical stability, and environmental durability. The three-dimensional, multi-level pore network in natural wood is a sophisticated conduction system developed by plants, closely related to xylem differentiation [11]. As the primary water-conducting tissue, xylem vessel elements form hollow tubes through programmed cell death. During secondary growth, a pore system with specific morphology and function is established. This micro-to-macro hierarchical structure determines a plant’s water transport efficiency and drought resistance. It also directly influences wood’s mechanical performance and durability, defining its value in construction, papermaking, and bioenergy.

The micro-nano pore system is an adaptive structure from long-term plant evolution, closely tied to the programmed differentiation of xylem cells (Figure 1a). Figure 1a illustrates representative cell types in wood; hardwoods exhibit a more differentiated cell system including fibers, vessel elements, and parenchyma cells. During cell wall development, cellulose microfibrils (2–4 nm) form via β-1,4-glycosidic bonds. Arranged in spirals, they create a three-dimensional network and multi-level pores: 2–5 nm microfibril gaps, 50–200 nm micropores, and lumens from micrometers to millimeters, forming graded water transport channels [12]. Xylem vessel development directly shapes pore function. Gymnosperm tracheids are elongated cells (typically less than 1 cm in length) that serve both mechanical and conductive functions; their structure reflects adaptation to relatively stable hydraulic environments. Angiosperm vessels, developed from continuous vessel elements, form meter-long continuous lumens after end wall dissolution, improving long-distance transport efficiency [13]. Vessel elements become hollow tubes via programmed cell death. Cell walls undergo lignification and secondary thickening, forming structures like spiral- or ladder-like perforation plates to enhance strength and optimize water transport. For example, camphor plants have both single and ladder plates, while tung plants have more efficient single plates, reflecting ecological adaptation [14]. The spatial distribution of cell wall components also aids structure formation. Lignin is in the interstices, while cellulose and hemicellulose concentrate in the S2 layer. Microfibril polymerization produces 3–7 nm nanopores matching water molecule diameter, ultimately forming a natural multi-scale micro–nano pore system [15].

Micro-nano pore structure directly determines functional adaptability. Tall trees like redwood develop highly filamentous nano-networks of vessels, increasing water transport cross-section and reducing embolism sensitivity by regulating pore channel curvature. Species like walnut achieve a balance between water transport efficiency and mechanical properties through a gradient pore distribution. Under drought or freeze–thaw stress, water in the xylem lumen can reach a negative pressure metastable state. Under drought or freeze–thaw stress, water in the xylem can be under tension (negative pressure), which can lead to cavitation and embolism formation [16]. This embolism is a key limiting factor for plant survival in extreme environments. The precise pore structure is a stress-resistance solution from long-term evolution. The micro–nano structure of natural wood achieves a high degree of unity in water transport, mechanical properties, and environmental adaptability through its multi-level pore system, component distribution, and physio-biological synergy, providing a structural foundation for practical applications [15].

2.1.2. Formation of Artificial Wood Micro/Nano Channels

Wood is a natural porous material. Its cell lumens, pores, cell wall pores, and microfibril gaps form a hierarchical porous structure, which is the basis for wood processing and functional transformation. Regulating artificial wood micro-nano pores is essentially a targeted study of the wood–water interaction mechanism. Modifying natural wood’s porous structure via physical, chemical, or biological means, especially delignification, provides a controllable model for revealing wood–moisture interactions (Figure 1b).

Physical methods alter internal pore structure through external energy or mechanical action, achieving controllable regulation. These include drying, steam explosion, microwave, compression, and carbonization treatments. For example, microwave drying uses electromagnetic waves to act on internal moisture, promoting rapid evaporation and forming a more uniform pore structure between cell walls and lumens, improving drying efficiency and pore distribution [17]. Biological methods use enzymatic hydrolysis or microbial degradation to selectively modify wood cell walls, forming desired micro-nano pores. For example, cellulase and xylanase can selectively degrade hemicellulose and some cellulose, creating more microporous structures in the cell wall and enhancing adsorption and permeability [18]. Both physical and biological methods can effectively regulate pore structure, yielding higher specific surface area, richer pore types, and superior functional properties.

Chemical methods involve delignification or chemical reactions to alter wood’s composition and microstructure, constructing specific pores. For example, removing lignin detaches wood cells, exposing cellulose microfibrils and generating more nanopores [19]. Treatments with TEMPO, sodium chlorite, sodium hypochlorite, sodium hydroxide, or hydrogen peroxide can break chemical bonds between wood components, achieving in situ exfoliation and forming rich mesoporous structures [20]. During delignification, lignin is removed from the cell wall. Severe delignification can form mesopores and even cause cell separation. Delignified wood shows low adsorption hysteresis, mainly due to molecular chain sliding in hemicellulose and amorphous cellulose after lignin removal. When water molecules adsorb onto the hydrophilic cell wall matrix, the S2 layer expands, affecting physical properties. The average pore size of the cell wall in a water-saturated state is about 2.5 times that at moisture absorption equilibrium from a dry state [21].

Artificial wood micro-nano pores retain the hierarchical structure of natural wood. Their water absorption behavior and moisture states are deeply influenced by pore structure and modification method, showing unique patterns. Whether it is the sub-millimeter 3D network preserved by physical methods and the strong capillary force from high-aspect-ratio nanowires, the opened microfibril gaps from chemical delignification, or the rich hydrophilic sites from retained hemicellulose in biological methods, all directly shape water transport paths. Simultaneously, moisture state relates to pore size distribution. The proportion of strongly hydrogen-bonded “ice-like” bound water in nanopores, semi-bound water with limited flow in mesopores, and free water in macropores changes dynamically with the degree of lignin removal and component retention [22]. Therefore, artificial wood micro-nanochannels, with controllable pore parameters (size, connectivity, surface chemistry) and clear variables (lignin removal degree, modification method), are useful for studying the dynamic interaction between water and pores.

It is essential to recognize that artificially constructed wood micro/nano channel models, especially deeply delignified or chemically modified systems, represent highly simplified model systems. By removing lignin and part of the hemicellulose, they significantly reduce chemical complexity and may alter pore wall surface energy and mechanical properties. Consequently, the moisture transport, sorption behavior, or mechanical response observed in these models may differ quantitatively or even qualitatively from that in intact, natural wood. For example, the dominance of capillary effects may be amplified in artificial models, while the complex barrier effect of the lignin–carbohydrate complex on moisture diffusion in natural wood is diminished. Therefore, the primary value of artificial models lies in isolating variables and elucidating the mechanism of specific single factors (e.g., cellulose nanopore geometry). Conclusions drawn from them must be applied to natural wood with caution and require validation through comparative experiments on natural specimens.

2.2. The Status and Classification of Moisture in Wood

2.2.1. Introduction and Classification of Water

Water is the most abundant and important substance in nature [23]. As a special polar molecule, it consists of one oxygen and two hydrogen atoms, with a H-O-H angle of ~104.5° [24]. It readily forms abundant hydrogen bonds between molecules and has an extremely complex phase diagram. Understanding water’s micro-structure and dynamics is significant for fundamental research and new technologies. It is important to note that the classification of water in wood is based on operational definitions linked to experimental methods and exists on a continuum. “Bound water” refers specifically to water strongly hydrogen-bonded to cell wall polymers, identified by its non-freezable nature and restricted mobility. “Free water” refers to water in lumens with bulk-like properties. The term “interfacial” or “confined water” describes water in nano-scale environments (e.g., between microfibrils) whose properties are perturbed by confinement; it represents a transitional state whose measured characteristics can vary depending on the probe technique’s spatial and temporal resolution.

Main water types include bound, free, intermediate, interfacial, confined, ionic, and capillary condensation water [25]. In heterogeneous porous media, water molecules, prone to environmental and temperature influences, interact physically and chemically with the matrix, with chemically bound water predominating over free and physically bound water. Bound water, tightly linked via hydrogen bonds, electrostatic forces, or physical adsorption [26], has low fluidity and rarely participates in chemical/microbial processes, whereas free water resides in gaps with weak matrix interactions, high mobility, and involvement in metabolism, dissolution, and nutrient transport [27]. Intermediate water, a transitional state adsorbed on bound water via weak hydrogen bonds [28], and “unfrozen water” [29] do not reflect total bound water content or polymer–water interaction strength, arising from water crystallization–polymer glass transition interplay. Capillary condensation water forms in microcapillaries (<10 μm radius) via capillary action [30]. Interfacial water formation depends on the dispersion medium’s interface type and area [31]. At low temperatures, monolayer water adsorbs on charged surfaces; strongly bound interfacial water exists as dense single molecules/clusters, while weakly bound water forms continuous layers with HDW (High-Density Water) and LDW (Low-Density Water) [32], exhibiting higher mobility and distinct temperature dependence [33]. In microconfined environments, strong solid–water interactions drive hydrogen bond network reconstruction, forming 2D ordered hydration shells [34], with confined water properties influenced by environmental size, shape, and interface characteristics. Ionic water, an aqueous electrolyte solution, forms hydrated ion layers whose thickness/stability affect conductivity and solubility [35].

2.2.2. Moisture in Wood

As a natural porous material, wood’s properties are closely tied to its moisture content. Moisture affects wood’s physical and mechanical properties, processing, use, and durability. Moisture content directly influences drying shrinkage and swelling. As a multi-scale hydrophilic polymer, wood contains bound water (hydrogen-bonded to the cell wall), free water and water vapor in the cell lumen, and microcapillary water in the capillary system (

Table 1. Comparison of characteristics and impacts of different types of water in wood.

Type of Water	Location	Binding Mode/Force	Core Properties	Migration Characteristics
Bound Water	Cell Wall	Hydrogen bonds	High viscosity and chemical potential; Low molecular mobility	Slow and complex; Requires overcoming hydrogen bond
Free Water	Cell Lumen, Large Pores	No strong hydrogen bond binding	Low viscosity and chemical potential; High molecular mobility	Smooth flow; High migration rate
Microcapillary Water	Micro capillary system	Capillary force	Properties between bound water and free water	Transported via capillary action
Water Vapor	Between Wood Pores and External Atmosphere	No direct binding;	dynamically changes with environmental humidity	Diffusion (moisture absorption/desorption)

) [36]. These forms are interrelated and transform within wood’s microstructure, collectively affecting its properties.

Bound water plays an important role in multi-component water migration during wood drying. The distribution, drying method, and relaxation time of bound and free water in wood differ. There is still controversy regarding how wood interfaces affect bound water structure and properties and the structure of water in confined environments. Hydrophilic components like cellulose and hemicellulose bind tightly to the cell wall via hydrogen bonds with water, related to the cell wall’s chemical composition and micro-structure. In wood drying, bound water movement is a slow, complex process requiring overcoming hydrogen bond constraints, critically impacting drying rate and quality [37]. Bound and free water differ significantly in structure, properties, thermodynamics, and kinetics. Bound water in wood cell walls is often described as having an ‘ice-like’ structure due to its ordered hydrogen-bonding network, but its density is typically higher than that of bulk water. Its diffusion coefficient is ~0.0687 cm²/h, and actual migration is slow due to cell wall constraints [38]. In nuclear magnetic resonance detection, the T₂ relaxation time of bound water is relatively short (0.1–10 ms), indicating a lower degree of molecular motion freedom; at the same time, it also has a high glass-forming ability, making it easier to form a glassy state at low temperatures, which can affect the stability of wood in low-temperature environments.

