MnO₂ Nanostructure-Based Novel Sensing: A Review

Abstract

Growing public concern over the living environment, food safety and the healthcare industry has spurred rapid advances in advanced sensing technology for environmental monitoring, food-safety screening, and biomedical surveillance. Consequently, developing a novel sensing strategy which is efficient, inexpensive, and easy to operate has become a major research focus in recent years. MnO₂ nanostructures combine advantages of high specific surface area, quantum confinement, surface effects originating from their nanostructure, pronounced redox activity, broad optical absorption, distinctive electrochemical behavior, multiple accessible oxidation states, low cost and environmental benignity contributed by MnO₂, which make them a critical material candidate for developing advanced sensing technology. This paper provides a comprehensive overview of MnO₂ nanostructure-based novel sensors over the past five years. The contents of this review are listed as follows: (1) synthetic strategy and sensing advantages of MnO₂ quantum dots, 1D MnO₂, 2D MnO₂ and hierarchical MnO₂; (2) recent research advances in detection methodology and corresponding principles based on MnO₂ nanostructures; and (3) the applicational progress of MnO₂ nanostructure-based novel sensing technology in the field of food safety and biosensing. Finally, the foregoing discussion is integrated, and the current shortcomings and future development trends of novel sensors based on MnO₂ nanostructures are critically assessed.

1. Introduction

Rapid modernization development has brought prosperity while also causing emerging social challenges, including environmental contamination, food-safety risk and the outbreak of infectious diseases. Due to the threat to air quality, water resources, food security and biosafety produced by airborne pollutants, including NH₃, NO₂, HCHO, CH₃CHO and C₆H₁₅N [1]; heavy-metal ions, including Cr, As, Se, Sr, Hg, Pb, as well as N₂H₄·H₂O; organophosphorus pesticides; antibiotic residue; and pathogens [2,3,4], it is urgently necessary to establish efficient and rapid analytical tools [5] for real-time and ultrasensitive monitoring of the above-mentioned contaminants [6].

Traditional strategies for pollutant detection include chromatography, atomic absorption spectrometry, atomic fluorescence spectrometry, microbial techniques and the enzyme-linked immunosorbent assay. Although it offers high response rates and resolutions, chromatography involves expensive equipment, complex operations and time-consuming procedures [7]. Spectrometry offers high efficiency and non-destructive detection, but suffers from limited selectivity and significant sensitivity variation [8,9]. The microbial technique is low-cost and environmentally friendly but exhibits slow responsiveness and poor stability [10]. The enzyme-linked immunosorbent assay boasts high specificity and response rates but relies heavily on antibodies and can only detect a single target [11]. Therefore, developing novel sensing technology with high sensitivity, high efficiency, excellent stability, low cost, a simple process and environmental friendliness would facilitate convenient, timely and precise monitoring of the target substance. In recent years, novel sensing technologies, consisting of electrochemistry, electrochemiluminescence (EL), photoelectron chemistry, colorimetry and fluorescence, have advanced rapidly [12,13,14,15,16,17,18]. For instance, the electrochemical sensor converts the components and concentration of the target into electrical signals including current, potential and conductivity via a redox reaction. The EL sensor identifies the target utilizing light signals generated from electrochemical reaction-excited luminescence, featuring high sensitivity and multi-component analyses.

Nanomaterials enhance the sensitivity of the sensor due to its high specific surface area, unique quantum confinement and surface effects, which shorten the diffusion path and improve the electron transport efficiency of the target. The size compatibility of nanomaterials enables the miniaturization and portability of sensors. Additionally, the high reactivity of nanomaterials enables a reduction in sensing costs. Most importantly, the outstanding photo-electro-thermal property of nanomaterials confers multimodal detection capabilities to the sensor to address numerous challenges in target analysis [19,20]. Manganese dioxide (MnO₂) nanostructures have garnered significant attention due to their outstanding redox capability, broad optical absorption, unique electrochemical property, high charge transfer rate, diverse oxidation states, structure flexibility, large specific surface area, high physicochemical stability, environmental friendliness, as well as low cost [21,22]. Generally speaking, the MnO₂ nanostructure is mainly applied in sensing in two ways. On the one hand, the MnO₂ nanostructure is directly employed as the primary sensing material due to its unique physicochemical properties. On the other hand, due to its excellent carrier characteristics, MnO₂ can be combined with other sensing materials to construct a novel sensor with multi-scenario adaptability.

Over the past five years, there have been numerous reports about the application of MnO₂ nanostructures in sensing, yet comprehensive reviews in this area remain scarce [23,24,25]. In order to provide a comprehensive understanding of MnO₂ nanostructure-based novel sensing, in this review, the synthesis method and sensing advantage of zero-dimensional (0D) MnO₂ or MnO₂ quantum dots, one-dimensional (1D) MnO₂, two-dimensional (2D) MnO₂ and hierarchical MnO₂ are first summarized. Next, the detection principles and characteristics of MnO₂ nanostructure-based novel sensors, including electrochemistry, EL, photoelectron chemistry, colorimetry, fluorescence, photothermal techniques and quartz microbalance (QCM), are systematically elucidated and compared. Most importantly, the specific application of MnO₂ nanostructure-based sensors in environmental monitoring, food-safety monitoring and biomedical monitoring is comprehensively discussed. Finally, the existing limitations and future development trend of nano-MnO₂-based sensors are briefly outlined.

2. Manganese Dioxide Nanostructure

The sensing performance of MnO₂ is highly dependent on its nanostructure, which in turn is closely related to the morphology and dimensionality of MnO₂. MnO₂ with different dimensions exhibits distinct advantages in sensing. The synthesis method and sensing advantage of 0D, 1D, 2D and three-dimensional (3D) MnO₂ will be introduced and summarized systematically in the following (Scheme 1).

2.1. Zero-Dimensional MnO₂

0D MnO₂ refers to MnO₂ materials with a size of three dimensions usually in the range of 1–100 nm and mainly including MnO₂ nanoparticles and MnO₂ quantum dots. In particular, the size of a MnO₂ quantum dot in three dimensions is usually less than 10 nm, mostly concentrated in the range of 2–10 nm, which leads to strong constraints on carriers in three spatial directions and atomic-level discretization of electronic energy levels, finally contributing to significant quantum confinement effects. The most widely recognized MnO₂ quantum dot owns intrinsic structural characteristics including an ultra-small size and a high surface-to-volume ratio, thereby possessing more edge active sites per unit mass. Moreover, due to the edge effect and quantum confinement, a MnO₂ quantum dot exhibits enhanced or novel properties, such as high photoluminescence quantum efficiency. Furthermore, compared to the larger material, a MnO₂ quantum dot exhibits optical stability, wavelength-dependent photoluminescence, chemical inertness, cellular permeability and biocompatibility, which make it a promising candidate in bio-related sensing technology. The strong light absorption ability makes 0D MnO₂ nanomaterials promising energy acceptors in fluorescent sensors. The large specific surface area and outstanding catalytic property make 0D MnO₂ nanomaterials substrates in electrochemical sensors. Furthermore, the high oxidative capacity of 0D MnO₂ nanomaterials enables their widespread application in optical sensors.

The most common strategies for synthesizing 0D MnO₂ include the reduction of KMnO₄ by a reducing agent [26], the oxidation of MnCl₂ by an oxidizing agent [27], the oxidation of Mn₃O₄ by an oxidizing agent [28], and a redox reaction between KMnO₄ and Mn²⁺ [29]. The commonly used reducing agents for KMnO₄ include metallic elements, sodium citrate, HCl, H₂SO₄, methionine, 2-(N-morpholino)ethanesulfonic acid, poly(2-hydroxypropyl)acrylate, poly(3-hydroxytyrosyl)acrylate, poly(allylamino)acrylate, glucose, acetone and casein. The commonly used oxidizing agents for MnCl₂ include sodium hydroxide, H₂O₂ and bovine serum albumin. The oxidizing agent for Mn₃O₄ is NaOH. Common Mn²⁺ species involved in redox reactions with KMnO₄ include MnCO₃, MnCl₂ and MnSO₄.

For biosensing application, Guo et al. synthesized β-casein–MnO₂-quantum dots (CS-MnO₂ QDs) via an in situ one-pot method at room temperature [30] (Figure 1a). In this system, the protein simultaneously functions as both a reducing agent and a stabilizer. Specifically, specific amino acid residue in casein reduces MnO₄⁻ to MnO₂, while the protein acts as a capping agent to control the growth and stabilize the morphology of the quantum dot. Compared to MnO₂ quantum dots synthesized by a top–down strategy, this bottom–up approach is rapid, simple and environmentally friendly. The multifunctional CS-MnO₂ QDs nanoenzyme exhibits superior catechol oxidase-like activity, enabling their application in fluorescence-enhanced biosensing of redox-active dopamine based on an interface passivation strategy. Kuang et al. reported the synthesis of chiral MnO₂ nanoparticles (MnO₂ NPs) using D-gluconic acid as a ligand via a disproportionation reaction [31]. In summary, as shown in Figure 1b, D-gluconic acid was pre-introduced into the MnCl₂ solution to form Mn²⁺–gluconic acid complexes. Subsequently, Mn²⁺ was oxidized to Mn⁴⁺ in the presence of NaOH, which further induced the nucleation and growth of chiral MnO₂ NPs. Transmission electron microscopy (TEM) images reveal that chiral MnO₂ NPs exhibit uniform elliptical morphology with an average diameter of approximately 5 ± 0.8 nm. The designed chiral MnO₂ NPs were applied for detecting intracellular H₂O₂ based on a dual-mode approach combining circular dichroism spectroscopy and magnetic resonance imaging. Eduard Llobet et al. synthesized a MnO₂–graphene oxide composite (MnO₂/GO) via the oxidation of Mn₃O₄ by NaOH [28] (Figure 1c). Briefly, graphene oxide and Mn₃O₄ were dispersed in oxalic acid. After thorough mixing, a NaOH solution was added to facilitate the gradual precipitation of MnO₂ onto graphene oxide. Finally, MnO₂/GO was obtained after filtering, drying and grinding. By controlling synthesis parameters such as temperature, stirring speed, addition rate and raw material ratio, a sample with desired crystallinity and optimal NO₂ gas sensing performance was derived. Dinh Quang Khieu et al. synthesized MnO₂ nanoparticles via a redox reaction between KMnO₄ and MnSO₄ [32]. The MnO₂ nanoparticle was yielded by slowly adding a KMnO₄ solution to a MnSO₄ solution and further stirring at room temperature (Figure 1d). The specific reaction was as follows: 2KMnO₄ + 3MnSO₄ + 2H₂O → 5MnO₂ + K₂SO₄ + 2H₂SO₄. The synthesized MnO₂ nanoparticle was applied to detecting biomolecules, including chloramphenicol and tinidazole, utilizing electrochemical technology.

2.2. One-Dimensional MnO₂

1D MnO₂ is characterized by nanoscale dimensions in one dimension, which commonly includes nanotubes, nanofibers, nanowires and nanorods. Due to significant elongation in a single direction, 1D MnO₂ exhibits a rapid electron transport rate along the oriented axis, which further accelerates the response speed of the sensor. Furthermore, the interstitial space between 1D MnO₂ chains promotes electrolyte diffusion, conferring significant sensing advantages in photocatalytic, electrocatalytic, and photoelectrocatalytic sensors [33]. Based on composition, 1D MnO₂ can be categorized into single-component 1D MnO₂ and 1D MnO₂ composites. Single-component MnO₂ offers advantages such as low cost and simple preparation, but its performance is limited. 1D MnO₂ composites possess advantages of excellent conductivity, structural stability and large specific surface area due to synergistic effects, which expand their sensing applications in fields of biomedicine, environmental monitoring and remediation, as well as food-safety management.

Currently, the most prevalent strategies for synthesizing single-component 1D MnO₂ include the reduction of KMnO₄ using reducing agents [34], the oxidation of Mn²⁺ using oxidizing agents [35], and a redox reaction between KMnO₄ and Mn²⁺ [36]. Ethanol, HCl, acetic acid, 2-morpholinoethanesulfonic acid, porous carbon, graphene oxide, polyaniline (PANI) and metal ions are often used for reducing KMnO₄. The Mn²⁺ sources primarily include MnSO₄ and MnAc₂, and O₂, NaOH and Na₂SO₄ are utilized to oxidize Mn²⁺. The Mn²⁺ involved in the redox reaction with KMnO₄ is primarily MnSO₄. There exist two primary strategies for preparing 1D MnO₂ composites. One involves first synthesizing single-component 1D MnO₂, and then other substances are assembled with it, or a small-sized target nanostructure is chemically modified onto it. Another involves directly growing single-component 1D MnO₂ on a larger target composite or modifying the existing target single-component 1D MnO₂ with the MnO₂ nanostructure to form a heterostructure composite.

Li et al. prepared a ZnO/MnO₂ heterojunction nanocomposite through first synthesizing a 1D MnO₂ nanotube followed by compositing it with a ZnO nanosheet. The KMnO₄ was initially reduced by HCl to prepare the hollow MnO₂ nanotube [37] (Figure 2a). Then, several ZnO/MnO₂ binary hybrids were synthesized by incorporating varying amounts of the hollow MnO₂ nanotube in the process of preparing the ZnO nanosheet. The thin ZnO nanosheet was uniformly modified with the hollow MnO₂ nanotube, the wall thickness of which was approximately 40 nm. The ZnO/MnO₂ heterostructure exhibits not only a large specific surface area and superior gas adsorption efficiency at the heterointerface but also enhanced charge carrier transport efficiency, resulting in significantly improved NH₃ sensing performance under room temperature. Liu et al. prepared a 1D M13 phage@MnO₂ (M13-E4@MnO₂) nanowire at room temperature by adding NaOH to a mixed solution of M13-E4 and MnAc₂ [38] (Figure 2b). Specifically, the carboxyl group bearing the E4 tag in M13-E4 first bound to Mn²⁺ through coordination interaction. Secondly, NaOH was added to a mixture containing M13 and Mn²⁺ in order to oxidize Mn²⁺ to MnO₂ in air. In this process, O₂ from the air and the weakly basic environment provided by NaOH are crucial. The MnO₂-mineralized phage retains filamentous morphology, with a length of approximately 1 ± 0.1 μm and a diameter of 20–30 nm. The MnO₂ crystal is uniformly distributed on the phage scaffold. The synthesized M13-E4@MnO₂ nanowire can electrocatalytically oxidize H₂O₂ under neutral pH conditions, endowing significant potential for the biosensing of H₂O₂. Susana I. Córdoba de Torresi et al. directly modified a target Au nanoparticle onto a pre-synthesized 1D MnO₂ nanomaterial, effectively enhancing the catalytic activity of 1D MnO₂ and increasing the sensing sensitivity by 116%. First, a MnO₂ nanowire was synthesized via a redox reaction between KMnO₄ and MnSO₄ [39] (Figure 2c). The synthesized MnO₂ nanowire exhibits uniform morphology with a width of 50 ± 10 nm and a length of 1 μm, approximately. Subsequently, the nanowire served as rigid template for the uniform nucleation and growth of Au nanoparticles. The incorporated Au nanoparticle enhances the photocatalytic performance of the MnO₂ nanowire through a localized surface plasmon resonance effect. This composite material was applied to enhance H₂O₂ sensing based on photocatalytic oxidation.

2.3. Two-Dimensional MnO₂

A 2D nanostructure is marked by nanoscale dimensions in two dimensions, such as nanoscale monolayers and multilayers, nanoflakes and nanoribbons. 2D MnO₂ primarily encompasses MnO₂ nanoflakes and related composites. Similar to 1D MnO₂, single-component 2D MnO₂ is readily synthesized via a simple process, while exhibiting limited applicability and sensing performance. When MnO₂ is composited with other sensing substrates, the sensing performance, including responsiveness and repeatability, can be significantly enhanced.

The most prevalent strategies for synthesizing 2D MnO₂ include the reduction of KMnO₄ using a reducing agent [33], the oxidation of Mn²⁺ using an oxidizing agent [40], and a redox reaction between KMnO₄ and Mn²⁺ [41]. Reduction agents such as ethanol, HCl, H₂SO₄, 2-(N-morpholino)ethanesulfonic acid, 3-morpholinopropanesulfonic acid, ethyl acetate, metallic elements and metal hydroxide are usually utilized to reduce KMnO₄. H₂O₂, NaOH and triethanolamine are commonly used oxidizing agents for Mn²⁺. The Mn²⁺ used to reduce KMnO₄ is mainly MnCl₂, MnSO₄ and MnAc₂.

Xiong et al. proposed a top–down solar-assisted green strategy for preparing 2D MnO₂, which is environmentally friendly, simple, low-cost and rapid [42]. As shown in Figure 3a, ethanol was first added to a KMnO₄ solution as the electron acceptor and mixed thoroughly at room temperature. The precursor solution was then placed in a quartz flask and exposed to sunlight to initiate the reaction. During the process, the solution color changed from purple–red to dark brown. TEM images reveal that the synthesized MnO₂ nanosheet exhibits an ultrathin, nearly transparent 2D-layered structure with abundant graphene-like wrinkles. The synthesized MnO₂ nanosheet demonstrates excellent dispersion in aqueous solution without a surface stabilizer, facilitating its convenient application in colorimetric sensing. The synthesized MnO₂ nanosheet demonstrated rapid, sensitive colorimetric sensing for detecting triclosan in wastewater and assessing total antioxidant capacity in human serum. To enhance detection specificity and the high sensitivity demanded in biosensing, single-component MnO₂ is often limited. Conversely, integrating multiple sensing substrates and achieving synergistic enhancement between them enables the preparation of a biosensor with higher catalytic activity and selectivity, as well as chemical stability. In this article, a 2D MnO₂ nanosheet was synthesized via a one-step oxidation reaction of MnCl₂ by H₂O₂ [43]. Specifically, MnCl₂ was first added to a solution containing tetramethylammonium hydroxide and H₂O₂ and was stirred overnight. Then, the MnO₂ nanosheet was prepared through centrifugation and washing (Figure 3b). SEM images reveal that the product consists of wrinkled, ultrathin, monolayer nanosheets. Finally, the synthesized MnO₂ nanosheet@graphene oxide layer@Au nanoparticle composite was utilized to construct a novel electrochemistry-based cell sensor for detecting CTC in blood, demonstrating specific selectivity and sensitivity with broad application prospects in clinical diagnostics. Based on a redox reaction between KMnO₄ and MnSO₄, a 2D Al-MnO₂ nanosheet was first synthesized via a one-step hydrothermal method by Liu et al. The final composite was produced by combining the 2D Al-MnO₂ nanosheet with a NiCo-MOF nanowire array [44] (Figure 3c). SEM images reveal that the synthesized MnO₂ is composed of multiple nanosheets, each approximately 20 nm thick and 200 nm in diameter. Given the inherently poor conductivity of pure MnO₂, Al doping effectively narrows the band gap and increases the electron concentration in the conduction band of pure MnO₂, which further enhances the charge transport capability of pure MnO₂. It is worth noting that the electrostatic repulsion between negatively charged manganese vacancies within the MnO₂ nanosheet maintains the structural stability of nanosheet colloids, further improving the performance of fluorescence sensing [45,46,47].

A 2D MnO₂ nanosheet has great potential in sensing due to its large specific surface area. However, the practical application of 2D MnO₂ still faces the challenges of insufficient long-term stability and poor reusability. On the one hand, when there exists a redox reaction between MnO₂ and target analytes such as glutathione and H₂O₂, Mn⁴⁺ will be reduced to soluble Mn²⁺, which not only leads to irreversible consumption of the active substrate but also causes the collapse of the lamellar structure in MnO₂, eventually leading to the loss of reactive sites. During analyzing serum and environmental water samples, biomacromolecules such as protein can be irreversibly adsorbed on the surface of a MnO₂ nanosheet to form a pollution layer, which hinders the adsorption of the target and electron transfer across the substrate. Meanwhile, due to its high surface energy, a 2D nanosheet is prone to stacking and agglomeration during the period of long-term storage and repeated usage, resulting in a decrease in the effective specific surface area and the number of active sites. In order to solve the above-mentioned challenges, there exist several strategies, including element doping, heterostructure construction and compositing with the second material, which can effectively improve the structural stability of 2D MnO₂ nanosheets.

2.4. Three-Dimensional MnO₂

3D hierarchical structures are assembled by diverse structural units consisting of 0D, 1D or 2D nanostructures. Common 3D hierarchical structures include nanoclusters or nanoflowers and spherical or box-shaped hollow mesoporous nanomaterials. A 3D nanostructure effectively increases the specific surface area of the material, providing more active sites for sensing. In addition, a 3D nanostructure allows for the adsorption and reaction of more target molecules, thereby enhancing sensing sensitivity. The large specific surface area and reduced transfer resistance for ions and electrons of 3D MnO₂ make it suitable for (photo)electrochemical sensors. Furthermore, 3D structures exhibit high mechanical strength, rendering them suitable for sensing under extreme conditions, such as ultra-high or ultra-low temperature and ultra-high or ultra-low pressure [21,48,49,50,51,52].

The reduction of KMnO₄ using a reducing agent [53], the oxidation of Mn²⁺ using an oxidizing agent [54], and a redox reaction between KMnO₄ and Mn²⁺ [18] are often utilized to prepare 3D MnO₂. There exist various reduction agents for KMnO₄ reduction, such as HCl, 2-(N-morpholino) ethanesulfonic acid, oleic acid, polyallylamine hydrochloride, graphite and carbon fiber. The Mn²⁺ sources primarily include MnCl₂, MnSO₄ and MnAc₂. H₂O₂, Na₂SO₄, K₂S₂O₈, (NH₄)₂S₂O₈ and K₃[Fe(CN)₆] are mainly utilized to oxidize Mn²⁺. Common Mn²⁺ sources involved in redox reactions with KMnO₄ include MnCl₂, MnCO₃ and MnSO₄.

By utilizing a smartphone app platform and employing a 3D MnO₂ nanocluster as the signal probe to effectively amplify biological signals, Lin et al. not only ensured specific color assessment in colorimetric detection but also achieved real-time synchronous monitoring of Salmonella by coupling sampling time with sampling location. The 3D MnO₂ nanocluster was synthesized by reducing KMnO₄, using polyvinylpyrrolidone as the surfactant and HCl as the reducing agent respectively [55] (Figure 4a). Considering the potential risks associated with the hydrothermal method and the utilization of the strong oxidizing agent KMnO₄, Yue et al. employed a self-templating method to synthesize hollow cubic MnO₂ via an in situ oxidizing reaction [56] (Figure 4b). The resulting nanosheet and hollow hierarchical structure not only exposes more active sites to enhance reaction activity but also improves electron transfer kinetics and accelerates the reaction rate. Moreover, compared to the conventional bulk structure, the hollow nanostructure effectively releases stress generated during the sensing reaction, thereby enhancing its long-term operational stability. Additionally, the generated oxygen vacancy can serve as an active site for oxygen adsorption, which promotes the catalytic reaction. A more detailed explanation of how these vacancies are intentionally characterized is necessary. The 530.4 and 531.5 eV peaks in the O 1s spectrum are attributed to Mn-OH and H-O-H, respectively, which are caused by oxygen defects, indicating the presence of oxygen vacancies in H-MnO₂. The electron paramagnetic resonance (EPR) signal at g = 2.003 further demonstrates that H-MnO₂ has oxygen vacancies. The specific synthesis process is as follows: a surface-smooth cubic manganese complex intermediate was first synthesized via a co-precipitation strategy based on an oxidation reaction between K₃[Fe(CN)₆] and MnCl₂·4H₂O; subsequently, ferricyanide ions and Mn²⁺ diffused to the surface of the cubic template of the complex and formed the hollow MnO₂ structure by an in situ redox reaction with OH⁻. TEM images reveal that the MnO₂ hollow cube not only retains its original cubic structure but also exhibits a hollow configuration. This hollow structure, composed of numerous nanosheets, endows the material with a large specific surface area. The authors developed a smartphone-assisted portable colorimetric sensor by encapsulating the MnO₂ hollow cube in sodium alginate for on-site, real-time, specific and rapid colorimetric detection of three liver function biomarkers, including aspartate aminotransferase, alanine aminotransferase and alkaline phosphatase. The 3D heterojunction material can be obtained by growing the MnO₂ nanostructure on the existing 3D framework. The strong interfacial interaction at the heterojunction enhances sensitivity, reactivity and stability of the sensing material. Zhu et al. synthesized a MnO₂ nanoblock via a one-step hydrothermal method based on a redox reaction between KMnO₄ and MnSO₄ [57] (Figure 4c). SEM images reveal that the synthesized MnO₂ nano-blossom consists of small-sized MnO₂ nanosheets, with an average diameter of approximately 300–400 nm. Due to the excellent catalytic activity, the presence of abundant oxygen vacancies and p-p heterojunction of the obtained MnO₂/titanium carbide composite, it was applied to detect volatile organic compounds such as hexanal, a lung cancer biomarker, demonstrating good reproducibility and stability.

The performance differences of MnO₂ nanostructures with different dimensions (0D, 1D, 2D, and 3D) in electrochemical sensing are primarily manifested in four aspects: effective surface area, electron transfer pathways, analyte diffusion efficiency, and accessibility of active sites.

0D MnO₂ boasts an extremely high specific surface area, offering abundant adsorption sites. However, it suffers from high inter-particle contact resistance, impeding electron hopping transfer between particles and leading to a high overall charge transfer resistance (R_ct). Additionally, it tends to agglomerate, compromising its long-term stability. 1D MnO₂ offers several advantages in which electrons can rapidly conduct along the long axis of nanowires, significantly reducing R_ct. Intertwined into a 3D network, it provides continuous electrolyte diffusion channels. Yet, its specific surface area is slightly lower, with active sites predominantly distributed on the side and end faces. 2D MnO₂ possesses superior sensing capabilities, and its ultra-high theoretical specific surface area and exposed active edge sites facilitate proton intercalation/deintercalation by allowing for the insertion of ions or water molecules between layers. The abundant -OH on its surface enhances hydrogen bonding/electrostatic interactions with analytes. However, nanosheets are prone to stacking, leading to poor electron conduction perpendicular to the layer direction. 3D MnO₂ combines the high activity of low-dimensional units with the mechanical stability of the overall structure. The coexistence of macropores and mesopores promotes rapid electrolyte infiltration and product release. However, some active sites within 3D MnO₂ are buried, resulting in a utilization rate that is lower than theoretical values.