Free water resides in large pores like cell lumens, not strongly bound by hydrogen bonds. Its density is low (~1 g/cm³), with low viscosity and chemical potential, and a diffusion coefficient of ~0.0916 cm²/h. Flow within wood is relatively smooth. Free water has a longer T₂ relaxation time (10–100 ms), more free molecular motion, lower glass-forming ability, and is more prone to phase transition with temperature. When moisture content exceeds the fiber saturation point, free water appears in the lumen, significantly affecting wood’s physical and mechanical properties (e.g., dimensional stability, strength). For example, wood volume expands significantly as bound water enters the cell walls; free water in lumens contributes little to swelling. Microcapillary water exists in wood’s microcapillary system (gaps between microfibrils, cell wall micropores), typically nanometers to micrometers in size [39]. Its existence depends on capillary force, and its properties are between bound and free water. During water migration, especially at low moisture stages, microcapillary water is an important transport carrier via capillary action.

Water vapor exists between wood pores and the external atmosphere, in equilibrium with external vapor. Its content varies with environmental humidity. High humidity leads to vapor absorption and increased moisture content; low humidity leads to vapor diffusion outward and decreased moisture content. The potential of free water and capillary water in the lumen is similar, allowing relatively easy transformation. Bound water potential decreases with moisture content; lower moisture content means stronger binding to the cell wall and stronger hydrogen bonding.

The multi-layered hierarchical structure of wood (from nanoscale microfibril gaps to micrometer sized cell cavities) creates structural and property differences between free water and bound water at the spatial scale, which in turn affects the distribution and existence of water [40]. For example, in the nanoscale pores of the cell wall, bound water mainly exists, while in large spaces such as the cell cavity, free water is predominant. The difference in spatial distribution results in varying rates and modes of moisture change for different types of wood during the process of moisture absorption or desorption [41]. At present, there is still significant controversy regarding the impact of wood interfaces on the structure and properties of bound water, as well as the structure and properties of water in confined environments. For example, how the chemical composition of the cell wall surface (such as the distribution of lignin and hemicellulose) affects the hydrogen bonding network that binds water, and how the micro nano scale pore structure restricts the molecular movement of water, are crucial for a deeper understanding of the interaction mechanism between wood and water.

2.3. Hydration Interactions in Wood’s Micro/Nano Channels

The micro/nano channels of wood and hydration interactions in wood constitute a key research field in wood science and engineering, encompassing wood’s structural properties, moisture behavior, and its application potential in the development of functional materials. As a natural porous material, wood contains abundant micro/nano porous structures in its cell walls; these structures not only determine wood’s physical and mechanical properties but also influence the mechanism of its interaction with water molecules. In-depth research on wood’s micro/nano channels and hydration interactions is conducive to improving the comprehensive utilization rate of wood and promoting its application in fields such as adsorption, sensing, energy storage, and building materials.

The ultimate goal of deciphering hydration behavior at micro/nano interfaces is to explain and predict the macroscopic properties of wood. This “scale-bridging” can be conceptualized through a mechanistic chain: the specific hydrogen bond interactions of water with cell wall polymers at the nanoscale (probed by FTIR, QENS) dictate local adsorption energetics and bound water structure. These localized events integrate at the cell wall scale, generating swelling stresses (quantified via NMR or inferred from DSC data) that deform the microfibril network. The collective deformation of cell walls then propagates, causing anisotropic swelling at the tissue scale (measurable macroscopically). Consequently, the cooperative response of myriad cells manifests as the bulk material’s hygroscopic expansion, modulus reduction, and altered permeability. Thus, the nanoscale “mechanisms” revealed by advanced characterization are indispensable for constructing predictive, multi-scale models that link fundamental interfacial science to engineering performance.

2.3.1. Hydration Interactions and Hydrogen Bonds

The hydration of wood exhibits particularities. In a broad sense, it refers to the process where water molecules form bound water with the surface or internal groups of a substance via hydrogen bonds, van der Waals forces, and other interactions. As a natural hydrophilic material, wood hydration includes not only the chemical bonding between water molecules and hydrophilic groups but also physical adsorption and capillary condensation within channels. Its essence lies in the multi-dimensional interaction between water molecules and the surface of micro/nano channels, with polar attraction and hydrogen bonding serving as the core driving forces [42]. The polarity of water stems from its molecular structure: the electronegativity of oxygen atoms is much higher than that of hydrogen atoms, and the asymmetric structure of the H-O-H angle causes the oxygen end to carry a partial negative charge and the hydrogen ends to carry partial positive charges [43]. This characteristic enables water molecules to form directional attraction with polar groups on the channel walls, such as the hydroxyl groups of cellulose and the glycosidic bonds of hemicellulose.

Hydrogen bonds are the key interaction force: a hydrogen atom in one water molecule can form a hydrogen bond with the oxygen atom of another water molecule (Figure 2a). Though individually weak, these bonds are vast in quantity. In nanoscale channels, the narrow space enhances the contact between water molecules and the channel surface, leading to a significant increase in the density of hydrogen bond interactions. Each water molecule can form hydrogen bonds with multiple hydroxyl groups, while water molecules are also connected via hydrogen bonds to form an ordered network and layered bound water, thereby restricting their molecular motion [44]. In contrast, in microscale channels (e.g., cell lumens), the larger spatial scale increases the distance between water molecules and the channel surface, weakening hydrogen bond interactions. Here, water mostly exists in the form of free water and migrates primarily via capillary forces; at this point, the interactions between water molecules are stronger than their binding to the channel surface. Furthermore, the dynamic formation and breaking of hydrogen bonds determine the migration and diffusion of water within the channels, regulating the progress of hydration.

2.3.2. Hydration Interactions and Cellulose

Cellulose is a polymer composed of D-glucopyranose units linked by β-1,4 glycosidic bonds, and its degree of polymerization can vary depending on the tree species. It exhibits characteristics such as specific molecular conformation, crystallinity, amphiphilicity, bonding modes (intermolecular and intramolecular hydrogen bonds), and metastable configurations. These characteristics profoundly influence its interaction with water molecules, thereby determining the properties of wood hydration. The specific molecular conformation of cellulose is of great significance for studying the interaction between cellulose and water [45]. The hydrogen bonding interactions between molecular chains and within molecules are enhanced; coupled with the effects of non-covalent bonds such as ionic bonds, electrostatic attraction, and van der Waals forces, cellulose readily interacts with water molecules.

As shown in Figure 2b, the metastable configurations of cellulose mainly refer to Ia and Ib, which are the most widely distributed forms in natural Cellulose I within cell walls [46]. Molecular simulations have shown that the (110) crystal plane of Cellulose I exposes hydroxyl groups and exhibits stronger water-binding capacity compared to the (100) plane of Cellulose I, thus possessing better wettability and providing favorable conditions for hydration [47]. The ordered arrangement of polar groups in cellulose ensures short-range hydrogen bonding with other polar groups in an aqueous environment [48].

The amphiphilicity of cellulose molecular chains allows water molecules to not only wet the surface but also migrate rapidly. This is primarily because cellulose contains both polar and non-polar regions, and the intramolecular hydrogen bonding in the molecular chains also accelerates the hydrophobic effect of C-H bonds. Additionally, cellulose layers exhibit molecular-level roughness. In wet cellulose layers, charged cellulose chains or “molecular fibrils” extend approximately 100 nanometers from the surface, and traditional theories cannot explain the interfacial forces between cellulose interfaces [49].

2.3.3. Interfacial Hydration

Interfacial water plays a crucial role in numerous important phenomena of wood. Investigating the chemical structure–property relationships of wood components such as cellulose and hemicellulose, as well as their interactions with the hydration environment, is of great significance for understanding the interactions between water and wood’s amphiphilic components.

Wood’s interfacial hydration exhibits diverse states under different scenarios. The hydrophilic amorphous regions of wood cell walls (e.g., hemicellulose and lignin matrices) adsorb a large number of water molecules, forming rigid hydration shells that bind firmly to the interface; the cohesion of water molecules contributes to the stability of the cell wall interface. Water-soluble components in wood can affect the growth of hydrate crystals through surface adsorption, and the crystallization rate of free water confined in wood’s micro/nano channels is slow. In wood matrices with one-sided hydration, there exist a Minimum Hydration Layer (MHL), an Immersion Layer (IL), and a Full Hydration Layer (FHL). The MHL absorbs 5–10% (w/w) water within 1 h, reaching a total water content of 29–39% and an unfrozen bound water content of 28–29% at 4 h [50]. In ion environments related to wood, the water content is approximately 50% at 4 h, with unfrozen water accounting for 28% and a T₂ relaxation time of <10 ms. The FHL consists of the gel state of wood cell walls and the sol layer in cell lumens, with a water content of 85–86% and unfrozen water of 11%. When wood is at the air–water interface, capillary force dominates; water adsorption layers exist on the cell wall surface and fill microscopic pores (e.g., gaps between microfibrils), playing a core role. The first molecular layer of wood in contact with aqueous solutions is highly ordered, with reduced translational and orientational mobility of water molecules, its behavior resembles supercooled water or amorphous ice rather than being simply “ice-like” [51].

The hydration at the interface of wood exhibits unique hydration characteristics and mechanisms due to the presence of hydrogen bonds. The synergistic effect of interfacial water is reflected in the ultrafast breaking and reformation of complex hydrogen bond networks, which affects the OH stretching vibration frequency of water molecules and the hydrogen bond configurations of adjacent and distant water molecules [52]. The adsorption process between wood and water involves physical or chemical bonds. Adsorption isotherm models are used to study the interaction between water and wood, enabling the prediction of monolayer–multilayer adsorption processes, the number of adsorption sites, and adsorption enthalpy [53]. The adsorption process includes two regions: capillary condensation and the formation of multimolecular adsorption layers. In multilayer or hydration adsorption models, the relative humidity (RH) range of 0–98% is the hygroscopic range, and 98–100% is the over-hygroscopic range. When RH < 50%, water is mainly bound via hydrogen bonds with cellulose and hemicellulose in the cell wall; when RH = 100%, water absorption primarily occurs through capillary condensation in large voids such as cell lumens. At high RH, strongly bound water acts as a substrate layer for loosely bound secondary water, and water is adsorbed at wood’s hydrophilic sites and forms clusters.