In summary, the electrochemical sensing performance of MnO₂ nanostructures with different dimensions is determined by their electron conduction pathways, effective surface area, and accessibility of active sites. 1D MnO₂ achieves an optimal balance between sensitivity and stability due to its quasi-1D electron channel and good dispersibility. 2D MnO₂ provides the highest response current per unit area and the lowest oxidation overpotential, making it suitable for the high-sensitivity detection of molecules such as neurotransmitters. 3D MnO₂ exhibits the best mass transfer capability and pre-enrichment effect, achieving the lowest detection limit in the detection of trace heavy metals and glucose. Although 0D MnO₂ is easy to synthesize, it is limited by inter-particle resistance and agglomeration issues, making it suitable only for scenarios where sensitivity is not critical.

3. Manganese Dioxide Polymorph

MnO₂ consists of several crystal structures, containing mainly α-, β-, γ-, δ-, ε-, λ- and R-forms, which are formed via the common-edge or common-angle connection of octahedron MnO₆ units. Among them, α-, β-, and γ-MnO₂ belong to chain tunnel structures. Specifically, α-MnO₂ is characterized by a “2 × 2, ~0.46 nm × 0.46 nm” tunnel structure. β-MnO₂ has a “1 × 1, ~0.23 nm × 0.23 nm” tunnel structure with a tunnel size of 2.3 × 2 Å. γ-MnO₂ is a rhombohedral crystal system composed of alternating “1 × 1, ~0.23 nm × 0.23 nm” channels of pyrolusite and “1 × 2, ~0.23 nm × 0.46 nm” channels of rhodochrosite. δ-MnO₂ exhibits a representative 2D laminar structure characterized by a substantial inter-lamellar spacing of approximately 7 Å. The crystal structure of ε-MnO₂ is relatively disordered. λ-MnO₂ has a 3D structure constructed by arranging the tunnel structures in different ways. R-MnO₂ has a long-range disordered structure.

MnO₂ with different crystal phases has different semiconductor properties due to the different arrangement of atoms. α-MnO₂ belongs to n-type semiconductors, which has a band gap of 1.3–2.7 eV and high conductivity of about 5.98 cm⁻¹·Ω⁻¹. β-MnO₂ belongs to n-type semiconductors, which has a narrow band gap of 0.2–0.37 eV and a conductivity of about 1.74 × 10⁻³ cm⁻¹·Ω⁻¹. γ-MnO₂ belongs to n-type semiconductors, and its conductivity is lower than that of β-MnO₂. δ-MnO₂, which belongs to n-type semiconductors, has a band gap of 1.36–2.0 eV and poor conductivity. The carrier type of ε- and λ-MnO₂ is not clear. The carrier type of R-MnO₂ is mainly n-type and has a band gap of 2.06–2.52 eV and poor conductivity.

As is known to all, the electrocatalytic activity and electrochemical sensing performance of MnO₂ are closely dependent on its crystal structure. Due to fully exposed MnO₆ edges and abundant oxygen vacancies contributed by the long Mn-O bond and low average oxidation state of Mn, α-MnO₂ has a high specific surface area and excellent electrocatalytic activity. Due to the narrow tunnel of the β-MnO₂-contributed molecular size screening effect, only small molecules can reach the active site, and macromolecules are selectively excluded. Although its electrocatalytic activity is usually lower than that of α-MnO₂ and β-MnO₂, γ-MnO₂ has the advantage of surface chemical adjustability. The layered structure of δ-MnO₂ endows it with the maximum available area and abundant oxygen vacancies, and the interlayer water in it contributes to ion transport. The crystal structure of ε-MnO₂ is relatively disordered, and its specific surface area and specific capacitance are low. The 3D spinel tunnel interconnection structure of λ-MnO₂ can accommodate a variety of ions without significant volumetric change, and there is little research on applying λ-MnO₂ in sensing. The long-range disordered structure of R-MnO₂ gives it a large number of defects and dangling bonds as active sites, contributing to particularly high surface activity.

It is necessary to discuss the mixed valence states of MnO₂ and their impact on sensing performance. Firstly, the distribution of manganese oxidation states in different crystal forms of MnO₂ should be discussed. The basic structural unit of MnO₂ is the [MnO₆] octahedron, and theoretically, Mn has a valence of +4. However, due to oxygen vacancies, proton insertion, or the insertion of alkali metal ions during crystal growth, some Mn⁴⁺ is reduced to Mn³⁺ to maintain charge balance. The proportion of Mn³⁺ and its stability vary significantly among different crystal forms. In α-MnO₂, when the tunnel contains large-sized cations, it can stabilize a large amount of Mn³⁺, with a molar ratio of Mn³⁺/(Mn⁴⁺ + Mn³⁺), reaching 15–30%. These Mn³⁺ are located near the vertices of the octahedra, adjacent to oxygen vacancies, forming localized electronic states. In δ-MnO₂, there are usually water molecules and cations between the layers, with a high proportion of Mn³⁺ (10–40%), which varies with the type of cations between the layers. The presence of Mn³⁺ leads to an increase in the interlayer spacing and an improvement in electronic conductivity. In β-MnO₂, it has a dense structure and contains almost no tunnel cations, with the proportion of Mn³⁺ typically being less than 5%, exhibiting near-stoichiometric Mn⁴⁺. In γ-MnO₂, it contains a large number of structural defects and Mn³⁺ (20–35%) and is often used as a battery cathode material, but it is less commonly used in sensing applications. Most importantly, the mechanism of the influence of the oxidation state on electrochemical sensing performance should be demonstrated. The oxidation state of Mn in MnO₂ could influence the performance of sensors, including charge transfer and conductivity, catalytic active center and analyte selectivity. Just in terms of charge transfer and conductivity, the 3d electronic configuration of Mn³⁺ is t₂g³ e_g¹, which exhibits a higher tendency for electron localization compared to Mn⁴⁺ (t₂g³ e_g⁰). However, the double exchange interaction between Mn³⁺-O-Mn⁴⁺ can generate mixed-valence conductive channels. Therefore, an appropriate amount of Mn³⁺ can significantly reduce the charge transfer resistance (R_ct) of the material. For example, δ-MnO₂ nanosheets rich in Mn³⁺ exhibit an R_ct for H₂O₂ that is 1–2 orders of magnitude lower than that of β-MnO₂. As for the catalytic active center, the Mn³⁺ site is considered to be the true active center for many electrocatalytic reactions, such as H₂O₂ reduction, glucose oxidation, and ascorbic acid oxidation. Mechanistically, Mn³⁺ is more prone to bind with dissolved oxygen or peroxides, forming highly active Mn³⁺-(O₂) or Mn³⁺-(OH) intermediates, which subsequently complete the catalytic cycle through electron transfer from Mn³⁺ to Mn⁴⁺. An excessively high proportion of Mn³⁺ may lead to aggregation of Mn³⁺ sites, forming an inert Mn₂O₃ phase, which in turn reduces catalytic activity. Therefore, there exists an optimal Mn³⁺/(Mn⁴⁺ + Mn³⁺) ratio to achieve the maximum response current. Moreover, oxidation states of Mn will influence the analyte selectivity in sensing. Different analytes exhibit varying affinities for the oxidation states of manganese. For example, H₂O₂ tends to coordinate with Mn⁴⁺, subsequently accepting electrons to generate •OH and OH⁻, with Mn⁴⁺ being reduced to Mn³⁺. Therefore, materials with a high initial proportion of Mn³⁺ can complete this reduction step more quickly, whereas dopamine preferentially complexes with surface Mn³⁺ and undergoes oxidation through ligand-to-metal charge transfer (LMCT). The response of dopamine to δ-MnO₂, which is rich in Mn³⁺, is typically 5–10 times greater than that to β-MnO₂.

4. Manganese Dioxide Nanostructure-Based Novel Sensing Technology

Chemical sensors can be categorized into chemical reaction-based and physical interaction-based types, depending on the interactions between the receptor and the target substance [58]. In the process of chemical reaction-based sensing, a chemical reaction between the target and the receptor occurs, such as redox. Novel chemical reaction-based sensors include electrochemical sensors, EL sensors, photoelectrochemical (PEC) sensors and colorimetric sensors. Physical interaction-based sensing involves highly specific physical interaction between the target and the receptor. Novel physical interaction-based sensors include fluorescence sensors, photothermal sensors and QCM sensors (Scheme 2). MnO₂ represents one of the ideal candidate materials for constructing a high-performance novel sensor. However, pure MnO₂ suffers from poor conductivity and structural instability. Therefore, compositing MnO₂ with other functional materials not only compensates for its inherent disadvantage but also achieves a synergistic enhancement effect.

4.1. Electrochemical Sensor

An electrochemical sensor conducts analyte identification by converting the component and concentration of the target into a current, potential or conductivity signal through a redox reaction between the target analyte and the recognition layer [59]. To date, electrochemical sensors have become the most widely applied type due to their high sensitivity, high efficiency, low cost, low detection limit and versatile signal consisting of voltage, current and impedance. As mentioned above, MnO₂ possesses flexible valence states that can reduce overpotential in electrochemical reactions and enhance the current response in the system, thereby improving the sensitivity and selectivity of electrochemical sensors. Simultaneously, the high specific surface area of the MnO₂ nanostructure provides abundant active sites for adsorbing and reacting with target analytes, enabling faster mass transport and stronger signal output within the system, thereby further improving the sensitivity and response speed of electrochemical sensors. However, pure MnO₂ applied in electrochemical sensors suffers from poor conductivity, whereas a synergistic enhancement effect can be achieved by combining pure MnO₂ with other highly conductive materials. The incorporation of highly conductive materials, like graphenes, carbon materials and conductive polymers, not only enhances the electron transport rate within MnO₂ but also effectively prevents agglomeration of MnO₂ nanoparticles, which contributes to the full exposure of active sites, thereby further improving the signal strength and sensitivity of electrochemical sensors. Noble metal nanoparticles like Au, Ag, Pt and Pd inherently possess excellent conductivity and exhibit catalytic activity toward numerous reactions, which not only improve electron transport efficiency within MnO₂ but also generate dual-enhancement catalytic effects with MnO₂, thereby enhancing the selectivity and sensitivity of electrochemical sensing. Consequently, the MnO₂ nanostructure and its composites offer exceptionally prominent advantages when applied in novel electrochemical sensors.

A more in-depth discussion of the interactions between MnO₂ and target analytes, charge transfer and catalytic activity is essential for a comprehensive review on MnO₂-based electrochemical sensors.

A deep understanding of the chemical interaction between MnO₂ and the analyte is a prerequisite for the rational design of highly selective sensors. Depending on the analyte, the interaction mechanism can be divided into three categories: electrostatic interactions and hydrogen bonding, enzyme-like catalytic oxidation, ion exchange and surface complexation. Electrostatic interactions and hydrogen bonding are first discussed. Under neutral pH conditions, the isoelectric point of MnO₂ is approximately 2–4, indicating a negatively charged surface. Dopamine undergoes protonation of its amine group at physiological pH, resulting in a positively charged state, thus generating a strong electrostatic attraction with MnO₂. Simultaneously, the catechol group of dopamine can form a bidentate hydrogen bond with the hydroxyl group on the surface of MnO₂. This pre-adsorption significantly reduces the distance between the active site and the analyte, enabling rapid electron transfer to the Mn⁴⁺ center via a tunneling effect. Subsequently, dopamine is oxidized to dopamine quinone, releasing two electrons and two protons, which are transferred to the electrode through the aforementioned Mn³⁺/Mn⁴⁺ pathway. Enzyme-like catalytic oxidation is then demonstrated. MnO₂ exhibits efficient catalase-like activity. Its catalytic cycle can be summarized as follows: Mn⁴⁺ + H₂O₂ → Mn³⁺ + •OOH + H⁺, Mn³⁺ + H₂O₂ → Mn⁴⁺ + •OH + OH⁻. The generated free radicals exhibit strong oxidizing properties and can further oxidize other substrates on the electrode surface. This cascade reaction renders MnO₂ an ideal material for enzyme-free sensors, circumventing the issue of natural enzyme deactivation. It is necessary to illustrate ion exchange and surface complexation. The K⁺ or Na⁺ ions between the layers of layered δ-MnO₂ can undergo ion exchange with heavy metal ions in solution. Meanwhile, the vacant sites in the tunnel structure can form inner-sphere complexes with heavy metal ions. In anodic stripping voltammetry (ASV) detection, after the enrichment step, applying a negative potential reduces the heavy metal ions to zero valence and deposits them on the surface of MnO₂; then, applying a forward scan, the metals are re-stripped as ions, generating sharp current peaks. The tunnel size of MnO₂ determines the ion selectivity: 2 × 2 tunnels (α-MnO₂) have a preferential embedding ability for Pb²⁺, while 1 × 1 tunnels (β-MnO₂) essentially exclude Pb²⁺.

The electrochemical sensing performance of MnO₂ is essentially determined by the efficiency of heterogeneous electron transfer (HET) between the analyte and the electrode interface. This process is regulated by two key factors: the crystal structure and surface electronic density of states of MnO₂. Different crystal forms of MnO₂ (α, β, γ, δ, and λ) exhibit distinct [MnO₆] octahedral connection modes, forming tunnel or layered structures. Among them, δ-MnO₂ and α-MnO₂ demonstrate the lowest charge transfer resistance (R_ct). This is attributed to the presence of numerous Mn³⁺ sites and oxygen vacancies in their structures, which significantly enhance the density of electronic states near the Fermi level. In contrast, β-MnO₂ exhibits near-insulating semiconductor behavior due to the extremely low proportion of Mn³⁺, leading to a sluggish HET process. More importantly, the surface redox couple of Mn³⁺/Mn⁴⁺ acts as an intermediate energy level for “electron hopping”. When an appropriate potential is applied, the Mn⁴⁺ on the electrode surface can be reduced to Mn³⁺, and at the same time, adsorbed analyte molecules inject electrons into Mn⁴⁺, completing charge compensation. This process can be quantitatively characterized by Mott–Schottky analysis and electrochemical impedance spectroscopy (EIS): in solutions containing the target analyte, the R_ct of the MnO₂-modified electrode is usually significantly reduced, indicating that the analyte participates in and promotes interfacial electron transfer. Recent density functional theory (DFT) calculations further indicate that surface hydroxyl groups (-OH) and oxygen vacancies (V₀) can effectively modulate the local work function of MnO₂. Oxygen vacancies introduce defect energy levels into the forbidden band, reducing its band gap from approximately 2.5 eV (without defects) to 1.2–1.8 eV, thereby enabling the oxidation or reduction of analytes at lower overpotentials. Additionally, the high specific surface area and crystal facet exposure effects brought by nanostructures are also crucial: for example, the (001) facet of δ-MnO₂ nanosheets possesses abundant edge sites, where the Mn atoms are coordination-unsaturated and more prone to direct interaction with electron donor analytes.

The electrocatalytic activity of MnO₂ can be decomposed into two parts: “intrinsic activity” and “structure-induced activity”. The intrinsic activity originates from the mixed valence states (+3 and +4) of Mn. The presence of Mn³⁺ endows MnO₂ with activities similar to those of peroxidase and superoxide dismutase. For the sensing of H₂O₂, the widely accepted mechanism is as follows: adsorption (H₂O₂ coordinates with surface Mn⁴⁺), electron transfer (H₂O₂ loses two electrons to generate O₂, and these two electrons are transferred to the electrode through the Mn⁴⁺/Mn³⁺ cycle), and regeneration (Mn³⁺ is re-oxidized to Mn⁴⁺ by the anodic potential). The rate-determining step of this process is typically the first electron transfer. Catalytic activity can be quantified by the Tafel slope and turnover frequency (TOF). The Tafel slope of δ-MnO₂ for H₂O₂ oxidation is often as low as 40–60 mV/dec, indicating its excellent catalytic performance at low overpotentials. The structure-induced activity originates from the edge sites, step sites, and defect centers brought about by the nano-morphology. For example, MnO₂ nanoflowers assembled from ultrathin nanosheets expose a large number of edge oxygen vacancies and exhibit an extremely high current response to the oxidation of ascorbic acid (AA). MnO₂ nanorods, growing along the [001] direction, primarily expose (110) and (100) surfaces, exhibiting stronger adsorption and catalytic leaching capabilities for heavy metal ions such as Pb²⁺. Mesoporous MnO₂ possesses 3D interconnected pores, which not only provide channels for rapid diffusion of electrolyte ions but also allow a large number of dangling bonds on the pore walls to directly participate in the pre-concentration and electrocatalysis of analytes.

4.2. Electrochemiluminescence Sensor

An EL sensor conducts target identification utilizing an optical signal generated from electrochemical reaction-excited high-energy electron transfer of luminescent material, highlighted by high sensitivity, a broad linear range, a high signal-to-noise ratio and simple instrumentation, finding extensive applications in environmental monitoring, bioanalysis and clinical diagnostics [60]. Unlike its role in electrochemical sensors, the advantage of applying the MnO₂ nanostructure to an EL sensor primarily lies in its efficient catalysis and the regulation of the EL process. Most EL materials generate light signals by forming active intermediates through a series of electron transfer reactions with a co-reactant. Due to its excellent electrochemical reactivity and extremely high specific surface area, MnO₂ can serve as an efficient co-reaction promoter to accelerate the redox of co-reactants significantly. This enables the generation of more luminescent active intermediates under higher rates and lower applied voltages, thereby significantly enhancing the signal intensity and sensitivity of EL sensing. Additionally, the vast specific surface area of MnO₂ nanostructures enables efficient loading of substantial amounts of electrochemiluminescence materials, facilitating the construction of integrated electrochemiluminescence sensors, such as sandwich-type sensors and solid-phase sensors. However, pure MnO₂ exhibits poor conductivity and limited functionality. An electrochemiluminescence sensing platform with superior performance can be constructed by incorporating secondary functional materials, such as noble metals and conductive materials, into pure MnO₂. The synergistic effect between highly conductive materials and MnO₂ creates a “high-speed channel” for electron transport, thereby enhancing the signal intensity and response speed of electrochemical luminescence sensors. Precious metals can not only serve as active electrochemical-luminescent materials but also generate synergistic catalytic effects with MnO₂, thereby enabling the construction of self-amplifying or dual-signal electrochemical luminescence sensors.

4.3. Photoelectrochemical Sensor

The detection principle of a PEC sensor is as follows: The separation between the charge and hole occurs on the surface of a photoactive material induced by light excitation. The electron on the surface of the photoactive material transfers from the valence band to the conduction band to generate an electron–hole pair. The formation of an electron–hole pair promotes a redox reaction on the electrode surface, subsequently generating a measurable current in the external circuit. The PEC signal is related to analyte concentration when the analyte interacts with the substrate. A PEC sensor typically exhibits multiple advantages, including high response rates, low detection limits, negligible background signals, low energy consumption and simple instrumentation. Consequently, PEC sensors have found extensive applications in food testing, bioanalysis, clinical diagnostics and environmental protection [61]. MnO₂ serves as a direct electrocatalyst in electrochemical sensors, while it primarily synergizes with optically active materials in PEC sensors. MnO₂ polymorphs possessing n-type semiconductor properties can better match numerous optically active n-type semiconductors. The photo-generated electron rapidly transfers from MnO₂ to the semiconductor, while the hole transfers from the semiconductor to MnO₂ when a “MnO₂@n-type semiconductor” composite is exposed to illumination, which significantly suppresses the recombination of the electron–hole pair, thereby enabling the generation of stronger and more stable photocurrent signals. Additionally, the photoelectrical response efficiency of MnO₂ can be further enhanced by combining with other photoactive materials or conductive materials. Therefore, the MnO₂ nanostructure and its composites offer significant advantages for their application in novel PEC sensors.

4.4. Colorimetric Sensor

Colorimetric sensors have merits such as high sensitivity, high selectivity, high detection efficiency, simple fabrication and ease of visual identification, making them widely used for detecting toxic pollutants, like metal ions, organic dye and pesticides [62]. In enzyme-mediated and enzyme-mimetic colorimetric sensors, analyte detection is carried out based on color changes induced by the redox of 3,3,5,5-tetramethylbenzidine catalyzed by a natural or bioinspired enzyme. The advantages of utilizing MnO₂ in colorimetric sensors include its unique enzyme-like catalytic activity and its inherent property as a color indicator. MnO₂ possesses intrinsic oxidase-like activity, which is capable of oxidizing colorless substrates into colored ones without H₂O₂. Compared to a natural enzyme, a MnO₂ bioinspired enzyme offers lower cost, higher stability and stronger colorimetric signal intensity. Furthermore, a nano-MnO₂ solution can function as a color indicator, making it simple and convenient for colorimetric sensing. Specifically, dark Mn⁴⁺ is reduced to transparent Mn²⁺ upon the addition of a reducing analyte. Notably, when MnO₂ is combined with a noble metal nanoparticle, a synergistic catalytic effect between them significantly enhances the enzyme-like catalytic activity, resulting in a faster color reaction and deeper color. Therefore, it is worth exploring the application of the MnO₂ nanostructure and its composites in novel colorimetric sensors.

4.5. Fluorescent Sensor

As a type of colorimetric sensor, a fluorescence sensor detects the target by monitoring changes in fluorescence signals in fluorophore after reacting with analytes [63]. The MnO₂ nanostructure exhibits high fluorescence quenching efficiency, which regulates the “on” and “off” of the fluorescence signal through both the Förster resonance energy transfer (FRET) and the inner filter effect (IFE). FRET only occurs over a very short distance. Specifically, the fluorescent molecule will transfer energy to nearby MnO₂ nanomaterials through dipole–dipole interaction instead of emitting photons directly, resulting in fluorescence quenching. On the other hand, IFE can occur over a long distance. Particularly, when fluorescent molecules and MnO₂ nanomaterials coexist, the strong absorption spectrum of MnO₂ overlaps with the excitation or emission spectrum of fluorescent molecules, resulting in a decrease in fluorescence signals. Fluorescent sensors have been widely adopted in environmental monitoring and medical diagnostics due to their high sensitivity, rapid response and non-destructive analysis. Notably, the 2D planar structure of a MnO₂ nanosheet maximizes face-to-face contact and interaction between MnO₂ and fluorophore molecules, significantly enhancing fluorescence quenching efficiency and sensing sensitivity. Furthermore, integrating the MnO₂ nanostructure with a Au nanorod or mesoporous SiO₂ can combine the strength of multiple materials to achieve more complex sensing. Consequently, it is worth exploring the application of MnO₂ and its composites in novel fluorescent sensors.

4.6. Photothermal Sensor

In a photothermal sensor, target analysis is carried out through detecting temperature changes originating from a photothermal conversion substrate which absorbs light energy and converts it into thermal energy. The low cost, simple operation, easy data readout and portability are main highlights of photothermal sensing, making it widely applicable in environmental monitoring, biomedical research and industrial control [17]. The detection mechanism of a photothermal sensor refers to the photothermal effect. The photothermal conversion substrate generates heat via electron transition or vibration under an external light source, while the produced heat can be altered when the photothermal material interacts with the target substance, enabling concentration sensing of analytes. The MnO₂ nanostructure exhibits outstanding photothermal conversion efficiency. Upon absorbing infrared light, MnO₂ converts light energy into heat through a non-radiative relaxation process, causing an increase in ambient temperature. MnO₂-based photothermal sensors can be divided into “signal-off” sensing based on chemical reduction and “signal-on” sensing in which MnO₂ acts as a carrier or quencher. Specifically, in “signal-off” sensing, in the presence of a reducible target, MnO₂ with high infrared absorption activity is reduced to Mn²⁺ with extremely weak infrared absorption, resulting in the weakening or disappearance of the substrate’s photothermal effect and a decrease in environmental temperature. In “signal-on” sensing, other photothermal nanomaterials can be loaded on the MnO₂ nanosheet. Before adding the target substance, MnO₂ will quench or encapsulate the photothermal effect of other materials. Upon adding the target substance, MnO₂ is decomposed, and then the photothermal material is released, and the photothermal effect is restored. In photothermal sensing, MnO₂ is typically combined with other highly photothermally active materials, including precious metals, copper sulfide and polydopamine. The synergistic effect between MnO₂ and photothermally active materials yields enhanced photothermal effects and stronger temperature signals, thereby improving the sensitivity and signal-to-noise ratio of photothermal sensing. Therefore, applying MnO₂ and its composites in novel photothermal sensors holds great significance.

4.7. Quartz Microbalance Sensor

QCM sensing is based on the piezoelectric effect, analyzing the target substance by measuring the impact of the adsorbed mass on the resonant frequency of a quartz crystal within an alternating electric field. Characterized by ultra-high sensitivity and real-time dynamic monitoring, a quartz crystal microbalance sensor finds extensive applications in biosensing and environmental monitoring [18]. MnO₂ nanostructures possess extremely high specific surface areas and abundant pores. They can not only efficiently adsorb and enrich target analytes through physical adsorption or chemical interactions but also significantly “amplify” the mass of the target molecule into mass change across the entire nanomaterial layer. This effect substantially enhances the frequency response signal of QCM sensing, thereby improving detection sensitivity. Notably, pure MnO₂ suffers from poor conductivity and structural instability under specific conditions, and the durability and sensitivity of QCM sensors can be enhanced by combining pure MnO₂ with conductive polymers. Consequently, applying nano-MnO₂ and its composites to novel QCM sensors holds considerable significance.

5. Manganese Dioxide Nanostructure-Based Novel Sensing in Environmental Monitoring, Food-Safety Monitoring and Biomedical Monitoring

MnO₂ nanostructures, encompassing 0D, 1D, 2D and 3D nanostructures, have been extensively applied in environmental monitoring, including air, water and soil pollutants, as well as food-safety monitoring and biomedical monitoring. Novel sensing technologies include electrochemistry, electrochemiluminescence, photoelectron chemistry, colorimetry, fluorescence, photothermal techniques and QCM. In this section, based on different application fields and multiple sensing technologies in a specific field, the application of MnO₂ nanostructure-based novel sensing in various fields over the past five years are summarized, particularly focusing on sensing performance and corresponding mechanisms, and the improvement of sensing performance by MnO₂ microstructure.

5.1. Environmental Monitoring

5.1.1. Air Pollutant Monitoring

Gas sensing is primarily carried out based on electrochemical technology, detecting the target gas by monitoring conductivity changes in sensing substrates under different gas environments. The process involves multiple theories, including the chemisorption oxygen model, grain boundary barrier model, bulk resistance model and space charge layer model. The core principle of gas sensing lies in the interaction between the gas and substrate surface. 0D, 1D, 2D and 3D MnO₂ can be utilized for air pollutant monitoring. Common air pollutants include NH₃, NO₂, HCHO, CH₃COOH, CH₃CHO, hexanal, triethylamine and allyl mercaptan.

The application of MnO₂ nanostructures and their composites in air pollutant monitoring over the past five years have been summarized in

Table 1. MnO₂ nanostructure-based novel sensing for air pollutant monitoring.