2.3.4. Confined Water in Wood’s Micro/Nano Channels

Confined water in wood’s micro/nano channels refers to interfacial water restricted within its one-dimensional (1D) and two-dimensional (2D) spaces (Figure 2c), such as the water involved in intramolecular hydration of microfibrils in wood fibers [54]. It differs significantly from free water in terms of structure and dynamic properties, and often exists in this form when participating in biological activities, similar to the interfacial water confined inside carbon nanotubes. Confined water must interact with various interfaces and molecular groups of wood; the magnitude of its unusual properties stems from the formation of an extended hydrogen bond network. Moreover, water molecules confined in low-dimensional spaces exhibit special properties distinct from free water, mainly due to the interface size of wood’s micro/nano channels and the strong interactions between water molecules and the interface.

Confined water in wood’s micro/nano channels possesses numerous special properties, such as anisotropic dielectric response, a diffusion coefficient different from that of free water, and local structures of hydrogen bond networks in individual water molecules or clusters [55]. Unlike free water, it has a relatively higher density and viscosity, lower diffusion coefficient, faster spin-lattice relaxation process, stronger glass-forming ability, and lower freezing temperature. The formation of the hydration layer restricts the migration of water molecules; the hydrogen bond breaking rate of water molecules in the hydration layer is lower than that of free water. The translation and rotation of interfacial water in the hydration layer are time-dependent, resulting in a slower average rate of hydrogen bond breaking between water molecules in the hydration layer compared to that in bulk water [56].

Figure 2. (a) Optimized structures of the adsorption of single water on hydroxyl group, carboxyl group, carboxyl and ether, hydroxyl and carbonyl group, showing the molecular interactions between water and wood components [42]. (b) H-bonding system in two cellulose allomorphs, cellulose Ib and cellulose II, illustrating structural differences affecting water interaction [46]. (c) A schematic overview of the research characteristics of wood from tissue scale to nanoscale, summarizing the multi-scale approach to wood research [57].

Figure 2. (a) Optimized structures of the adsorption of single water on hydroxyl group, carboxyl group, carboxyl and ether, hydroxyl and carbonyl group, showing the molecular interactions between water and wood components [42]. (b) H-bonding system in two cellulose allomorphs, cellulose Ib and cellulose II, illustrating structural differences affecting water interaction [46]. (c) A schematic overview of the research characteristics of wood from tissue scale to nanoscale, summarizing the multi-scale approach to wood research [57].

As a confined structure, wood’s micro/nano channels form weak hydrogen bond interactions between the internal water and the matrix. Confined spaces generally reduce the entropy and bond energy of water molecules. Confined water exists as a water molecule film adsorbed on wood’s pores, and the vapor pressure generated during evaporation is related to the channel diameter. For water in ultra-fine nanochannels, liquid mobility is more strongly hindered than that of water in the macroscopic state. A water film with a thickness of 1–7 nm in the channels can remain stable even under high vacuum, which is attributed to the curvature, nanoscale roughness, and confinement effect of wood channels—these factors lower the vapor pressure of water, thereby inhibiting evaporation. For example, in carbon nanotubes, the favorable confinement properties of water change drastically with diameter: when the diameter is 0.8–1.0 nm, a vapor-like phase is exhibited with maximum entropy increase; when the diameter is 1.1–1.2 nm, a low-entropy ice phase is formed; and when the diameter is >1.4 nm, a bulk water state appears with increased migration entropy [58]. When the diameter of carbon nanotubes ranges from 2.5 to 7.5 nm, studies on radial density distribution, radial hydrogen bond distribution, and contact angle have shown that a water droplet can contain up to 4632 water molecules, and the contact angles on the inner walls of carbon nanotubes with different diameters range from 103° to 109°. The water film inside the tubes can also remain stable under high vacuum—these characteristics share similarities with the properties of confined water in wood’s micro/nano channels [59].

3. Application of Advanced Characterization Methods in Hydration Research

The behavior of different water types in wood—bound water, capillary water, free water, water vapor, and interfacial/confined water—varies fundamentally depending on their location, binding mechanisms, and properties. To accurately decipher these behaviors, advanced characterization methods tailored to each water type have become indispensable. As summarized in

Table 2. Advanced characterization methods mapped to water types and structural scales in wood.

Water Type	Core Characterization Methods	Key Information Provided
Bound Water	LF-NMR FTIR/ATR/Micro-FTIR DSC NIR Spectroscopy	Content & mobility (T2 relaxation) Hydrogen bond strength & configuration Non-freezable water content & phase transitions Moisture content quantification
Microcapillary Water	MRI Cryo-SEM DSC Gravimetric Analysis	2D/3D spatial distribution Visual morphology & storage sites Freezing/melting behavior Macroscopic water content
Free Water	MRI Cryo-SEM Gravimetric Analysis	Spatial distribution & migration Visual confirmation in lumens Quantitative mass changes
Water Vapor	Dynamic Vapor Sorption (DVS) Gravimetric Analysis	Sorption isotherms Diffusion kinetics Thermodynamic parameters
Interfacial/Confined Water	QENS SANS AFM THz-TDS	Diffusion coefficients & dynamics Nanoscale structure (bundle size/spacing) Molecular-scale hydration layers Collective H-bond network vibrations

, these methods span multiple domains, including spectroscopy, nuclear magnetic resonance (NMR) and imaging, neutron and X-ray scattering, thermal analysis, and microscopic imaging. Each technique probes a distinct dimension: molecular vibrations, proton relaxation, atomic-scale structure, thermophysical properties, or spatial morphology. Collectively, they provide critical information on water content and mobility, hydrogen bond strength, spatial distribution, dynamic diffusion, and interactions with the crystalline matrix. This suite of tools overcomes the limitations of traditional methods—such as inadequate resolution, lack of specificity, or invasiveness—which often fail to distinguish between water states or capture microscopic dynamics. More importantly, they establish an integrated, multi-scale characterization framework that links molecular-level interactions to macroscopic phenomena.

3.1. Spectroscopic Methods

3.1.1. Infrared Spectroscopy Method

The infrared spectroscopy method captures characteristic infrared absorption peaks generated by molecular vibrations, enabling it to sensitively reflect the interactions between water molecules and wood components such as cellulose and hemicellulose. It serves as a crucial tool for studying the state of water in wood. When using infrared spectroscopy to investigate wood hydration, common methods include Fourier transform infrared (FTIR) spectroscopy, two-dimensional correlation spectroscopy (2DCOS), near-infrared (NIR) spectroscopy, and microscopic infrared spectroscopy. These methods reveal the interactions between wood and water from different dimensions.

Fourier transform infrared spectroscopy can study changes in hydroxyl accessibility and hydroxyl vibrations during humidity variations. It can characterize the formation and strength of hydrogen bonds, the state of water molecules, calculate bound water content, and analyze the vibrational relaxation and rotational dynamics of water molecules. Jing Yuan et al. used infrared spectroscopy to investigate the interactions between macromolecules in fibers and parenchyma cells and water at different humidity levels. Their study showed that the absorption band in the wavenumber range of 3600–3000 cm⁻¹ is affected by changes in relative humidity (Figure 3a,b) [60]. This band includes hydrogen bonding of internal hydroxyl groups, prevalent intramolecular and intermolecular hydrogen bonds, and hydroxyl groups in cellulose macromolecules.

Two-dimensional correlation spectroscopy can be applied to analyze spectral intensity fluctuations caused by external physical or chemical perturbations (e.g., temperature, pressure, electric field strength, concentration). It can identify overlapping sub-peaks within the broad hydroxyl stretching vibration band and more clearly elucidate the sequence of changes in different hydrogen bonds during heating. Yilu Guo et al. used two-dimensional correlation spectroscopy and moving window methods to study in detail the structural changes of hydrogen bonds in cellulose acetate during heating [61]. This technique enabled the identification of four types of intermolecular and intramolecular hydrogen bonds in cellulose acetate: 3461 cm⁻¹ (intra-chain v1), 3478 cm⁻¹ (intrachain v1’), 3369 cm⁻¹ (intrachain v2), and 3409 cm⁻¹ (intrachain v3). Additionally, as adsorbed water was desorbed, the peak at 3461 cm⁻¹ tended to shift to 3478 cm⁻¹, indicating that intermolecular hydrogen bonds are relatively strong but unstable, while intramolecular hydrogen bonds are relatively weak but stable (Figure 3c,d).

Near-infrared spectroscopy methods detect the moisture content of wood samples by measuring their absorbance after being irradiated with near-infrared light; samples with different moisture contents exhibit varying abilities to absorb near-infrared light energy. The advantages of near-infrared spectroscopy for measuring wood moisture content include non-contact detection, short testing time, good information timeliness, high accuracy, no damage to the sample structure, and the ability to detect multiple components. However, it suffers from data overlap and weak signal intensity, requiring the use of algorithms for data processing. Long Liang et al. explored the application potential of near-infrared (NIR) spectroscopy combined with partial least squares (PLS) regression for determining the moisture content and basic density of poplar wood chips [62]. By collecting NIR spectra of wood chip surfaces, calibration models for predicting moisture content and basic density were established, and multiple spectral preprocessing methods were employed to improve the accuracy and robustness of the prediction models (Figure 3e,f). The models were tested using a fully independent sample set, and the results showed acceptable predictive performance for both moisture content (prediction coefficient of determination [R2p] = 0.98, standard error of prediction [SEP] = 2.51%) and basic density (R2p = 0.87, SEP = 17.61 kg/m³).

Furthermore, the combination of infrared spectroscopy methods with hyperspectral imaging methods provides a non-contact, non-destructive, and high-precision solution for water detection in wood. Te Ma et al. used near-infrared spectroscopy and hyperspectral imaging methods to develop a method for rapid visualization of the dynamic distribution of free water, weakly hydrogen-bonded water, and strongly hydrogen-bonded water in wood during the drying process [63]. By combining spectral data with partial least squares regression (PLSR) and principal component analysis (PCA), quantitative and visual analysis of changes in wood moisture content and water molecule structure was conducted. The results showed that the distribution and dynamic migration of free water, weakly bound water, and strongly bound water could be distinguished using PCA principal component scores, enabling image display with high spatial resolution (Figure 3g–i).

Microscopic infrared spectroscopy combines the “molecular fingerprinting” capability of infrared spectroscopy with the “high-magnification” function of microscopy. It allows both microscopic observation of morphological features and infrared spectroscopic analysis of molecular structures, enabling accurate analysis of the composition of micro-regions without damaging the sample. Xin Guo et al. used in situ microscopic Fourier transform infrared spectroscopy (micro-FTIR) combined with a custom-designed sample cell to systematically analyze the molecular interactions between adsorbed water molecules and wood [64]. The results showed that carbonyl groups (C=O and C-O) in wood achieve adsorption by forming hydrogen bonds with water molecules, which is specifically reflected in the significant shifts in the spectral bands at 1733 cm⁻¹, 1604 cm⁻¹, and 1236 cm⁻¹. Further component band analysis of the spectra in the 2900–3700 cm⁻¹ wavenumber range identified three states of water molecules with different hydrogen bonding strengths: 3178 cm⁻¹ (strong hydrogen bonds), 3514 cm⁻¹ (moderate hydrogen bonds), and 3602 cm⁻¹ (weak hydrogen bonds). These three states of water molecules change with variations in relative humidity.