Nanostructure	MnO2 Morphology	Substrate	Analyte	LOD	Response	Response Time	Reference
0D	nanoparticle	PANI@MnO2@rGO	NH3	0.21 ppm	15.56% under 50 ppm	6–10 s	[64]
	nanoparticle	graphene oxide@ MnO2	NO2	385 ppm	16.3% under 1 ppm		[28]
1D	nanowire	Ni-MnO2/ Ti3C2Tx·MXene	NH3	0.23 ppm		45 s	[65]
	nanorod	Co-MnO2@ MnCo2O4.5	triethylamine		340% under 50 ppm	9 s	[66]
2D	nanosheet	MXene·Ti3C2TX@ NiCo2O4@MnO2	CH3COOH	0.05 ppm	12.5% under 20 ppm	130 s	[67]
	nanosheet	NiO/MnO2@NiO	allyl mercaptan	15 ppb	56.69% under 40 ppm	115 s	[1]
3D	nanoflower	MnO2@Ti3C2Tx	glyoxaldhyde	20 ppm	52% under 20 ppm	134 s	[57]
	nanonest	MnO2/TiO2	triethylamine	12 ppb	6.8% under 100 ppm		[68]

, particularly including the morphology of MnO₂, the composition of the substrate and the analyte, the limit of detection (LOD), the response and response time of the sensor, and the corresponding reference.

MnO₂ Nanostructure-Based Non-Heterostructure Composite

In order to enhance sensing performance and broaden application scenarios, compositing pure MnO₂ with other materials or functional modifications of MnO₂ are essential.

Zeng et al. reported a simple process for preparing a sensing composite via physical blending of PANI-loaded reduced graphene oxide (rGO) and MnO₂ nanoparticles. Specifically, firstly, a MnO₂ nanoparticle and rGO were prepared via acid-assisted hydrothermal reduction pyrolysis and the Hummer-hydrazine reduction method, respectively. Subsequently, the ternary nanocomposite PANI@MnO₂@rGO was assembled through in situ oxidation polymerization-solution self-assembly [64] (Figure 5a). For PANI alone and PANI@MnO₂, the measured gas response-recovery profiles did not exhibit significant changes upon exposure to NH₃ gas. A PANI@MnO₂@rGO-based sensor exhibits remarkable selectivity toward NH₃, demonstrating a response value of 15.56 at 100 °C for 50 ppm NH₃, with response and recovery times of 6 and 10 s, respectively. The sensing mechanism of PANI@MnO₂@rGO involves an oxygen-containing anion on the surface: The O₂ is absorbed on the surface of the ternary composite. The adsorbed O₂ molecule undergoes dissociation by extracting electrons from the conduction band of the n-type MnO₂ nanoparticle, generating multiple oxygen-containing anion species. These oxygen-containing anion species react with NH₃, thereby reducing the resistance of the substrate and enhancing sensing performance. Furthermore, the incorporation of MnO₂ and rGO enhances the gas adsorption site, promotes charge transfer and increases the overall material conductivity, collectively elevating sensitivity, ensuring effective responsibility for changes in NH₃ concentration and retaining outstanding stability.

In addition to physical blending with other substrates, heteroatom doping is also an effective method to enhance the sensing performance of pure MnO₂. Benefiting from the synergistic effect of chemical and electronic sensitization from heteroatoms, the surface activity of substrate materials can be significantly improved, thereby promoting the catalytic decomposition of gas molecules. Mari et al. synthesized a composite material consisting of a Ni-doped MnO₂ nanowire (Ni-MnO₂ NWs) and Ti₃C₂T_x·MXene (Ni-MnO₂/Ti₃C₂T_x·MXene) using a one-step hydrothermal method and self-assembly technique applied in NH₃ detection based on the electrochemical method under room temperature [65] (Figure 5b). There exist significant structural advantages in Ni-MnO₂/Ti₃C₂T_x·MXene: The 3D hierarchical architecture is formed through loading of Ni-MnO₂ NWs onto a multilayer Ti₃C₂T_x·MXene surface uniformly with the nanowire tightly integrating into the MXene layer. Additionally, more oxygen vacancies are introduced, and a Mn³⁺/Mn⁴⁺ mixed oxidation state is formed by Ni doping. Moreover, Brunauer–Emmett–Teller (BET) analysis reveals that the composite material possesses a specific surface area of 85.5 m²/g, which is 26 times higher than that of pure MXene. X-ray photoelectron spectroscopy (XPS) analysis was performed to assess the elemental composition and electronic properties of MnO₂, Ni-MnO₂, Ti₃C₂T_x·MXene, MnO₂/Ti₃C₂T_x·MXene, and Ni-MnO₂/Ti₃C₂T_x·MXene electrode materials. The multiple deconvolutions observed in each material indicate several contributions to the MnO₂ spectra, likely due to the presence of Mn⁴⁺/Mn³⁺ oxidation states. Ni-MnO₂/Ti₃C₂T_x·MXene suggests a lower Mn oxidation state than in α-MnO₂, likely due to interactions with Ni in Ni-MnO₂/Ti₃C₂T_x·MXene, creating oxygen vacancies that improve structural stability and electrochemical reversibility. These structural advantages of Ni-MnO₂/Ti₃C₂T_x·MXene endow it with outstanding performance toward NH₃ sensing under optimized conditions, with a sensitivity of 0.072 μA/ppm, a detection limit as low as 0.23 ppm, a response/recovery time of only 45 s/79 s (at 20 ppm NH₃), bilinear response across a NH₃ concentration of 1–50 ppm, along with outstanding selectivity, repeatability and long-term stability. Compared to a bare SPCE, the MnO₂, NiMnO₂, Ti₃C₂T_x·MXene, Ni/Ti₃C₂T_x·MXene, MnO₂/Ti₃C₂T_x·MXene, Ni-MnO₂/Ti₃C₂T_x·MXene, and Ni-MnO₂/Ti₃C₂T_x·MXene-modifed electrodes exhibit the highest chronoamperometric current response. The response/recovery times for the Ni-MnO₂/Ti₃C₂T_x·MXene sensor were 45 s/79 s, respectively, which are 1.18/1.50, 1.68/1.24,1.40/1.09, 1.22/1.31, 1.46/1.25, and 1.06/1.13 times shorter than those of the bare electrode and the MnO₂, Ni-MnO₂, Ti₃C₂T_x·MXene, Ni/Ti₃C₂T_x·MXene, and MnO₂/Ti₃C₂T_x·MXene ones, respectively. In other words, it exhibited the shortest response/recovery times. It is noteworthy that Ni-MnO₂/Ti₃C₂T_x·MXene has been successfully applied to fish meat freshness monitoring. The sensing primarily relies on releasing electrons that originated from a three-electron oxidation reaction between NH₃ and [TFSI]⁻ in ionic liquid electrolytes. Ni-MnO₂/Ti₃C₂T_x·MXene enhances the signal intensity through a triple synergistic effect, in which Ni acts as an electrocatalyst to promote NH₃ oxidation, MnO₂ NWs provide a reaction site via high adsorption capacity and oxygen vacancy, and MXene accelerates electron transport due to its high conductivity.

By doping MnO₂ or physically blending it with other sensor substrates, the synergistically coupling advantage of different active materials can effectively enhance the sensitivity, selectivity, repeatability and long-term stability of gas sensing.

MnO₂ Nanostructure-Based Heterojunction Composite

For MnO₂ sensing materials prepared via physical blending without chemical bonding interaction, issues including substrate separation and degraded sensing performance after prolonged operation often exist. Conversely, sensing stability can be enhanced by constructing a heterostructure with robust chemical bonding interaction. It is well-established that forming a heterostructure between two semiconductors is effective for improving gas-sensing performance. Strong interaction at the heterointerface significantly influences oxygen adsorption, catalytic activity and charge transfer, thereby further enhancing sensor response.

Li et al. synthesized a NiO nanosheet and MnO₂@NiO nanosheet via a sol-gel and etching-recrystallization method for detecting allyl mercaptan (AM), a marker gas of psychological stress release, thereby assessing stress levels [1] (Figure 5c). Specifically, the particular structure generated from the attachment of the p-p heterojunction consists of MnO₂ which vertically aligns with a porous NiO nanosheet that contributes to superior sensing performance. Fundamentally, the gas sensing of the MnO₂@NiO nanosheet operates primarily through the release of electrons when the AM gas reacted with adsorbed oxygen on its surface, leading to a decrease in resistance. The response time and recovery time are closely related to the detection speed of the sensor, and they are two important parameters for the evaluation of gas sensing performance. The values of response time and recovery time of the NiO and MnO₂@NiO sensor were respectively 480 and 15 s, and 115 and 25 s at 40 ppm of AM vapor. Compared to a NiO nanosheet-based sensor, this composite-based sensor exhibits a faster response time for 40 ppm of AM gas. Additionally, the p-p heterojunction in MnO₂@NiO accelerates the response rate via interfacial charge transfer, and the porous structure and high oxygen adsorption capacity of NiO contribute to a higher response value.

Additionally, direct loading MnO₂ onto the substrate via chemical bonding interaction represents another effective modification strategy. This approach not only enables efficient dispersion of MnO₂ on the substrate to prevent agglomeration but also enhances the selectivity and responsiveness of MnO₂ through heterojunction regulation. Yao et al. synthesized a MnO₂/Ti₃C₂T_x composite via a one-step hydrothermal method for the detection of hexanal gas, a lung cancer biomarker, with high sensitivity and selectivity [57] (Figure 5d). In particular, the composite was constructed by wrapping a MnO₂ nanosheet tightly on Ti₃C₂T_x·MXene, thereby forming a p-p heterojunction structure with abundant oxygen vacancies and high catalytic activity. Compared to pure MnO₂ nanospheres, the composite demonstrated a higher response value, namely 52% response, for 20 ppm of hexanal at 100 °C, with response and recovery times of 134 s and 381 s respectively, as well as an excellent linear range, resistance to humidity interference and long-term stability. The outstanding sensing performance of MnO₂/Ti₃C₂T_x primarily stems from the carrier regulation at the p-p heterojunction: O₂ molecules adsorbing in air form a hole accumulation layer (HAL), which thins upon hexanal contact due to electron–hole recombination, causing a significant resistance increase. Concurrently, high catalytic selectively of MnO₂ promotes hexanal oxidation. In summary, the heterojunction interface accelerates carrier separation and transport, and the synergistic interaction between the conductive network of MXene and the catalytic active site of MnO₂ coupled with the oxygen vacancy and defect site provide additional gas-reactive centers, thereby combining high responsivity and unique selectivity under low temperatures. A more detailed explanation of how these vacancies are intentionally characterized is necessary. The XRD diffraction peak corresponding to the δ-MnO₂ of MnO₂/Ti₃C₂T_x became very weak because of the growth of non-oriented layered MnO₂ nanosheets on the surface of MXene, or the existence of a large number of oxygen vacancies in MnO₂ after compounding with MXene. The low-binding energy OII peak at 531.4 eV in the XPS spectrum corresponds to oxygen vacancies, adsorbed oxygen, or M (Mn, Ti)-OH bonds. It is worth noting that the Mn³⁺/Mn⁴⁺ strength of MnO₂/Ti₃C₂T_x in the Mn 2p XPS spectra of MnO₂/Ti₃C₂T was much greater than that of MnO₂, indicating that the amount of Mn³⁺ ions in MnO₂ nanosheets on the surface of MnO₂/Ti₃C₂T_x greatly increased. The rise in the Mn³⁺ amount indicates a high electron cloud density around Mn sites, leading to more oxygen vacancies.

5.1.2. Pollutant Monitoring in Water Quality and Soil

MnO₂ nanostructure-based sensors utilizing electrochemical techniques, PEC techniques, colorimetric techniques, fluorescence techniques, photothermal techniques, QCM techniques, microfluidic techniques and solid-phase extraction adsorption techniques can be employed for monitoring pollutants in water and soil. Common water and soil pollutants include heavy metal ions, such as Cr, As, Se, Sr, Hg, and Pb; N₂H₄·H₂O; organophosphorus pesticides; antibiotic drugs, such as tetracycline and chloramphenicol; sulfonamide drugs, such as sulfadiazine; phenolic compounds, such as catechol, resorcinol and tetrabromobisphenol A; diethyl phthalate; malachite green; as well as rifampicin.

Applications of MnO₂ nanostructures in monitoring water and soil pollutants over the past five years have been summarized in

Table 2. MnO₂ nanostructure-based novel sensing for pollutant monitoring in water quality and soil.