While infrared spectroscopy provides a direct “fingerprint” of molecular vibrations, its application to the complex wood–water system necessitates critical interpretation. The broad and overlapping O-H stretching bands encompass contributions from both wood polymers and water, making deconvolution or two-dimensional correlation spectroscopy (2DCOS) essential but model-dependent. Discrepancies in assigning specific sub-bands to different water populations (e.g., “ice-like” vs. “liquid-like”) can arise from different fitting algorithms. Furthermore, methods like FTIR and micro-FTIR are primarily surface-sensitive, offering limited information on water distribution deep within the cell wall without destructive sectioning. Sample history, especially drying during preparation, can irreversibly alter the hydrogen-bonding network, affecting spectral reproducibility and complicating comparisons across studies that employ different preparation protocols.

3.1.2. Terahertz Spectroscopy Method

Terahertz (THz) waves lie between microwaves and infrared radiation, featuring low energy, non-ionization, and strong penetrability. They can excite molecular vibration, rotation, and lattice modes, making them particularly suitable for detecting weak interactions such as hydrogen bonds and van der Waals forces. In recent years, terahertz spectroscopy has been used to detect hydrogen bonds and the state of water in wood, analyze the absorption coefficients and refractive indices of wood samples with different moisture contents, and establish high-precision prediction models. The terahertz spectrum of water has three main peaks (Figure 4a): the first peak is in the range of 5–30 THz, corresponding to the vibration mode of the hydrogen bond network in water; the second peak is in the range of 45–50 THz, corresponding to the bending vibration of the internal bond angle of water molecules; and the third peak is in the range of 90–105 THz, corresponding to the stretching vibration of the internal bond length of water molecules. Based on this, Duan Tong-Chuan et al. studied the terahertz absorption spectra of different water models and the influence of temperature, further correlating THz spectroscopic observations with hydrogen bond dynamics [65]. They established a linear relationship between the central frequency of the collective hydrogen bond network vibration peak and the hydrogen bond lifetime. They found that each model could qualitatively describe the vibration mode of bulk water, but there were quantitative differences in the frequencies corresponding to the absorption peaks. Further research showed that the stronger the hydrogen bond interaction in water and the tighter the constraint of the hydrogen bond network, the higher the central frequency of the vibrational absorption peak (blue shift), and the hydrogen bond lifetime had a linear relationship with this central frequency. These findings are helpful for understanding the dynamics of the hydrogen bond network and molecular-scale vibrational motion in water.

Based on the detection and identification of hydrogen bonds by terahertz methods, the state of water in wood can also be studied. To achieve more detailed analysis of terahertz spectra, terahertz spectroscopy is often combined with analytical methods such as two-dimensional correlation spectroscopy, principal component analysis (PCA), and machine learning, which greatly expands the application of terahertz methods in the study of wood moisture. Min Zhang et al. used terahertz time-domain spectroscopy (THz-TDS) to investigate the terahertz spectral differences in four wood species (manglietia, amur linden, black walnut, and ebony) in the frequency range of 0.1–0.9 THz [66]. They applied principal component analysis (PCA) to the terahertz absorption spectra; the principal components extracted from the original data by PCA could replace the original absorption coefficient data and clearly distinguish between wood species (Figure 4b). Hiromichi Hoshina et al. analyzed ethylene-vinyl alcohol copolymer (EVOH) films under different humidity conditions using terahertz absorption spectroscopy [67]. They observed an intermolecular stretching mode of bound water (similar to that of liquid water) at approximately 6 THz; the intensity of the relaxation vibration mode of water molecules was significantly weaker than that of liquid water, while an extremely weak wagging mode indicated that the water was in a frozen state.

With the help of generalized two-dimensional correlation spectroscopy and perturbation-correlation moving-window two-dimensional correlation spectroscopy, the bound water was classified into three types: frozen water with an amorphous structure (Type I), liquid-like water with wagging motion (Type II), and neighboring water with a weak hydrogen bond network (Type III). Among them, the strong inhomogeneous intermolecular stretching band of Type I water indicated that the frozen bound water was amorphous; the frequency shift in this band in Type II water showed that the hydrogen bond network around vinyl groups was weak. Min Yu et al. obtained the absorption coefficients of poplar wood with different moisture contents using terahertz time-domain spectroscopy and constructed prediction models by combining classical machine learning, regularization, and gradient boosting decision tree algorithms [68]. Through feature selection (competitive adaptive reweighted sampling, CARS) and hyperparameter optimization (grid search, cross-validation), the optimal model was interpreted using the SHAP (SHapley Additive exPlanations) method. The results showed that: wood moisture content was positively correlated with the terahertz absorption coefficient; the gradient boosting decision tree algorithm had higher accuracy, with the XGBoost model being the best (R² > 0.96 for the test set) (Figure 4c); feature selection and hyperparameter optimization significantly improved performance; and SHAP analysis revealed that the frequency of 0.286 THz was crucial. This study demonstrated that terahertz time-domain spectroscopy combined with machine learning can rapidly and accurately detect wood moisture content.

Terahertz spectroscopy is sensitive to the collective vibrational modes and hydrogen bond network of water molecules, providing a unique window into the low-frequency dynamics of water in wood. However, wood itself exhibits strong absorption and scattering of terahertz waves, especially in high-moisture-content samples, posing difficulties for signal acquisition and quantitative analysis. The optical constants (refractive index, absorption coefficient) obtained by THz-TDS are macroscopic effective values, making it challenging to directly invert the local state differences in water in nano-scale micro-regions (e.g., cell wall vs. lumen). Moreover, current research primarily focuses on establishing empirical models between moisture content and absorption coefficients, while the physical correlation between spectral line shapes and specific water configurations (e.g., confined water, interfacial water) remains insufficiently understood. Deeper insights require integration with molecular simulations and more refined spectral decoupling methods.

Figure 4. (a) The water terahertz absorption spectrum, where the green region is four broad terahertz frequency windows that hold promise for non-thermal regulation of biomolecules, showing characteristic peaks for water analysis [65]. (b) The absorption coefficient spectrum of four wood samples, demonstrating species differentiation by THz spectroscopy [66]. (c) Distribution of the prediction data against the measured data in RF model, ENR model, and XGBoost model, validating moisture content prediction accuracy [68].

Figure 4. (a) The water terahertz absorption spectrum, where the green region is four broad terahertz frequency windows that hold promise for non-thermal regulation of biomolecules, showing characteristic peaks for water analysis [65]. (b) The absorption coefficient spectrum of four wood samples, demonstrating species differentiation by THz spectroscopy [66]. (c) Distribution of the prediction data against the measured data in RF model, ENR model, and XGBoost model, validating moisture content prediction accuracy [68].

As shown in

Table 3. Summary of spectroscopic methods.

Technique	Spatial/Temporal Resolution	Key Information Provided	Main Advantages	Main Limitations
FTIR	Micrometer-level (micro-FTIR); Fast scanning	Hydrogen bonding, water state distribution, molecular vibrations	Non-contact/non-destructive, provides molecular “fingerprint”, widely accessible	Signal overlap in broad bands, surface-sensitive, limited spatial resolution in conventional mode
2DCOS	Dependent on base spectroscopy; Resolves sequences of changes	Sequence of dynamic changes, deconvolution of overlapping peaks	Reveals sequence and correlation of molecular responses to external perturbation	Requires a series of spectra under perturbation, data interpretation can be complex
NIR	Millimeter to centimeter level; Rapid measurement	Moisture content, chemical composition, water structure	Fast, non-contact, suitable for online monitoring, can analyze multiple components simultaneously	Indirect measurement, relies on calibration models, weak signals and overlapping bands require chemometrics
THz	Sub-millimeter level; Fast scanning	Hydrogen bond network vibrations, collective water modes, material dielectric properties	Sensitive to hydrogen bonds and weak interactions, strong penetrability, low photon energy	Strong absorption by bulk water, complex data interpretation for heterogeneous materials

, spectroscopic methods provide multi-level information from molecular vibrations to network dynamics for studying the interaction between wood and water. Fourier transform infrared spectroscopy, as a fundamental method, provides direct “fingerprint” information to distinguish between hydrogen bonds of different strengths and moisture states. Based on this, two-dimensional correlation spectroscopy can reveal the sequence of hydrogen bond breaking and formation by analyzing the spectral changes under external disturbances, effectively resolving the overlapping signals in the broad absorption peaks. Near-infrared spectroscopy utilizes the combined and harmonic absorption of water to achieve rapid, non-contact quantitative detection of moisture content, which is very suitable for online monitoring. However, it relies on calibration models, and the signal is indirect. Terahertz spectroscopy takes a unique approach by detecting the collective vibration modes and hydrogen bond network dynamics of water molecules, providing low-frequency information that is difficult to obtain from other spectra. These technological advantages complement each other: for example, NIR can be combined with hyperspectral imaging to visualize moisture distribution, or 2DCOS can be used to deeply analyze the evolution mechanism of hydrogen bonds under temperature changes. The main challenge lies in the analysis of complex data, which requires the use of methods such as chemometrics and machine learning to convert spectral signals into reliable physical and chemical information.

3.2. Nuclear Magnetic Resonance Imaging and Its Imaging Methods

3.2.1. Low-Field Nuclear Magnetic Resonance

The low-field nuclear magnetic resonance (LF NMR) method, also known as the time-domain nuclear magnetic resonance (TD-NMR) method, is an effective, high-resolution, and non-invasive research technique. Owing to its high sensitivity to proton (1H) signals, it exhibits multi-dimensional applications in studying the moisture state of wood (Figure 5a) [69]. Nuclear magnetic resonance signals enable the acquisition of spin–lattice relaxation time (T1) and spin–spin relaxation time (T₂), respectively. By leveraging differences in relaxation times (particularly the transverse relaxation time T₂), low-field NMR methods can distinguish the existing states of moisture in wood, laying a foundation for understanding the interaction between moisture and wood. Sun Fengze et al. used low-field nuclear magnetic resonance (LF NMR) to investigate the stable and unstable states of moisture in wood, rattan, and bamboo during the moisture absorption process [70]. They analyzed the T₂ relaxation behavior during adsorption and found that at the same moisture content, the T₂ relaxation peak time of stable moisture was shorter than that of unstable moisture (peak shift to the left in the spectrum), with a more significant leftward shift observed at high moisture content. Additionally, the T₂ peak time was related to the moisture absorption history: the T₂ peak time of wood after stable adsorption was shorter than that of wood after stable desorption. Through normalized correlation analysis, they also discovered that the area and height of the T₂ peak were significantly correlated with the proportion of wood cell walls, and the degree of leftward shift was mainly affected by the effective adsorption area. This provides key evidence for revealing the connection between moisture states and wood structure.

The low-field NMR method is widely applied in wood moisture research: it can not only distinguish moisture states based on differences in relaxation times but also track moisture migration and drying processes. Kang Xu et al. used low-field NMR methods to study poplar wood, employing the CPMG pulse sequence to distinguish between free water and bound water (Figure 5c) [71]. Their findings showed that at high moisture content, free water decreased first; at low moisture content (below the fiber saturation point), the drying process was dominated by bound water diffusion. The newly developed SE-SPI pulse sequence they proposed enables non-destructive layered determination of the T₂ distribution in wood. Combined with the area integration method to calculate the moisture content of each layer, it clearly reflects the moisture content gradient during the drying process, providing an effective means for accurate monitoring of the drying process (Figure 5b). The migration path of moisture in wood is of great significance for studying wood hydration. Luoyi Yan et al. investigated moisture penetration in the microstructures of oak and poplar wood using dynamic NMR relaxation [72]. The results indicated that moisture first enters the cell wall in the form of bound water, then slowly invades the fibers, and finally penetrates the vessels, with vessel penetration being the slowest. In poplar wood, vessels even begin to be invaded only after the fibers are completely filled. The traditional capillary model cannot fully explain this process: bound water changes the wettability of the cell wall, and its diffusion coefficient exhibits anisotropy (longitudinal diffusion is the fastest, while radial and tangential diffusion are slower).

Moisture in wood exhibits pore size dependence: the more confined the pore space, the faster the movement of water molecules. Thus, moisture can be distinguished through the distribution of NMR relaxation times. Wood is mainly composed of cellulose, hemicellulose, and lignin, and contains abundant sites capable of adsorbing water. These sites interact with water molecules through the formation or breaking of hydrogen bonds. Therefore, in addition to wood channels, wood components also influence hydration. Tiantian Yang et al. focused on Populus cathayana Rehd. as the research object, removing approximately 8%, 20%, and 70% of hemicellulose through hydrothermal treatment [22]. Combining low-field nuclear magnetic resonance (LF NMR) with scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and nitrogen adsorption analysis, they explored the effect of hemicellulose removal on the interaction between wood and water. The results showed that hemicellulose removal enriches the porous structure of wood (especially mesopores) and reduces adsorption sites, leading to faster initial water absorption in wood and a higher proportion of free water (the free water/bound water ratio in the 3H group exceeded 4.5, which was more than 15% higher than that in the untreated group). It also promotes moisture loss during drying (with more significant free water loss in the early stage) and shortens the T2 of both bound water and free water. These findings indicate that hemicellulose removal affects moisture distribution and migration by altering the physical and chemical environment of wood.

LF-NMR is unparalleled for non-destructively differentiating water states based on mobility. However, the common practice of deconvoluting the T₂ distribution into discrete peaks corresponding to “bound” and “free” water is a simplification. The transverse relaxation time is influenced by a confluence of factors: pore size, surface chemistry (e.g., hydrophilicity), and the presence of paramagnetic ions in the wood matrix. Consequently, similar T₂ values may represent water in different physical environments (e.g., water in small lumens vs. water in large cell wall pores). A significant and often overlooked source of variability is the moisture sorption history. The hysteresis observed in T₂ times between adsorption and desorption cycles points to irreversible changes in the polymer matrix, meaning the measured “state” of water is path-dependent. Therefore, reporting and controlling for moisture history is crucial for meaningful comparisons.

3.2.2. Nuclear Magnetic Resonance Imaging Methods

Low-field nuclear magnetic resonance (LF NMR) methods can fully cover the entire material property curve from the absolutely dry state to the fully water-saturated state, and they can also accurately identify the distribution of water molecules in the hierarchical structure of wood. In addition, by encoding positional information into nuclear magnetic resonance signals, magnetic resonance imaging (MRI) can present spatial distribution characteristics, providing an efficient means to evaluate moisture migration at the cellular scale during wood drying and water absorption processes. Magnetic resonance imaging is capable of delivering molecular information about free water, adsorbed water, and cell wall polymers at the sub-millimeter scale (i.e., along concentration gradients).

Muhammad Asadullah Javed et al. studied the water absorption process of heat-treated modified Scots pine (Pinus sylvestris) samples using magnetic resonance imaging (MRI) and gravimetric analysis [73]. MRI spin density images can present the spatial distribution characteristics of free water with high resolution, while T₂-weighted images can show differences in T₂ values across different structural parts of wood. Individual resin canals can be distinguished in high-resolution images, particularly in the latewood regions of modified samples, with a density estimated at (2.7 ± 0.6) mm⁻². Traditional methods can only provide point measurements or destructive testing, whereas MRI enables continuous scanning of wood to obtain moisture distribution in three-dimensional space (e.g., moisture accumulation in areas such as interlayer gaps and growth rings) without disturbing the natural moisture state of the sample. J. Leko et al. used a medical-grade 3T MRI scanner to explore the applicability of MRI in large-scale wood moisture durability studies, with cross-laminated timber (CLT) as the research object [74]. The results showed that the PETRA sequence could clearly identify features of CLT such as joints and growth rings when the moisture content was ≥19%, with a resolution close to 1 mm; however, a decrease in moisture content led to a reduction in image resolution (Figure 5d). Ultimately, this study confirmed that MRI can non-invasively monitor the moisture distribution and dynamic changes in CLT, providing a visual basis for moisture management in large-scale wood buildings.

MRI enables non-destructive visualization of water spatial distribution, but its resolution and signal-to-noise ratio are constrained by magnetic field strength and water molecule concentration. At low moisture content (e.g., below the fiber saturation point), the signal weakens drastically, making clear imaging difficult and missing the critical process of cell wall water removal. Conventional MRI sequences are sensitive to free water, but specific imaging of bound water within cell walls remains challenging. Furthermore, MRI produces proton density-weighted images where signal intensity conflates water content and relaxation properties. More accurate quantification of the spatial distribution of different water states requires combining T₁ and T₂ mapping. For anisotropic wood structures, isotropic imaging voxels may obscure details of moisture migration along specific directions (e.g., longitudinal).

As shown in

Table 4. Summary of nuclear magnetic resonance methods.

Technique	Spatial/Temporal Resolution	Key Information Provided	Main Advantages	Main Limitations
LF-NMR	No spatial resolution; Millisecond-second temporal resolution	Water state distribution (free/bound water), transport kinetics, pore confinement effects	Non-invasive, highly sensitive to protons (1H), enables real-time process monitoring	Cannot provide direct spatial distribution, relatively high equipment cost
MRI	Sub-millimeter level (~1 mm)	Spatial distribution of water (2D/3D), water differences across structural parts	Non-destructive, visualization of water distribution and migration, suitable for large samples	Resolution better at high moisture content, weak signal at low moisture content, expensive equipment

, nuclear magnetic resonance methods have become one of the most direct and effective means to distinguish between free water and bound water in wood by detecting the relaxation behavior of protons in water molecules. Low-field nuclear magnetic resonance can quantitatively analyze the proportion and migration kinetics of water in different states without loss, but it does not provide spatial information on its own. Magnetic resonance imaging methods fills this gap by encoding spatial signals to achieve non-destructive visualization of the two-dimensional or even three-dimensional distribution of moisture inside wood. These two technologies complement each other and jointly construct a comprehensive moisture analysis system from microscopic state to macroscopic distribution, especially suitable for tracking dynamic processes such as drying and moisture absorption.

Figure 5. (a) The basic NMR working principle [69]. (b) 3D trajectory and corresponding contour plot of T₂ distribution for five layers at each drying time [70]. (c) Probability density function of the transversal relaxation time of water (blue curves) in oak and poplar samples after 2 weeks of immersion in water bath and measured over 20 h, showing saturated state, illustrating moisture distribution and species differences [71]. (d) Moisture loss comparisons for sample A2 MRI image, from left to right, Day 0–Day 1, Day 0–Day 4, Day 0–Day 8, and Day 0–Day 12 [74].

Figure 5. (a) The basic NMR working principle [69]. (b) 3D trajectory and corresponding contour plot of T₂ distribution for five layers at each drying time [70]. (c) Probability density function of the transversal relaxation time of water (blue curves) in oak and poplar samples after 2 weeks of immersion in water bath and measured over 20 h, showing saturated state, illustrating moisture distribution and species differences [71]. (d) Moisture loss comparisons for sample A2 MRI image, from left to right, Day 0–Day 1, Day 0–Day 4, Day 0–Day 8, and Day 0–Day 12 [74].

3.3. Neutron Scattering and X-Ray Diffraction Methods

3.3.1. Neutron Scattering

Neutron scattering methods (including small-angle neutron scattering, SANS, and quasi-elastic neutron scattering, QENS) exhibit multi-dimensional applications in wood hydration research, thanks to their sensitivity to hydrogen/deuterium isotopes, non-invasiveness, and nanoscale detection capability. Small-angle neutron scattering (SANS) can probe structural features in the 1–100 nm scale, making it suitable for studying the pore structure of wood cell walls and moisture filling states. It enables more accurate interpretation of molecular-scale interactions between water and cellulose microfibril bundles in plant cell walls. Quasi-elastic neutron scattering (QENS) provides quantitative details of dynamic relaxation processes, making it applicable to studying dynamic phenomena such as molecular diffusion and relaxation in materials, and capable of capturing the microscopic movement characteristics of moisture in wood.

The interaction between wood microfibrils and moisture affects the packing and arrangement of microfibrils. In naturally hydrated wood, cellulose microfibrils form bundle-like structures through hydrogen bonds and other interactions; their size and arrangement directly influence the mechanical properties and moisture response of wood. Paavo A. Penttilä et al. combined small-angle neutron scattering with contrast variation using polyethylene glycol (PEG) to determine the scattering contribution of cellulose microfibril bundles in natural wood cell walls [75]. Experiments measured the average diameter of microfibril bundles in native wood as 12–19 nm, and the center-to-center spacing of microfibrils within the bundles could be extracted from the same dataset, revealing the dense packing characteristics of microfibril bundles in the hydrated state. A subsequent study by the same research group further confirmed that the center-to-center spacing of microfibrils in hydrated softwoods (e.g., fir, spruce) is approximately 4.3–4.4 nm, while that in hardwoods (e.g., birch) is smaller (approximately 3 nm); this difference is directly related to the moisture sensitivity of wood (Figure 6a) [76]. Additionally, SANS can be used to real-time capture the nanostructural dynamics of wood during moisture variation, revealing the correlation between moisture loss and structural shrinkage, as well as irreversibility. Aleksi Zitting et al. employed time-resolved small-angle neutron scattering (SANS) to investigate the microscopic changes in cellulose microfibrils and their bundle structures in unmodified spruce cell walls during air drying [77]. Dynamic vapor sorption (DVS) measurements were used to quantitatively verify the scattering analysis results. The findings showed that drying occurs in two stages: the constant-rate stage primarily removes free water from cell lumens, with minimal changes in microfibril bundle structure; the falling-rate stage mainly involves the removal of bound water from cell walls, during which the diameter of microfibril bundles shrinks significantly from 18 to 19 nm to approximately 8 nm, the spacing decreases, and the water diffusion coefficient drops sharply.