Nanostructure	Sensing Mechanism	MnO2 Morphology	Substrate	Analyte	Linear Detection Range	Minimum Detection Limit	Reference
0D	electrochemical sensing	nanoparticle	vancomycin@ MnO2 nanozyme	staphylococcus aureus		10 cfu/mL	[27]
	electrochemical sensing	nanoparticle	PANI@ MnO2	nitrite	CV: 0.1–10 mM CA: 19.98–732.17 μM	CV: 4.38 μM CA: 1.08 μM	[29]
	colorimetric sensing	nanoparticle	CuO@MnO2 nanozyme	2-mercaptobenzothiazole	0.1–15 μM	5.9 nM	[26]
	colorimetric sensing	nanoparticle	crystalline MnO2	Se4+	10–600 μg/L	1.8 μg/L	[69]
	colorimetric sensing	nanoparticle	conjugated polymer nanoparticles@ MnO2	organo-phosphorus pesticides		0.025 ng/mL	[70]
1D	electrochemical sensing	nanorod	Cu-MOF/ rGO/CuO@ α-MnO2	resorcinol	0.2–22 μM	0.2 μM	[71]
	colorimetric sensing	nanorod	PANI@ MnO2 nanozyme	organo-phosphorus pesticides	0.50–50 μM	0.39 μM	[72]
2D	PEC sensing	nanosheet	Ag2S quantum dot@MnO2	Cr6+	100 pM–30 μM	6.46 pM	[40]
	colorimetric sensing	nanosheet	carbon dot@MnO2	Hg2+	0.01–0.1 μM	0.01 μM	[33]
	colorimetric sensing	nanosheet	ultrathin MnO2 nanomembrane	tetracycline antibiotics	2–225 μM	0.4 μM	[73]
	fluorescence sensing	nanosheet	branched poly(ethyleneimine) carbon quantum dots@MnO2 nanosheet	malachite green	0.2–14 μM	0.06 μM	[74]
	fluorescence sensing	nanosheet	TPB-DMTP@ S-CDs/MnO2	organo-phosphorus pesticides	1 × 10−6–12 μg/mL	7.9 × 10−7 μg/mL	[75]
3D	fluorescence sensing	octahedron	UiO-66-NH2/ MnO2	organo-phosphorus pesticides	1.0 × 10−7–10 mg/L	8.9 × 10−8 mg/L	[76]
	QCM sensing	sea urchin	GCN@ MnO2	chloramphenicol		11 μM	[18]

, mainly including the sensing mechanism, the morphology of MnO₂, the composition of the substrate and analyte, the linear detection range and the minimum detection limit of the analyte, along with the corresponding reference.

MnO₂ Nanostructure-Based Electrochemical Sensing

For detecting dangerous pathogens, traditional methods such as biological culture colony counting are time-consuming and labor-intensive. Electrochemical sensing has garnered significant attention in pathogen detection due to its simplicity, cost-effectiveness, sensitivity, ease of miniaturization and resistance to interference.

Ma et al. constructed a multifunctional and low-cost detection platform for detecting Staphylococcus aureus based on a composite material [27] (Figure 6a). Vancomycin (Van) was first coupled to bovine serum albumin (BSA) via a bacterial imprinting polymer (BIP), which was then loaded onto MnO₂, and the Van@BSA-MnO₂ composite was synthesized. Upon binding with captured Staphylococcus aureus, the reaction between the substrate and target was catalyzed by the Van@BSA-MnO₂ complex, generating an electrical signal that is proportional to the concentration of the bacterial target. The Van@BSA-MnO₂-based sensor exhibits remarkable sensitivity, being capable of detecting a single bacterial cell in phosphate-buffered saline. The sensor exhibits outstanding selectivity, particularly in distinguishing target Staphylococcus aureus from interfering bacteria of the same genus at a concentration of up to 100 times higher. Additionally, to assess the applicability of the sensor in intricate food matrices, experiments were performed using milk as a representative example. To reduce the matrix’s interference, milk was diluted 10-fold and spiked with varying concentrations of S. aureus. The recovered S. aureus cells were then analyzed. Of particular significance is the assay’s sensitivity, as evidenced in its ability to detect S. aureus concentrations as low as 10 CFU/mL.

MnO₂ Nanostructure-Based Colorimetric Sensing

Due to its advantages of rapid response, direct visual observation and ease of operation, colorimetry is commonly regarded as a widely applicable environmental detection method.

It is crucial for designing high-performance colorimetric sensors through preparing advanced functional nanostructures with high specificity and catalytic performance, such as synergistically coupling other sensing substrates with MnO₂ to further optimize catalytic activity. Yang et al. synthesized a composite material of PANI loaded with MnO₂ nanoparticles (PANI-MnO₂) via one-step self-assembly redox polymerization under room temperature to construct a smartphone-based colorimetric sensing platform for detecting organophosphorus pesticides (OPs) [72] (Figure 6b). This composite consists of rod-like PANI as the substrate and MnO₂ nanoparticles uniformly distributed on its surface, forming a core–shell heterojunction with a mesoporous structure. The oxidase activity of PANI-MnO₂ is inhibited by catalyzing the conversion of ascorbic acid-2-phosphate (AAP) to ascorbic acid (AA) via alkaline phosphatase (ALP). Under the same conditions, the relative catalytic activity is in the order of PANI-MnO₂ > MnO₂ nanosheets > MnO₂. The redox peak currents of PANI-MnO₂/GCE were higher than MnO₂ nanosheets/GCE and MnO₂/GCE, demonstrating the better conductivity and cycling stability of PANI-MnO₂/GCE. On the other hand, the oxidation capacity for 3,3′,5,5′-tetramethylbenzidine (TMB) of PANI-MnO₂ can be restored through adding OPs, which inhibits the activity of ALP, thereby achieving a linear detection range of 0.50–50 μM with a detection limit as low as 0.39 μM. This unique core–shell architecture enables the material to exhibit a 28.7% higher catalytic efficiency than pure MnO₂ at pH 4.0. Furthermore, to explore the practical usability of the proposed colorimetric method, we determined the glyphosate concentration in tap water, lake water, soil, pear, soybean and cucumber. Among them, OPs were detected in the soybean sample but were not detected in the other samples. The recovery rate was in the range of 98.4–108.6%, and the relative standard deviation (RSD) was within the range of 0.6–3.7%. All these results indicate the feasibility of the proposed method for detecting glyphosate in practical samples. The underlying sensing mechanism involves color development through oxidizing TMB by reactive O species generated from dissolved O₂ oxidizing catalyzed by MnO₂. The introduction of PANI not only enriches TMB molecules via electrostatic adsorption but also accelerates electron transfer through an extended π-conjugated system. Concurrently, the specific surface area is increased, and the active site is exposed due to the specific mesoporous structure of PANI-MnO₂.

MnO₂ Nanostructure-Based Fluorescent Sensing

There exist a variety of advantages of fluorescence sensing, such as high sensitivity, broad applicability, controllable cost and rapid analysis. Furthermore, ratio fluorescence detection technology, which measures the ratio of fluorescence with two different wavelengths, can effectively mitigate the impact of the fluorescence background and environmental factor, further enhancing sensing interference resistance.

Li et al. synthesized a water-soluble fluorescent conjugated polymer nanoparticle (WSCPNs)@MnO₂ (WSCPNs@MnO₂) probe using WSCPNs via nanoprecipitation for the highly sensitive and visual fluorescence-based detection of OPs [70] (Figure 6c). This sensing material consists of poly(9,9-di-n-octylfluorene-2,7-diyl) (PFO) as the fluorescent core and polystyrene-maleic anhydride (PSMA) as the amphiphilic shell. MnO₂ was anchored onto the surface of WSCPNs via in situ synthesis, forming a spherical structure. The fluorescence emission peak of WSCPNs highly overlaps with the absorption spectrum of MnO₂, enabling efficient FRET with a fluorescence quenching efficiency as high as 99%. In the process of sensing, the conversion of acetylthiocholine (ATCh) to thiocholine (TCh) is first catalyzed by acetylcholinesterase (AChE), and then the MnO₂ is reduced to Mn²⁺ catalyzed by TCh, restoring the blue fluorescence of WSCPNs. While in the presence of OPs, the activity of AChE is inhibited, and the oxidation of o-phenylenediamine (OPD) to form 2,3-diaminophenazine (DAP) catalyzed by residual MnO₂ contributes to the emitting of yellow fluorescence. Quantitative analysis was achieved by monitoring the fluorescence intensity ratio at 440 nm and 574 nm, with a detection limit as low as 0.0139 ng/mL. And the detection limit of smartphone RGB analysis visualization is 0.025 ng/mL. The in situ synthesized compact structure not only enhances FRET efficiency but also improves the photostability and interference resistance of sensing materials. The practicality of this method was evaluated by spiking paraoxon into leaves and soil. The samples, spiked with paraoxon standards at concentrations of 100 ng/mL, 200 ng/mL, and 300 ng/mL, were measured using the ratiometric fluorescence mode, the fluorescence visualization mode, and the gas chromatography–mass spectrometry (GC-MS) method as the standard. The fluorescence visualization revealed that the recovery values ranged from 96.7 to 99.9% for tree leaves and 97.6–101.4% for soil. Using ratiometric fluorescence, the recovery values were in the range of 97.6–100.7% for tree leaves and 95.7–103.1% for soil. These results confirm the accuracy and reliability of the ratiometric fluorescence probe for detecting paraoxon in real samples. This compact structure also minimizes non-specific adsorption through uniform coverage, significantly improving the probe’s sensitivity and stability. The paper provides a low-cost, highly sensitive dual-mode sensing platform for environmental monitoring.

5.2. Food-Safety Monitoring

Common food contaminants include nitrite, dopamine, antioxidants, Salmonella enterica, aflatoxin, ochratoxin A, trithion, dithiocarbamate and dichlorvo. MnO₂ nanostructure-based novel sensing technologies for food-safety monitoring include electrochemical methods, colorimetric methods, fluorescence methods, photothermal methods, lateral flow immunoassays and microfluidic sensing.

Applications of MnO₂ nanostructure-based novel sensing technology in food-safety monitoring over the past five years have been summarized in

Table 3. MnO₂ nanostructure-based novel sensing for food-safety monitoring.

Nanostructure	Sensing Mechanism	MnO2 Morphology	Substrate	Analyte	Linear Detection Range	Minimum Detection Limit	Reference
0D	electrochemical sensing	nanoparticle	ZnO@ MnO2-rGO	antioxidant molecules	hydroquinone: 0.008–10 µM and 10–350 µM monotert-butyl hydroquinone: 0.008–10 µM and 10–320 µM	hydroquinone: 0.0011 µM monotert-butyl hydroquinone: 0.0012 µM	[77]
	fluorescence sensing	nanoparticle	upconversion nanoparticles@ MnO2	pseu- dopurine	0.5–50 mg/L	0.14 mg/L	[78]
	fluorescence sensing	nanoparticle	carbon quantum dots@MnO2 nanoparticles	nitrite			[79]
	colorimetric sensing	nanoparticle	PDA/MnO2 nanoenzyme	dithio- carbamate		0.63 ng/mL	[80]
1D	electrochemical sensing	nanotube	MnO2 nanotubes@ layered double hydroxide nanocages	H2O2	1 × 10–3–4 mmol/L 4–8 mmol/L	0.26 µmol/L	[81]
2D	electrochemical sensing	nanosheet	graphene@ MnO2	organo-phosphorus pesticide	3–4000 ng/mL	1.2 ng/mL	[82]
	electrochemical sensing	nanosheet	MnO2 nanoenzyme	organo-phosphorus pesticide		0.025 ng/mL	[83]
	colorimetric sensing	nanosheet	MnO2 nanoenzyme	shigella flexneri	103–107 cfu/mL	103 cfu/mL	[84]
	colorimetric sensing	nanosheet	zein/MnO2 composite	H2O2 and lactic acid		H2O2: 7.2 × 10−4 mol/L lactic acid: 7.5 × 10−4 mol/L	[85]
	fluorescence sensing	nanosheet	okra carbon nanoparticles@ MnO2 nanosheets	DDVP	4–120 μg/L	1.2 μg/L	[86]
	fluorescence sensing	nanosheet	Au nanoclusters@ MnO2 nanosheets	methyl parathion	0.005–200 ng/mL	3.1 pg/mL	[87]
	fluorescence sensing	nanosheet	Ru@UiO-66@ MnO2 nanosheets/ thiamine	DDVP and chlorpyrifos		DDVP: 9.99 × 10−6 μg/mL chlorpyrifos: 9.99 × 10−5 μg/mL	[88]
3D	colorimetric sensing	nanoflower	MnO2 nanoenzyme	aflatoxin M1	6–160 ng/L	2.1 ng/L	[89]
	fluorescence sensing	nanoflower	MnO2 nanoflower	ochratoxin A	0.05–33.35 ng/mL	0.069 ng/mL	[90]

, including the sensing mechanism, the morphology of MnO₂, the chemical composition of the substrate and analyte, the linear detection range and the minimum detection limit of the analyte, and the corresponding reference.

5.2.1. MnO₂ Nanostructure-Based Electrochemical Sensing

The MnO₂ nanostructure and its composites offer exceptionally prominent advantages for their application in novel electrochemical sensor-based food-safety monitoring.