Quasi-elastic neutron scattering (QENS) focuses on studying the dynamic properties of bound water, enabling the differentiation of bound water in different states and the quantification of their parameters. For instance, Hugh O’Neill et al. used Quasi-elastic neutron scattering (QENS) to directly measure a diffusion coefficient of (0.85 ± 0.04) × 10⁻¹⁰ m²/s at 250 K for non-frozen water associated with cellulose surfaces [78]. This value is more than an order of magnitude lower than the self-diffusion coefficient of bulk water (~2.3 × 10⁻⁹ m²/s at 298 K), providing direct experimental quantification of the severely restricted water mobility due to hydrogen bonding and confinement. Furthermore, their study observed a “sudden flow” of confined water in microfibril gaps at ~260 K, with the diffusion coefficient jumping to 1.77 × 10⁻¹⁰ m²/s, revealing the discontinuous nature of water dynamics in confined environments.

Combining neutron scattering data with molecular dynamics simulations can deepen the understanding of hydration mechanisms at the atomic scale. Paavo A. Penttilä et al. proposed that by calculating the scattering patterns of microfibril bundle models, phenomena observed via SANS, such as bundle shrinkage and changes in water diffusion coefficient—can be correlated with intermolecular interactions (e.g., cellulose–water hydrogen bonds, hydrophilicity of hemicellulose), explaining the molecular origin of structural changes (Figure 6c) [79]. For example, simulations confirmed that the shrinkage of microfibril bundles during drying is related to increased tension on microfibrils after water loss from the hemicellulose matrix, which is consistent with the spacing changes measured by SANS. This combination of multiple methods provides key theoretical support for the modification, preservation, and functional application of wood materials.

Neutron scattering methods can specifically resolve the location and motion of water molecules through H/D contrast, but their data analysis relies heavily on structural models and fitting assumptions. For instance, extracting microfibril bundle dimensions from Small-angle neutron scattering (SANS) data typically assumes homogeneous solid cylinders, which deviates from the actual structure of fibrils with amorphous surfaces and roughness, potentially leading to biased size estimates. Quasi-elastic neutron scattering (QENS) probes water diffusion, but the derived diffusion coefficient represents an average over specific spatiotemporal scales. The highly heterogeneous nano-environment within the wood cell wall may cause multi-exponential relaxation behavior, limiting the applicability of simple diffusion models. Successful application of these methods depends critically on sample preparation (e.g., deuteration level, homogeneity) and complex theoretical analysis frameworks.

3.3.2. X-Ray Diffraction Technique

X-ray diffraction (XRD) is a technique that characterizes the structural features of crystals (such as interplanar spacing, crystallinity, and crystallite size) by analyzing diffraction patterns generated from the interaction between X-rays and the crystal structure of materials. In wood research, XRD can accurately capture the microscopic effects of factors such as moisture, mechanical action, and aging on cellulose crystals, providing key evidence for understanding wood’s hygroscopic properties and mechanical behavior.

When wood absorbs moisture, the dimensional change in cellulose crystals is the most direct microscopic response, and this process can be clearly characterized by XRD. The research results of Lennart Salmên et al. showed that when paper samples (cellulose-dominated) absorb moisture, cellulose crystals exhibit significant anisotropic deformation: shrinkage occurs in the transverse direction (perpendicular to the fiber axis), manifested as a decrease in the d-spacing of the (200) crystal plane (diffraction peak shifts to a higher angle); slight elongation occurs in the longitudinal direction (parallel to the fiber axis), reflected by an increase in the d-spacing of the (004) crystal plane (diffraction peak shifts to a lower angle). More importantly, this deformation is reversible, when moisture desorbs, the crystal size can return to its initial state. This phenomenon is attributed to the hygroscopic swelling of amorphous cellulose and glucomannan hemicellulose: the longitudinal and transverse expansion of these non-crystalline components collectively generates stress, forcing the crystals to undergo transverse compression and longitudinal stretching, which reveals the mechanical coupling relationship between crystalline and non-crystalline components in wood (Figure 6d) [80].

Beyond crystal deformation, moisture also changes key parameters of cellulose such as crystallinity (CrI) and crystallite size (L200), and these changes are closely related to the state of moisture in wood. Experiments by Umesh P. Agarwal et al. indicated that for materials with Segal CrI < 90% (e.g., wood), moisture absorption increases CrI (with a 32.9% increase in wood) and L₂₀₀ (with a 49.5% increase in wood). This is because moisture promotes the rearrangement of cellulose chains in the amorphous region, converting part of the disordered structure into an ordered crystalline structure; in contrast, changes in high-crystallinity materials (e.g., tunicate cellulose) are negligible (only a 0.2% change in CrI) [81]. Combining XRD and NMR revealed that when the relative humidity is below 78%, the moisture in wood is mainly bound water. At this point, the hygroscopic swelling of hemicellulose compresses cellulose crystals, leading to a decrease in the d-spacing of the (002) crystal plane; when the humidity exceeds 78% (near the fiber saturation point), free water begins to appear, and XRD shows an increase in the proportion of the amorphous region, further confirming the correlation between moisture state and crystalline characteristics [82].

The microfibril angle (MFA), the angle between cellulose microfibrils and the fiber axis, is another core parameter affecting wood properties. XRD can accurately estimate MFA through the distribution of diffraction peaks of the (002) crystal plane, and its change is closely related to moisture content. Naiara Conceição Marques de Souza et al. studied Eucalyptus grandis wood and found that the average MFA of green wood (high moisture content) is 9.0°, while that of dry wood (low moisture content) is 7.5°; drying causes a significant decrease in MFA (approximately 1.5°). This phenomenon is related to the reduced spacing between microfibrils and enhanced hydrogen bonding during drying; less moisture makes the microfibrils arrange more tightly, resulting in angle shrinkage. By establishing a cubic model between MFA and T value (full width at half maximum of the (002) peak) (e.g., MFA = −12.198T3 + 113.67T2 − 348.4T + 358.09), XRD enables efficient calculation of MFA [83]. Different layers of the wood cell wall (e.g., S1 and S3 layers) have heterogeneous structures, and the mechanical properties of their microfibrils vary significantly under the influence of moisture and thermal modification. Research by Erina Kojima et al. showed that the strain response of microfibrils in the S1 layer is more sensitive, while the S3 layer is more stable; this is related to differences in the arrangement density of microfibrils and matrix composition between the two layers. By capturing strain changes in the (004) crystal plane in different layers, XRD reveals the influence of cell wall layer heterogeneity on the overall mechanical properties of wood, deepens the understanding of the complexity of wood structure, and provides a layered perspective for targeted optimization of wood properties [84].

X-ray diffraction accurately measures changes in the cellulose lattice in response to moisture, but the measured “crystallinity” is an operational definition based on diffraction intensity, not an absolute metric. The commonly used Segal method is convenient but overly simplistic, failing to distinguish between crystallite size effects and lattice disorder. It is also highly sensitive to moisture and the amorphous matrix in the sample, making comparisons of Crystallinity Index (CrI) measured at different humidity levels require caution. XRD primarily provides macro-averaged information from crystalline regions, with limited ability to probe the specific behavior of water molecules at cellulose crystal surfaces, interfaces, or junctions with the amorphous matrix (e.g., hemicellulose, lignin). Correlating lattice strain with macroscopic swelling stress also requires consideration of multi-scale coupling effects; XRD data alone is insufficient to build a complete mechanistic picture.

As shown in

Table 5. Summary of neutron and X-ray scattering methods.

Technique	Spatial/Temporal Resolution	Key Information Provided	Main Advantages	Main Limitations
SANS/QENS	Nanoscale structure (1–100 nm); Nanosecond-picosecond dynamics	Nanostructure (microfibril bundle size/spacing), water molecular dynamics (diffusion coefficient)	Sensitive to hydrogen isotopes, non-destructive, probes atomic/molecular scale motion	Requires neutron source, complex sample preparation
XRD	Atomic/Nanoscale (crystal structure)	Cellulose crystal parameters (d-spacing, crystallinity, microfibril angle), crystal deformation	Reveals crystal-scale interaction with moisture, precise measurement	Primarily provides crystalline information, limited data on amorphous regions

, neutron and X-ray scattering methods have pushed the research scale to the nanometer and even atomic levels. Small-angle neutron scattering can analyze the nanoscale arrangement and size changes in microfiber bundles within wood cell walls, while quasi-elastic neutron scattering can directly measure the diffusion motion of bound water on the picosecond to nanosecond time scale. X-ray diffraction focuses on the response of cellulose crystals themselves and can accurately measure lattice expansion/contraction, crystallinity changes, and microfiber angle changes caused by moisture changes. These technologies collectively reveal the microstructural roots of the moisture absorption, expansion, and contraction of wood, and by combining with molecular dynamics simulations, can explain macroscopic phenomena from the perspective of atomic interactions.

3.4. Thermal Analysis Methods

Differential scanning calorimetry (DSC) can quantitatively capture changes in thermophysical properties and thermal transition information of materials during thermal variation. By measuring the heat absorbed or released by a sample during heating or cooling, it reveals thermal transition behaviors such as glass transition, crystallization, and melting. In wood hydration, the existence form of moisture (free water, bound water) and its interaction with wood cell walls affect the thermal behavior of wood.

Moisture in wood can be classified into cell wall water (bound water) and capillary water (free water) based on its location and binding strength. DSC enables quantitative distinction between them by detecting thermal signals (peak position, peak area) of moisture freezing/melting, providing direct evidence for hydration mechanism research. Maria Fredriksson et al. used a novel press-plate-based system to adjust the moisture content of wood samples to multiple high levels, and distinguished cell wall water from capillary water via differential scanning calorimetry [85]. They determined the melting enthalpy of samples through DSC to calculate the content of freezable water (i.e., capillary water), with the remaining moisture being cell wall water. The results showed that no frozen moisture was detected in wood at a relative humidity of 95%, while in wood at a relative humidity of 99.65%, the capillary water content was equivalent to a moisture content of 0.1–0.3% (Figure 7a). Even if all frozen moisture detected at 99.65% relative humidity was assumed to be stored in threshold pores with a diameter of 21 nm (i.e., threshold pores one level smaller than those at 95% relative humidity), the overestimation of cell wall moisture was only equivalent to a moisture content of 0.1–0.3%. This indicates that wood cell walls exhibit adsorption hysteresis throughout the entire moisture content range, meaning that cell walls are fully wetted only when the entire wood sample reaches saturation.

The fiber saturation point (FSP) of wood refers to the moisture content at which only the adsorbed water in cell walls is saturated, with no free water in cell lumens or intercellular spaces [86]. It is a critical threshold for wood physical properties (e.g., swelling, strength) to change with moisture content. DSC quantifies FSP by determining the “maximum content of non-freezable water” and is one of the most commonly used methods currently; its accuracy can also be optimized by combining different calculation logics. Leandro Passarini et al. used 1 mm-thick latewood samples and calculated FSP under different equilibrium moisture content (EMC) conditions using two methods: the melting enthalpy zeroing method and the direct calculation method of non-freezable water content [87]. The results showed that FSP decreased with increasing acetylation degree: the EMC of the control group decreased by approximately 27% (EMCR), while the EMCR of the highest acetylation group was only about 9%. The FSP values of unmodified samples measured at a fast-scanning rate were higher.