H₂O₂ is commonly added to food as a disinfectant or bleaching agent during processing, but excessive H₂O₂ residuals will seriously affect human health. Xiong et al. synthesized a hierarchical porous gourd-like MnO₂/ZIF-67@Ni-Co layered double hydroxide composite (MnO₂/ZIF-67@LDH) via a multi-step hydrothermal strategy, serving as the substrate for electrochemistry-based H₂O₂ detection, with advantages in rapidity, sensitiveness and accuracy [81] (Figure 7a). The synthesis strategy for MnO₂/ZIF-67@LDH was as follows: First, a MnO₂ nanotube was prepared via a one-step hydrothermal approach based on the reduction of KMnO₄ using concentrated HCl. Subsequently, a ZIF-67 nanopolyhedron was assembled onto the MnO₂ nanotube through a secondary hydrothermal process to form MnO₂/ZIF-67. Finally, Ni-Co layered double hydroxide nanocages were deposited onto MnO₂/ZIF-67 via a third hydrothermal step. The formation process of the multistage structure in the MnO₂/ZIF-67@LDH composite was as follows: the formation of a MnO₂ hollow nanotube; the formation of a 3D nano-polyhedral structure by assembling the ZIF-67 polyhedron on the surface of the MnO₂ nanotube; the transformation of a solid dodecahedral structure to a hollow dodecahedral structure via solvothermal etching of the ZIF-67 template; and the formation of a 3D hollow nanocage structure by stacking Ni-Co layered double hydroxide onto a hollow polyhedral framework of ZIF-67. The catalyst materials could be arranged in increasing order of current density as follows: ZIF-67@LDH < MnO₂ < MnO₂/ZIF-67@LDH. Accordingly, the MnO₂/ZIF-67@LDH composite electrode has the highest activity. The designed electrochemical sensor for H₂O₂ detection exhibits two linear detection ranges, namely 1 × 10⁻³–4 mmol/L and 4–8 mmol/L. Sensitivity is as high as 693 µA·L/mmol·cm², and the sensor has an extremely rapid response of t < 3 s and excellent reproducibility and stability. The hollow structure of the MnO₂/ZIF-67@LDH composite provides multiple pathways and abundant cavities, facilitating electron transfer and H₂O₂ transport. Simultaneously, the hollow architecture of the composite offers sufficient residual space to accommodate volume and dimensional changes induced by multiple electrochemical reactions, ensuring the repeatability and stability of the sensor. Furthermore, the interconnected LDH nanosheet provides abundant active sites and a large specific surface area, enhancing sensing efficiency. The feasibility of using the MnO₂/ZIF-67@LDH electrode for the detection of H₂O₂ was evaluated in real samples of milk and juice using the standard addition method. Under optimal conditions, 20 µL of a milk supernatant was added to a 0.1 M of a NaOH solution containing H₂O₂. The recoveries and the RSDs range from 95.2% to 114.2% and 1.47–3.16%, respectively. These results demonstrate the effectiveness and feasibility of the proposed sensor in the detection of H₂O₂ in food.

5.2.2. MnO₂ Nanostructure-Based Colorimetric Sensing

The key for applying MnO₂ in colorimetric sensing primarily stems from its unique enzyme-like catalytic activity. Therefore, applying the MnO₂ nanostructure and its composites in novel colorimetric sensors warrants further research.

Shigella enteritis in pathogenic bacteria is one of the primary risk factors for causing disease infection, which typically transmits to humans through the consumption of contaminated animal-derived food. Therefore, sensitive and rapid detection of Shigella enteritis is of significant importance to global public health security. Zhang et al. employed 2D MnO₂ nanosheets (MnO₂ NSs) with advantages of strong bacterial adsorption capacity, excellent optical signals and good catalytic activity as the substrate to achieve rapid, sensitive and accurate detection of Salmonella enterica via leveraging their oxidase-like catalytic activity to amplify colorimetric signals [84] (Figure 7b). The MnO₂ NSs were synthesized using a one-step precipitation strategy based on the reduction of KMnO₄ by 2-morpholinoethane sulfonic acid. X-ray diffraction (XRD) and TEM revealed that the synthesized γ-MnO₂ exhibits a 2D sheet structure with an average size of approximately 250 nm. As there are different morphologies of MnO₂ besides MnO₂ NSs, MnO₂ NWs were also synthesized as controls to demonstrate the superiority of MnO₂ NSs. A performance comparison of these two morphologies of MnO₂ shows that MnO₂ NSs have a slight advantage. A colorimetric sensor based on MnO₂ NSs demonstrated a linear range of 10³–10⁷ CFU/mL and a detection limit of 10³ CFU/mL for Shigella enteritis. MnO₂ NSs perform the following functions simultaneously during colorimetric sensing: capturing bacteria as antibody mimics; providing strong colorimetric signals utilizing their brown color; and generating additional catalytic signals by amplifying oxidation activity of mimicking enzymes via immersion. The detection mechanism of MnO₂ NS-based colorimetric sensing for Salmonella enterica is as follows: the formation of a substrate–analyte complex through absorbing MnO₂ NSs onto the surface of the target bacteria; the formation of a brown band via special binding between the anti-Salmonella enteritidis monoclonal antibody and detection target when the substrate–analyte complex migrates to the T line, where the concentration of the bacterial target is positively linearly correlated with the color intensity of the T line; and the formation of a blue band that enhances detection sensitivity due to the pseudo-oxidase activity of MnO₂ NSs, which oxidizes TMB into ox-TMB. The unique 2D sheet structure of MnO₂ NSs facilitates adsorption of the analyte through electrostatic and hydrophobic interaction. In order to demonstrate the practical application of the developed MnO₂ NSs, several actual samples were selected for testing, and a series of different concentrations of bacterial suspension were added to these food samples. As the concentration of the bacteria decreases, the intensity of the brown band of the T-line gradually becomes weaker, until it stops at an LOD concentration of 104, 104, 104 and 105 cfu·mL⁻¹ for orange juice, drinking water, milk, and beef, respectively. After TMB catalytic coloration, the LOD becomes 103, 103, 103, and 104 cfu·mL⁻¹, respectively, for the above samples, which is decreased by 10 times. The average recovery of S. enteritidis is between 81.83% and 110.56%, and the RSD values are all less than 10%, indicating that the newly created immunochromatography technique has a lot of potential to be employed with actual samples.

5.2.3. MnO₂ Nanostructure-Based Fluorescent Sensing

The MnO₂ nanostructure exhibits high fluorescence quenching efficiency. Consequently, it is necessary to apply the MnO₂ nanostructure and its composites in novel fluorescent sensors.

One potential indicator for evaluating the early post-slaughter freshness of aquatic products is the hypoxanthine concentration in them. Xu et al. synthesized a MnO₂ in situ-coated upconversion nanoparticle (UCNP) composite (MnO₂@UCNPs) via an improved sacrifice template method based on reducing KMnO₄ by carbon. Utilizing the composite as the substrate, the highly efficient and sensitive hypoxanthine sensing platform was constructed based on hypoxanthine oxidation-induced MnO₂ reduction and fluorescence turn-on [78] (Figure 7c). The synthesis of MnO₂@UCNPs proceeded as follows: First, carbon-coated UCNPs (UCNPs@C) were synthesized via a one-step hydrothermal method. Subsequently, the MnO₂@UCNP composite was yielded by in-situ reducing of a KMnO₄ solution, utilizing UCNPs@C as the reducing agent and structure-directing agent concurrently. In the presence of a p-cresol oxidant and within a p-cresol concentration of 0.5–50 mg/L, the fluorescence intensity of the MnO₂@UCNP composite was positively correlated with p-cresol concentration. The detection limit of the platform for p-cresol sensing was 0.14 mg/L. Specifically, the detection mechanism of hypoxanthine by MnO₂@UCNP-based fluorescence platform is as follows: Under 980 nm laser excitation, UCNPs exhibit intense green and red peaks at approximately 550 nm and 665 nm, respectively. After encapsulating the MnO₂ shell, the fluorescence intensity of the MnO₂@UCNP composite becomes significantly weakened. The oxidation of hypoxanthine catalyzed by hypoxanthine oxidase yields reducing agents, including H₂O₂ and uric acid, which further reduce the MnO₂ coating to Mn²⁺. The MnO₂@UCNP composite progressively recovers its original fluorescence intensity with the gradual disappearance of the MnO₂ shell. The designed fluorescent sensing platform demonstrates broad applications in the quality control of aquatic products containing hypoxanthine. Moreover, two kinds of fish (large yellow croaker and olive flounder), shrimp (Trachypenaeus curvirostris), and shellfish (scallops) samples were chosen as practical seafood samples. The hypoxanthine concentrations of the samples at 4°C in three post-mortem periods were obtained by the proposed nanosystem and the high performance liquid chromatography (HPLC) method. There exists no statistical difference between HPLC and the proposed method for determining the concentration of hypoxanthine in actual seafood samples (p > 0.05), suggesting that the proposed nanosystem could realize the accurate detection of hypoxanthine in various real seafoods with high efficiency and relatively low cost.

5.3. Biomedical Monitoring

Common biomolecules related to life safety include H₂O₂, glucose, Staphylococcus aureus, alkaline phosphatase, ascorbic acid, cysteine, uric acid, glutathione, acetaminophen and cancer biomarkers. Electrochemical sensing, electrochemiluminescence sensing, PEC sensing, colorimetric sensing, fluorescence sensing, photothermal sensing, microfluidic sensing and microcantilever sensing based on MnO₂ nanostructures can all be employed for biomedical monitoring.

Applications of the MnO₂ nanostructure and its composites in biomedical monitoring over the past five years have been summarized in

Table 4. MnO₂ nanostructure-based novel sensing for biomedical monitoring.

Nanostructure	Sensing Mechanism	MnO2 Morphology	Substrate	Analyte	Linear Detection Range	Minimum Detection Limit	Reference
0D	fluorescence sensing	nanoparticle	Fe3O4@ Polydopamine @MnO2	disease marker alkaline phosphatase	0.05–40 U/L	0.045 U/L	[91]
	photothermal sensing	nanoparticle	Au@MnO2 core-shell structure	disease marker alkaline phosphatase	2.0–50 U/L	0.75 U/L	[17]
1D	electrochemical sensing	nanowire	phage templated MnO2 nanowires	glucose in blood sugar	5 μM–2 mM	1.8 μM	[38]
	electrochemical sensing	nanorod	Pd-α-MnO2@ graphene	neuro- transmitter dopamine, hepatotoxic and nephrotoxic agent, aceta- minophen	dopamine: 0.2–425 µM acetaminophen: 0.1–375 µM	dopamine: 0.0591 µM acetaminophen: 0.0854 µM	[92]
	electrochemical sensing	nanorod	rGO@MOFs@ α-MnO2	acetylcholine	0.1 µM–3 mM	5 nM	[35]
	fluorescence sensing	nanorod	MnO2 nanoenzyme	SARS-CoV-2			[93]
2D	electrochemical sensing	nanosheet	MnO2 nanoenzyme	Alzheimer’s disease biomarker β-amyloid-like protein	0.01–500 nM	0.44 pmol/L	[94]
	EL sensing	nanosheet	MnO2@ polydopamine	immune biological marker α-fetoprotein	0.01 pg/mL–5 ng/mL	10.7 fg/mL	[95]
	PEC sensing	nanosheet	MnO2@ Co3O4/Ti	superoxide anion in mitochondria	0.1–50,000 nM	0.025 nM	[41]
	colorimetric sensing	nanosheet	carbon quantum dots@MnO2 nanosheets	oxalate in urine	1–50 μM	690 nM	[96]
	colorimetric sensing	nanosheet	upconversion nanoparticles @MnO2	glutathione in cells		0.9 μM	[97]
	fluorescence sensing	nanosheet	dopamine accumulation @MnO2	serum disease marker alkaline phosphatase	1–80 U/L	0.34 U/L	[98]
	fluorescence sensing	nanosheet	Au nanoclusters @MnO2 nanosheets	cardiac marker troponin T	0–16 ng/mL	0.037 ng/mL	[99]
	photothermal sensing	nanosheet	Au nanorods @MnO2 nanosheets	melanoma biomarker tyrosinase	1.0–70 U/mL	0.34 U/mL	[100]
	QCM sensing	nanosheet	polypyrrole/ MnO2@ cellulose	G-series nerve agent methyl phosphonic acid dimethyl ester			[101]
3D	electrochemical sensing	nanoflower	Co-MnO2@ rGO-CNT	cancer biomarker H2O2	0.2 μM–18.0 mM	66.7 nM	[102]
	PEC sensing	nanomotor	MnO2-carbon nanobottle	microRNA	100 fM–100 nM	100 fM	[103]
	colorimetric sensing	nanoball	Ru-MnO2 nanoenzyme	cancer biomarker H2S and cystathionine-β-lyase	H2S: 0.1–70 μM cystathionine γ-lyase: 1–110 µM	H2S: 0.098 μM cystathionine γ-lyase: 0.95 µM	[104]
	colorimetric sensing	hollow cube	H-MnO2 nanoenzyme	liver function biomarkers		aspartate aminotransferase: 4.9 U/L alanine aminotransferase: 3.6 U/L alkaline phosphatase: 0.99 U/L	[56]
	colorimetric sensing	nanoflower	carbon quantum dots@MnO2 nanoblossoms	glutathione in cells	2.0–200 μM	0.558 μM	[105]
	fluorescence sensing	nanoflower	CaF2@MnO2	serum uric acid	0.1–30 μM	0.039 μM	[106]

, specifically covering the sensing mechanism, the structure and morphology of MnO₂, the chemical composition of the substrate and analyte, the linear detection range and the minimum detection limit of analyte, and the corresponding reference.

5.3.1. MnO₂ Nanostructure-Based Electrochemical Sensing

MnO₂ demonstrates exceptionally prominent advantages in novel electrochemical sensing, because it can reduce the overpotential of electrochemical reactions and enhance the current response of the system as an excellent redox couple.