Micropores (2–50 nm) in wood cell walls are the main storage sites for bound water. Based on the Gibbs–Thomson principle that “the smaller the pore size, the lower the freezing point”, the DSC thermoporometry method can characterize pore size distribution in a wet state. Xiang Zhong et al. solved the differential equation of moisture melting in wood obtained by differential scanning calorimetry (DSC) to obtain the change in frozen water content with temperature, and then calculated the pore size distribution by combining the Gibbs–Thomson equation [88]. The results showed that the pore volume decreased sharply with increasing pore size: micropores smaller than 10 nm accounted for more than 80% of the cell wall pores of wood, and cell wall pores with a diameter exceeding 20 nm were almost non-existent (Figure 7b). Compared with the traditional discontinuous method, this method does not require the assumption that “no moisture melts at low temperatures”, resulting in higher accuracy (9.4–52.4 times that of the traditional method), and the test time is shortened from 45 min to 23 min.

Figure 7. (a) Sorption isotherms for capillary water (upper) and cell wall water (lower) for untreated wood, pyridine controls and interface modifications [85]. (b) The water content of the samples with increasing temperature. The solid line and the dashed line indicate the frozen water content and the total water content, respectively [87]. (c) DSC curves of original, acetylated, and heat-treated solid poplar samples at different moisture states, showing thermal analysis of water phase transitions in wood [88].

Figure 7. (a) Sorption isotherms for capillary water (upper) and cell wall water (lower) for untreated wood, pyridine controls and interface modifications [85]. (b) The water content of the samples with increasing temperature. The solid line and the dashed line indicate the frozen water content and the total water content, respectively [87]. (c) DSC curves of original, acetylated, and heat-treated solid poplar samples at different moisture states, showing thermal analysis of water phase transitions in wood [88].

In addition, DSC can be combined with other advanced characterization methods to achieve comprehensive research on wood hydration. Shuyang Cao et al. focused on poplar wood from plantations, investigating the effects of heat treatment and acetylation modification on the moisture phase transition behavior of wood [89]. By combining differential scanning calorimetry (DSC) with moisture adsorption isotherm tests, they identified four types of characteristic peaks for wood moisture phase transition: Peak I: appears at −20~−10 °C, a sharp and intense peak corresponding to the freezing of free water in large pores such as cell lumens; Peak II: located at ~−40 °C, a broad and weak peak corresponding to the freezing of freezable bound water in cell wall micropores, which is easily masked by Peak I; Peak III: starts at a temperature below 0 °C, corresponding to the melting of bound water in cell walls; Peak IV: close to 0 °C, a symmetric and intense peak corresponding to the melting of free water (Figure 7c).

Differential scanning calorimetry distinguishes freezable from non-freezable water via phase transition thermal effects, providing a thermodynamic basis for defining the fiber saturation point. However, interpreting DSC thermograms involves methodological sensitivities: heating/cooling rates affect ice nucleation and growth, altering the shape, position, and area of melting endotherms, leading to variability in the determined “non-freezable” water content. Applying DSC thermoporometry to wood involves calculation models based on the Gibbs–Thomson equation, which typically assume independent cylindrical pores. This conflicts with the interconnected, irregular nanopore network in the wood cell wall. Therefore, the derived pore size distribution should be considered an “equivalent distribution” characterizing water confinement capacity rather than true geometry. Furthermore, DSC cannot distinguish water bound to different chemical components (cellulose, hemicellulose), as its signal is the sum of macroscopic thermal effects from all interactions. It is crucial to recognize that the Fiber Saturation Point (FSP) determined by DSC or any single technique is a method-dependent operational threshold. The DSC-derived FSP, based on the disappearance of freezable water, can be influenced by scan rate and data analysis. This value may differ from those obtained via NMR (based on motional changes) or dimensional methods (based on macroscopic swelling). Therefore, in comparative studies, methodological consistency is paramount, and reporting the FSP alongside the specific technique and key parameters is essential for reproducibility and meaningful interpretation.

3.5. Microscopic Imaging Methods

3.5.1. Atomic Force Microscopy

Atomic Force Microscopy (AFM) is a type of scanning probe microscopy method with nanoscale spatial resolution. Its core principle involves detecting weak interactions (such as van der Waals forces, hydrogen bonds, and adhesive forces) between a tiny probe and the sample surface, enabling multi-dimensional characterization of the sample’s topography, mechanical properties (e.g., stiffness, elastic modulus), chemical component distribution, and interfacial interactions. AFM allows in situ observation in a humid environment close to natural conditions, making it particularly suitable for biomaterials like wood that contain moisture-sensitive components (cellulose, hemicellulose, lignin). It can accurately capture the dynamic interaction between moisture and wood’s microstructure, providing key technical support for analyzing the correlation between wood moisture adsorption, transport, and molecular rearrangement.

Wood is mainly composed of cellulose, hemicellulose, and lignin. Among these, cellulose has elongated crystalline units, and the structure of wood cellulose microfibrils is closely related to moisture transport, serving as the basis for hydration in wood. Kazuho Daicho used Atomic Force Microscopy (AFM), Wide-Angle X-ray Diffraction (WAXD), Small-Angle X-ray Scattering (SAXS), solid-state 13C NMR spectroscopy, and all-atom molecular dynamics (MD) simulations to characterize the topography of cellulose protofibrils from plants such as wood, cotton, and ramie [90]. The results showed that although cellulose from different plant sources was traditionally thought to have structural differences, AFM height images and statistical analysis revealed that the cross-sectional size of dispersed cellulose microfibrils was uniform. They mainly existed as single microfibril units of 2–3 nm, with a crystallinity of approximately 20%, which was highly consistent with the 18-chain cellulose molecular model (Figure 8a). This finding revises traditional understanding, confirming that the uniform size of cellulose protofibrils in wood is the structural basis for forming uniform transport channels for moisture within cell walls. The spacing between protofibrils directly determines the consistency of moisture diffusion paths, providing a microstructural explanation for understanding the uniformity of wood’s overall moisture permeability.

As a renewable structural biopolymer, cellulose is widely present in nature and serves as a fundamental reinforcing component in the natural hierarchical structures of organisms such as plants, bacteria, and tunicates. In the study of the molecular interaction mechanism at the crystalline cellulose–water interface, Ayhan Yurtsever et al. focused on wood-derived cellulose nanocrystals (CNCs), using 3D-AFM force mapping methods (frequency shift Δf measurement) combined with molecular dynamics (MD) simulations to in situ characterize the 3D distribution of water molecules at the CNCs–water interface [91]. The results showed that water molecules form an ordered hydration layer with a thickness of approximately 0.8–1.2 nm on the CNC surface, and the structure is regulated by crystal faces: the (010) face exhibits a zigzag or honeycomb arrangement (with a period of approximately 1.05 nm, consistent with the fiber repeat unit), while the (110) face has a moisture-depleted region due to the presence of hydrophobic CH groups (Figure 8b). Additionally, moisture structuring is anisotropic: the water layer is flat along the cellulose chain axis direction and undulating perpendicular to the chain axis direction. Water molecules are strongly bound to the O11 hydroxyl sites on the cellulose surface via hydrogen bonds. This study is the first to visualize the interfacial interaction between wood cellulose and moisture at the molecular scale, clarifying the micro-mechanism by which cellulose—acting as the wood skeleton—regulates the selectivity of moisture adsorption.

Atomic Force Microscopy enables nanoscale and even molecular-level imaging under near-physiological humidity but introduces new complexities for interpreting the wood–water interface. In humid environments, the hydration layer and capillary forces between the tip and sample significantly affect the veracity of topography imaging and the accuracy of force spectroscopy measurements. The measured “adhesion force” is a composite of chemical bonding, capillary, and van der Waals forces. Quantitative dissection of the hydrogen-bonding contribution requires tip functionalization with specific groups and experiments under strict humidity control. When scanning soft, heterogeneous biomaterials like wood, even tapping mode can cause surface deformation or displacement; the observed “cellulose microfibril” morphology may already be perturbed by the imaging process. Therefore, interpreting AFM data necessitates careful analysis of scanning parameters, environmental conditions, and tip state.

3.5.2. Scanning Electron Microscopy

Scanning electron microscopy (SEM) is a powerful tool for analyzing the microstructure of wood with nanoscale resolution. However, conventional SEM operation requires a high-vacuum environment, which causes the complete sublimation of water from the wood sample, preventing the preservation of its true moisture state. Consequently, traditional SEM is primarily used to observe the dried structure of wood (e.g., pore morphology, cell wall construction) and cannot be employed to study the in situ distribution or form of water within wood. To overcome this fundamental limitation and enable in situ observation of water morphology in wood, cryogenic scanning electron microscopy (Cryo-SEM) has been developed and widely adopted. Cryo-SEM integrates low-temperature preparation methods, capable of rapidly freezing hydrated samples into a vitrified state, thereby instantly “fixing” the water in its original location. Subsequent observation within a maintained low-temperature vacuum chamber allows for the analysis of water distribution and the in situ state of chemical substances in wood. Through methods such as cryo-transfer and cryo-fracture, Cryo-SEM can reveal the microstructural features of wood in its natural hydrated state, including water storage and distribution in different cell types (e.g., vessels, fibers, parenchyma cells) and the form of water within the nanopores of the cell wall.

Conventional SEM excels at capturing the correlation between wood microstructures and moisture under environmental stress. However, when directly testing wood samples with poor conductivity, SEM yields suboptimal results, such samples often require drying and gold-sputtering treatment, which limits the application of SEM to some extent in studying the impact of wood moisture states on wood. In contrast, cryogenic scanning electron microscopy (cryo-SEM) integrates low-temperature methods, enabling the analysis of moisture distribution and chemical substances in wood while preserving the original state of moisture. Katsushi Kuroda et al. developed a cryo-TOF-SIMS/SEM combined system to study the chemical distribution and moisture state in the xylem of freeze-fixed Cryptomeria japonica [92]. Wood samples were transferred in a low-temperature environment of −130 °C to −180 °C, which effectively prevented ice sublimation and ensured that moisture was retained in the original state within wood tissues. SEM observation and TOF-SIMS analysis were conducted simultaneously: not only were the distributions of water (in the form of H₃O⁺ ions, m/z = 19), potassium (m/z = 39), and sequirinol (the main chemical substance of Cryptomeria japonica, m/z = 286) detected in frozen samples (e.g., water concentrated primarily in cell lumens, potassium enriched in cell walls and axial parenchyma cells, and sequirinol distributed in tracheid walls), but 3D chemical-moisture distribution imaging with a maximum height of 1000 μm was also achieved through the cyclic operation of “sectioning-analysis” (Figure 8c). Additionally, the study verified the influence of freeze-etching degree on moisture detection, providing a powerful tool for revealing the in situ distribution of moisture and chemical substances in the xylem of living trees.

The storage and utilization of moisture in wood are of great significance for studying wood hydration. SEM (especially cryo-SEM) can analyze the structural basis of wood moisture storage and utilization by capturing the moisture state of specific cell types, providing a theoretical basis for understanding the interaction between wood and moisture. Yazaki et al. studied Abies (conifer), Cercidiphyllum japonicum (diffuse-porous wood), and Quercus (ring-porous wood), using cryo-SEM to observe the moisture state of different xylem cell types during branch dehydration, and analyzing moisture storage mechanisms in combination with cumulative water release (CWR) (Figure 8d) [93]. SEM images showed that the distribution and consumption of moisture in different tree species exhibited significant structural dependence: Abies retained moisture in earlywood tracheids even during dehydration (with CWR reaching 250 kg/m³) to maintain transpiration flow. Cercidiphyllum japonicum could still store water through vessels at high CWR (>200 kg/m³), and imperforate tracheary elements (ITEs) continued to retain water during deep dehydration; Quercus, however, experienced complete water loss in vessels when CWR reached 100 kg/m³, and instead relied on ITEs (especially vascular tracheids around vessels) for moisture storage. Furthermore, cryo-SEM observed that ray parenchyma cells retained moisture even under deep dehydration, but their contribution to overall moisture storage was minimal. This study intuitively demonstrated the inherent correlation between wood structures (e.g., vessels, ITEs) and moisture storage capacity through SEM, providing direct microscale evidence for explaining differences in drought adaptation among different tree species.

Scanning Electron Microscopy, especially Cryo-SEM, can visualize water distribution at the micron scale, but the sample preparation process itself constitutes a major intervention into the native state. Conventional SEM requires conductive and dry samples, necessitating gold sputtering, which completely destroys the natural moisture state of wood, allowing only observation of the dried pore structure. Cryo-SEM attempts to fix water via rapid freezing, but insufficient freezing rates can lead to ice crystal formation and growth, rupturing cell walls and creating artifacts. The degree of freeze-etching also directly affects whether observed features represent lumen ice or bound water within cell walls. Furthermore, SEM images provide 2D projection information; reconstructing the 3D distribution of water requires expensive tomography methods. Therefore, the “water distribution” presented by SEM is essentially indirect morphological evidence of a state frozen at a specific moment after a complex preparation process.

As shown in

Table 6. Summary of microscopic imaging methods.

Technique	Spatial/Temporal Resolution	Key Information Provided	Main Advantages	Main Limitations
AFM	Nanoscale (molecular-level) resolution	Real-space imaging (nanostructure), surface topography, interfacial water molecule layer structure	Extremely high resolution, allows in situ observation near natural humidity, measures mechanical properties	Small scan area, sample surface needs relative flatness, relatively slow imaging
SEM	Nanometer-micrometer level	Micro-morphology (cells, pores), water distribution and storage sites (after freezing)	High-resolution intuitive morphology, cryo-technique preserves original water state	Conventional SEM requires dry samples; Cryo-SEM has complex preparation, requires prevention of ice artifacts

, microscopic imaging methods provide direct evidence for observing the microstructure and water distribution of wood in real space. Atomic Force Microscopy, with its nanoscale and even molecular level resolution, can not only depict the morphology of cellulose microfibers but also study the hydration layer structure of material surfaces in a near natural state, revealing the interface interaction between water and cell wall components. Scanning electron microscopy, especially cryo-electron microscopy methods, can clearly display the actual distribution and storage status of water in cell cavities, pores, and other structures at the micrometer scale, avoiding water loss caused by conventional sample preparation. These high-resolution imaging methods intuitively validate the models and conclusions derived from spectroscopic, scattering, and other methods, achieving mutual verification from indirect characterization to direct observation.

3.6. Comparative Synthesis and Data Integration Across Methods

As detailed in the preceding sections, each advanced characterization technique provides a unique window into the wood–water system, yielding specific quantitative parameters. Placing these parameters side-by-side reveals the consistency and complementary nature of the multidisciplinary approach.

Table 7. Key parameter ranges for different water states: a cross-technique data summary.

Water State	T2 Relaxation Time (LF-NMR)	Diffusion Coef. D (QENS)	Phase Behavior (DSC)	H-Bond Vibrational Feature (FTIR/THz)	Primary Probing Methods
Bound Water	1–10 ms	~10−11–10−10 m2/s	Non-freezable or broad melting (−40–0 °C)	O-H stretch significantly red-shifted (~3200–3400 cm−1)	LF-NMR, DSC, FTIR
Interfacial/Confined Water	10–50 ms	~10−10–10−9 m2/s	Signif. freezing point depression (ΔT = 5–20 K)	Altered collective modes/Blue shift (THz)	QENS, SANS, AFM, THz
Free Water	>50 ms (up to hundreds of ms)	~2.3 × 10−9 m2/s (bulk value)	Sharp melting peak near 0 °C	Similar to bulk water spectrum	MRI, Gravimetry, DSC

Note: Values are typical ranges based on literature [22,63,70,71,78,88], etc., and can vary with species, moisture content, and measurement conditions. This table aims to illustrate the parameter trends and consistency revealed by different methods.

synthesizes representative ranges for key parameters of different water states as measured by the core methods discussed in this chapter. The comparative data in Table 3 underscores several critical points: (1) Different methods converge on similar orders of magnitude for parameters defining a given water state (e.g., bound water is consistently characterized by short T₂, low D, and a depressed melting point), validating the operational definitions used throughout this review. (2) The parameters clearly differentiate between bound, interfacial, and free water, providing a quantitative foundation for the conceptual models presented in Chapter 1. (3) Discrepancies in absolute values (e.g., D from QENS vs. macroscopic diffusion models) often reflect the different spatial and temporal scales probed, highlighting the importance of technique selection based on the specific dynamic process of interest. This integrated, quantitative perspective is essential for moving from phenomenological observation to predictive modeling of wood–water interactions.

4. Conclusions and Prospects

4.1. Conclusions

This study provides a systematic review of the critical role advanced characterization methods play in revealing the complex behaviors at hydration interfaces within wood micro/nano channels. The results demonstrate that the multiscale hierarchical pore structure of wood fundamentally regulates the state, dynamics, and phase transition behavior of water through the synergistic effects of geometric confinement and interfacial chemistry. Spectroscopic methods (FTIR, 2DCOS, NIR, THz) offer molecular-level “fingerprint” information on bonding and network vibrations; nuclear magnetic resonance (LF-NMR, MRI) enables non-destructive, quantitative analysis of water states and spatial distribution; neutron and X-ray scattering methods (SANS, QENS, XRD) extend the observation scale to the nano- and atomic levels, revealing the structural responses of cellulose microfibril bundles and crystal lattice deformations; thermal analysis (DSC) distinguishes between different water states thermodynamically and characterizes pore structures in the wet state; and microscopic imaging methods (AFM, Cryo-SEM) ultimately achieve direct visualization of nano-scale hydration layers to micro-scale water storage sites in real space. The integrated application of these multimodal methods has constructed a comprehensive cognitive framework linking molecular mechanisms to macroscopic properties. This not only deepens the understanding of wood’s traditional water-related behaviors but also lays a solid scientific foundation for designing and developing advanced wood-based functional materials with tailored moisture-response characteristics.

4.2. Prospects

To advance this field, future research should focus on a tiered set of priorities that bridge fundamental understanding, technological capability, and sustainable application. We propose the following integrated roadmap to address unresolved questions and guide impactful innovation.

A deeper understanding of the fundamental nature of the hydration interface calls for more profound cross-scale mechanistic studies. This requires establishing a tighter synergistic validation loop between computational simulations and experimental characterization. High-precision molecular dynamics simulations can be utilized to reveal dynamic processes at the atomic/molecular scale, which should then be rigorously validated and calibrated using advanced experimental data from methods like neutron scattering and ultrafast spectroscopy. Concurrently, computational simulations should proactively predict water behavior in novel phenomena and material systems, thereby guiding experimental exploration. This deep integration serves as a critical bridge connecting nano-scale interface science with macroscopic materials engineering and forms the foundation for all predictive models.

Translating fundamental insights into reliable knowledge depends on developing more integrated characterization methodologies and analytical frameworks. A key direction is the development of multimodal in situ characterization platforms capable of simultaneously acquiring multiple types of physicochemical information. Examples include combining high-spatial-resolution imaging with spectroscopic analysis under dynamic humidity and mechanical loading. Confronting the complex, multidimensional datasets generated by such approaches necessitates the integration of artificial intelligence-driven data fusion and mining methods. This will be crucial for establishing quantitative structure–property–performance relationship models across scales, enabling precise prediction and design from microscopic mechanisms to macroscopic performance.

Specifically, the integration of machine learning (ML) with advanced characterization data holds great promise for uncovering hidden patterns in complex wood–water systems. To ensure scientific rigor and reproducibility, future work should adhere to several best practices: (1) Foundation on High-Quality Data: ML models are only as good as their training data. Datasets must be representative, span relevant conditions (species, treatments, moisture levels), and be grounded in accurate measurements. (2) Prioritize Interpretability: Moving beyond “black-box” models is essential. Employing feature selection based on domain knowledge (e.g., known spectroscopic bands) and tools like SHAP (SHapley Additive exPlanations) can link model predictions to physical mechanisms. (3) Rigorous Validation: Models must be validated on completely independent datasets not used during training or tuning to reliably assess generalizability and prevent overfitting. (4) Pursue Physics-Informed ML: The most powerful approach will integrate known physical laws (e.g., diffusion equations, sorption theories) directly into the ML architecture, creating models that are not only data-efficient but also physically plausible and predictive beyond the immediate training domain.

Ultimately, the vitality and value of research in this field will be demonstrated by its capacity to address real-world sustainability challenges. Future efforts should actively pivot towards application-oriented interfacial engineering research. The fundamental insights into confined water behavior are directly enabling the rational design of wood-based functional materials across diverse fields. For instance, understanding water and ion transport in nanochannels has informed the design of wood-based membranes for efficient water purification [94] and ion-selective separators for energy storage devices [95]. Exploiting moisture-induced swelling has led to hygromorphic actuators for soft robotics and passive building systems [96]. Furthermore, principles derived from wood–water interactions contribute to creating dimensionally stable engineered wood products for construction [97] and responsive humidity sensors [98].

Critically, several of the reviewed methods themselves are transitioning from laboratory tools to enablers of applied innovation. Near-Infrared (NIR) and Terahertz (THz) spectroscopy, for example, are being adapted for non-destructive, online moisture monitoring in industrial wood processing [99]. Similarly, Low-Field NMR is increasingly used to screen and predict the long-term hygroscopic performance of new wood composites. Driving the translation of fundamental scientific understanding into high-value, sustainable solutions constitute the fundamental impetus for the long-term advancement of this field.