MnO₂/G was first synthesized via a one-step hydrothermal process, and Pd was subsequently doped onto the surface of MnO₂/G through a two-step hydrothermal process, simultaneously filling cation vacancy and generating significant surface defects [92] (Figure 8a). The synthesized Pd@α-MnO₂/G was applied to electrochemical-based detecting of biomolecule dopamine (DA) and paracetamol (PA). The electrochemically active surface area of Pd@α-MnO₂/G was approximately two-times higher than that of α-MnO₂/G, which proved that the Pd led to remarkable electrochemical performance. Meanwhile, the semicircle diameters of the electrodes followed the order of bare GCE (287.9 Ω) > reduced graphene oxide (211.2 Ω) > α-MnO₂/G (136.2 Ω) > Pd@α-MnO₂/G (86.7 Ω). Thus, Pd@α-MnO₂/G exhibited the lowest value, confirming its faster electron transfer ability and better conductivity during the electrochemical reaction. It demonstrated a broad linear detection range for DA of 0.2–425 µM and a PA of 0.1–375 µM, an extremely low detection limit of 0.086 µM for DA and 0.059 µM for PA respectively, a high sensitivity of 0.0591 µA/µM and 0.0854 µA/µM for DA and PA, as well as excellent signal stability. Further analysis revealed that the abundant defects on Pd@α-MnO₂/G significantly alter the electronic and crystalline structure of the prepared composite material, which further endow Pd@α-MnO₂/G with a higher electroactive surface area, faster electron transfer capability and greater structural stability, thereby conferring outstanding electrochemical sensing performance. Moreover, the practicability of Pd@α-MnO₂/G was verified by determining DA and PA in drinking water and human urine samples. The RSD values were 1.1% for DA and 1.8% for PA in drinking water, and 1.6% for DA and 2.5% for PA in urine. The recovery range of DA and PA in real samples is 97.24–102.8, which indicates the potential of the modified electrode for real-time applications.

5.3.2. MnO₂ Nanostructure-Based Colorimetric Sensing

The MnO₂ nanostructure exhibits unique enzyme-like catalytic activity, and it is worth it to apply the MnO₂ nanostructure and its composites in novel colorimetric sensors.

Huang et al. constructed a MnO₂ nanozyme-mediated CRISPR-Cas12a-based colorimetric sensing system for SARS-CoV-2 detection [93] (Figure 8b). The MnO₂ nanozyme was designed as follows: there exists a biotin group at one end and a carboxyl group at the other in single-stranded DNA (ssDNA); the biotin end strongly binds to streptavidin-coated magnetic beads (MBs), while the carboxyl end attaches to MnO₂ nanorods, forming a MnO₂ nanozyme (ssDNA-MnO₂-MBs). The colorimetric sensing platform exhibited a good positive linear correlation between SARS-CoV-2 activity and solution color change within its concentration of 1 ng/L⁻¹ μg/L and with a detection limit of 0.32 ng/L for SARS-CoV-2. The detection mechanism of the designed colorimetric sensing system for SARS-CoV-2 is as follows: CRISPR-Cas12a is activated upon adding SARS-CoV-2 target nucleic acid, initiating cleavage of the ssDNA linker and releasing MnO₂ nanorods and MB units. Then, the TMB can be oxidized and catalyzed by MnO₂ nanorods, causing a transformation of the solution color from pale yellow to blue. Moreover, SARS-E, hCov-HKU1-E and MERS-uPE-E pseudoviruses were constructed to investigate the specificity of the system. The three above-maintained pseudoviruses and SARS-CoV-2-E pseudovirus with a concentration of 100 copies/μL were analyzed using the MnO₂ nanozyme-mediated CRISPRCas12a system. Only the SARS-CoV-2 pseudovirus caused a color change and was detected, while none of the other three pseudoviruses showed a noticeable signal change, indicating excellent selectivity. The designed colorimetric sensing system is characterized by simple operations and low cost, demonstrating its significant potential for molecular diagnostics in point-of-care testing.

5.3.3. MnO₂ Nanostructure-Based Fluorescent Sensing

It is worth it to apply the MnO₂ nanostructure and its composites in novel fluorescent sensor-based biomedical monitoring.

The reducing substance plays a crucial role in various physiological processes and antioxidant defense in living organisms. Nevertheless, accurately detecting and distinguishing reducing substances remains a significant challenge due to potential interference in complex samples. Meiling Liu et al. developed a nanoplatform that consists of a MnO₂ nanozyme/fluorescent polydopamine nanoparticle (MnO₂/FPDA) for H₂O₂ detection, which features dual-channel output signals including ratio-fluorescence and UV absorption [107] (Figure 8c). Notably, the platform exhibited a detection limit of 3.18 μM and 10.67 μM in a H₂O₂ concentration range of 5–500 μM and 15–500 μM, utilizing the fluorescence and colorimetric channel respectively. Given the comparatively higher concentration of H₂O₂ in milk and cancer cells compared to other substances, the developed platform proves to be effective in quantifying H₂O₂ in such samples. To assess the practical utility of the platform, the standard addition method was employed, revealing a recovery rate of 94.6 to 102.7% with a relative standard deviation below 4.3%. These outcomes affirm the suitability of the method for H₂O₂ analysis in complex practical samples. The detecting mechanism of the MnO₂/FPDA-based dual-platform sensor is as follows: The non-fluorescent reducing agent OPD can be oxidized to DAP with distinct yellow fluorescence catalyzed by MnO₂ with oxidase-like activity. The fluorescence originating from FPDA can be quenched in the system containing FPDA as a fluorescent emitter and the MnO₂ as a fluorescent acceptor. MnO₂ can be converted to Mn²⁺ under the impact of a reducing agent, leading to the fluorescence recovery of FPDA, the fluorescence reduction of DAP, and a simultaneous shift in the UV absorption peak of DAP. The research provides crucial insights into developing a sensing platform capable of selectively detecting specific reducing species in real samples or deploying sensor arrays to distinguish multiple components within real samples. These advances pave the way for enhancing the analysis and screening of reductive substances in cellular and food samples.

MnO₂ is a strong oxidant and can react non-specifically with various reducing agents in real samples, such as blood or food. Hence, there exists selectivity challenges of MnO₂ sensing in complex matrices. In real samples, the interference challenges of MnO₂ sensing mainly come from reducing small molecules in blood or urine, matrix coexisting substances in blood, and structural analogues of MnO₂. Among them, reducing small molecules include ascorbic acid, dopamine, uric acid in blood and urine, and glutathione in living organisms, and matrix coexisting substances include proteins, fats, cells, etc., in the blood, as well as other small molecules and anions and cations in the sample. The following strategies can effectively improve the selectivity of MnO₂ sensing in real samples: introduce highly specific biological enzymes or “probes” with high recognition ability into MnO₂, specifically recognizing the target-like “keys and locks”, and element doping and defect engineering can alter the electronic structure and introduce oxygen vacancies in MnO₂, thereby changing its adsorption and catalytic selectivity.

6. Conclusions and Outlook

In summary, with the application of the MnO₂ nanostructure in environmental monitoring, food-safety monitoring and biomedical monitoring as the core, the sensing advantages and synthesis strategy of 0D, 1D, 2D and 3D MnO₂ have been detailed, illustrated first with examples based on distinct dimensions. Secondly, the detecting advantages and detection mechanisms of MnO₂ nanostructure-based novel sensing technologies have been systematically summarized. Moreover, applications of the MnO₂ nanostructure across environmental monitoring, food-safety monitoring and biomedical monitoring using diverse sensing technologies have been summarized comprehensively. Undoubtedly, the development and widespread adoption of a novel sensor based on the MnO₂ nanostructure have laid a solid foundation for a sensitive, reliable, rapid, simple and low-cost analysis of environmental pollutants, food additives and biomarkers.

With the rapid advancement of materials science, nanoscale synthesis and artificial intelligence, novel sensors based on the MnO₂ nanostructure are poised to achieve broader and deeper research and applications in public health and ecological balance. Looking ahead, key research directions and development trends for novel sensors based on the MnO₂ nanostructure can be summarized as follows:

6.1. Modification of MnO₂ Nanostructure

Sensing performance can be further enhanced by surface modifications of MnO₂, the surface coating of MnO₂, compositing MnO₂ with other materials, and the design of the morphology and microstructure of MnO₂. Surface modifications of the MnO₂ nanostructure with molecular imprinting or biorecognition elements, including enzymes, antibodies and aptamers, are beneficial for improving the specificity and selectivity of sensors. Coating the MnO₂ nanostructure with inert shells, including silica and alumina, enhances the structural stability of the substrate and improves the performance consistency of the sensor. Fixing the MnO₂ nanostructure onto flexible substrates improves mechanical flexibility and enhances the potential application for flexible sensing of MnO₂. Compositing the MnO₂ nanostructure with carbon materials, including carbon quantum dots and graphenes; conductive polymers, including PEDOT and PSS; as well as noble metals, including Au, Ag and Pt, significantly boosts substrate conductivity, further improving sensor efficiency and response times. Developing the MnO₂ nanostructure with an ultrathin layered or a mesoporous structure increases the specific surface area and active site of the substrate, further improving the response time and selectivity of sensors.

6.2. Industrial Production of MnO₂ Nanomaterial

In the field of MnO₂ nanostructure-based novel sensing, the research focus should shift from the pursuit of novel morphology and extreme performance at the laboratory level to the establishment of industrial production processes with a controllable process, strong repeatability and appropriate cost. The synthesis methods of MnO₂ include hydrothermal/solvothermal methods, chemical precipitation/redox methods, electrochemical precipitation methods, liquid phase stripping methods, template methods, biosynthesis methods, spray pyrolysis methods, etc., and the hydrothermal method is relatively simple. At present, the hydrothermal synthesis of MnO₂ is usually carried out in the intermittent high-pressure reactor, while its uneven mass and heat transfer leads to large differences among batch products, and amplification production directly becomes difficult. In the future, a continuous flow microreactor can be utilized as a candidate for the industrial production of MnO₂ via a hydrothermal strategy. This can not only realize the uniform nucleation and growth of MnO₂ by accurately controlling the reaction parameters in the microchannel but can also combine with an online monitoring system to ensure the consistency of cross-batch product quality.

6.3. Development of Multimodal Sensors

MnO₂ nanostructure-based multimodal sensors leverage diverse physicochemical properties of MnO₂, including electrochemical activity, optical activity and redox properties, to simultaneously output multiple signal modes, including electrical signals, optical signals and thermal signals. The multimodal sensing platform offers significant advantages, including high reliability, a wide dynamic range and adaptability based on synergistic detection mechanisms. Specifically, a multi-signal output effectively reduces false alarms caused by a single-signal output, thereby enhancing sensor reliability. Since different signal types exhibit varying sensitivity to analyte concentrations, a multimodal sensor typically offers a broader linear range for analyte detection. Additionally, distinct types of signals may preferentially interact with different analytes, making a multimodal sensor suitable for analyzing complex real-world samples, such as blood, wastewater and food. Typical MnO₂ nanostructure-based multimodal sensors include electrochemical–colorimetric dual-mode sensors, electrochemical–photothermal dual-mode sensors and electrochemical–mechanical dual-mode sensors. Taking the electrochemical–colorimetric bimodal sensor as an example, when detecting H₂O₂ or glucose, the electrochemical signal can be generated through a redox reaction between the substrate and analyte on the one hand, and the solution becomes transparent via the reduction of Mn⁴⁺ to Mn²⁺ on the other hand. Consequently, the sensor can provide not only quantitative concentration data via electrochemical sensing but also semi-quantitative concentration data through colorimetric sensing.

6.4. Development of Intelligent Sensors

The intellectualization of a MnO₂ nanostructure-based sensor involves endowing the sensor with automated functions, including adaptability, self-diagnosis and data interaction through various strategies, including intelligent signal processing and algorithms, as well as IoT integration. Specifically, detection signal processing in real time, noise filtering and conducting baseline calibration can be realized by integrating a microprocessor within the MnO₂ nanostructure-based sensor. Moreover, the signal-to-noise ratio can be reduced and the detection accuracy can be improved by machine learning-assisted detection signal analysis. The uploading of sensing data to the cloud, and real-time transmission and remote monitoring of detection data can be conducted through a Bluetooth module. Intelligent sensors find widespread applications in healthcare, environmental monitoring and industrial safety surveillance. For instance, real-time wound infection monitoring and diabetes management can be conducted through analyzing H₂O₂ and glucose by MnO₂ nanostructure-based intelligent sensors. Specifically, the detecting signal intensity and glucose concentration in MnO₂-based sensing exhibits a non-linear relationship, especially at low and high concentrations. It is inaccurate to utilize a traditional linear regression strategy to fit the correlation between detecting signal intensity and glucose concentration. Conversely, machine learning can perform non-linear regression modeling on calibration data, which can significantly improve the prediction accuracy of glucose concentrations. Abreu et al. reported a MnO₂-based enzyme-modified screen-printed electrode for the electrochemical detection of glucose in serum. The sensor has a linear range of 0.75–40 mM and a detection limit of 0.078 mM. On the basis of this, the researchers utilized a variety of machine learning algorithms to build a model for “glucose concentration–signal intensity”. The decision tree algorithm accurately predicts the calibration parameter and acquires a coefficient determination exceeding 0.9, and the multi-layer perceptron model effectively predicted the glucose concentration with a determination coefficient of 0.828, which fully demonstrated the reliability of machine learning-assisted electrochemical sensing for glucose detection. Machine learning technology can also be integrated with embedded systems to achieve portable, real-time diabetes detection. Researchers combined micro-machine learning with an electronic nose of metal oxide sensors for real-time detection of diabetes. The XGBoost algorithm has a detection accuracy of 95%, and the deep neural network and 1D convolutional neural network have a detection efficiency of 94.44%, which provides important technical guidance for the development of non-embedded diabetes diagnosis equipment. Future advancements are anticipated through integrating bioengineering, artificial intelligence and microelectronics to drive the development of next-generation MnO₂ nanostructure-based intelligent sensing systems.