Hydrothermally Synthesized Metal Oxide Nanostructures for H₂O₂ Sensing and Oxidative Stress Management in Plants

Abstract

Hydrogen peroxide (H₂O₂) is a key reactive oxygen species involved in both cellular signaling and oxidative stress, making its reliable detection essential in biological and environmental systems. Electrochemical sensing has emerged as a promising approach for H₂O₂ monitoring due to its high sensitivity, rapid response, and suitability for in situ analysis. This review provides a comprehensive overview of nanostructured metal oxide electrodes for non-enzymatic electrochemical detection of H₂O₂. The effects of material composition, nanostructure morphology, and synthesis strategies (particularly hydrothermal methods) on sensor performance are critically discussed. Special attention is given to our previously reported studies, enabling a consistent comparison of structure–property relationships under similar experimental conditions. Furthermore, the application of these sensors in plant stress analysis is examined, including both the monitoring of oxidative stress and the evaluation of stress mitigation strategies using metal oxide nanoparticles. The role of nanoparticles as reactive oxygen species scavengers and enhancers of plant antioxidant systems is highlighted, demonstrating their ability to reduce H₂O₂ levels and improve plant physiological status under adverse environmental conditions. Overall, this work emphasizes the dual functionality of nanostructured materials as both sensing platforms and active agents for stress mitigation, highlighting their potential in agricultural and environmental applications.

1. Introduction

Hydrogen peroxide is a key reactive oxygen species (ROS) that plays a dual role in biological and environmental systems [1,2]. At low concentrations, it functions as an important signaling molecule involved in cellular regulation, including plant growth, immune responses, and adaptation to environmental stress. However, under conditions of oxidative stress triggered by abiotic factors such as salinity, drought, and herbicide exposure, or by biotic stressors, H₂O₂ can accumulate to toxic levels [3,4]. Excessive H₂O₂ causes cellular damage through membrane lipid peroxidation, protein oxidation, and inhibition of photosynthesis, ultimately impairing plant growth and productivity [5,6]. Consequently, accurate and real-time monitoring of H₂O₂ is essential for evaluating physiological states in biological systems, particularly in agriculture and biomedical diagnostics.

Conventional analytical techniques for H₂O₂ detection, including colorimetric assays [7], spectrophotometry [8,9], and fluorescence-based methods, have been widely used but suffer from several limitations. These approaches often require complex sample preparation, expensive instrumentation, and long analysis times, while also showing limited sensitivity and selectivity in complex biological matrices. Such drawbacks restrict their practical use in dynamic environments where rapid and continuous monitoring is required. In contrast, electrochemical sensing has emerged as a powerful alternative due to its high sensitivity, rapid response, cost-effectiveness, and capability for miniaturization [10]. Electrochemical sensors enable real-time, in situ monitoring without the need for optical labeling or complex instrumentation, making them particularly suitable for portable and field-deployable analytical systems [11].

Electrochemical sensing platforms typically operate using a three-electrode configuration consisting of a working electrode, a reference electrode, and a counter electrode. Among these components, the working electrode plays the most critical role because the electrochemical reaction of the analyte occurs on its surface. Traditional working electrode materials include glassy carbon [12], platinum [13], gold [14], and carbon-based materials [15]; however, their catalytic activity toward H₂O₂ can be limited without further modification. Recent advances in nanotechnology have significantly improved electrode performance through the use of nanostructured materials. Nanostructured materials typically exhibit specific surface areas of 20–250 m²·g⁻¹, corresponding to an increase in effective surface area of approximately 300–1000% compared with smooth films. Depending on composition and morphology, nanostructuring can also decrease the electrochemical charge-transfer resistance from several hundred ohms to several tens of ohms, resulting in improved electron-transfer kinetics and enhanced electrocatalytic performance [16,17].

Hierarchical three-dimensional architectures further enhance mass transport and catalytic activity, often providing 2–10-fold higher electrochemical response currents and several-fold improvements in sensitivity due to improved electrolyte accessibility and a greater density of exposed active sites. As a result, nanostructured electrodes commonly achieve sensitivities one to two orders of magnitude higher than conventional bulk electrodes, detection limits in the nanomolar to low micromolar range, and response times of only a few seconds, making them highly attractive for electrochemical H₂O₂ sensing.

Beyond sensing applications, understanding and monitoring H₂O₂ is particularly important in plant physiology because oxidative stress represents one of the major factors limiting agricultural productivity and ultimately reduce crop yield. Addressing oxidative stress therefore requires both effective monitoring strategies and innovative mitigation approaches that can improve plant resilience under adverse environmental conditions.

Nanotechnology has recently emerged as a promising strategy for plant stress mitigation. When applied to plants, nanoparticles such as ZnO, Fe₃O₄, TiO₂, and MgO can help alleviate oxidative stress by acting as ROS scavengers, including the neutralization of excess H₂O₂ [18,19,20]. In addition, many of these nanoparticles serve as essential micronutrient sources, stimulate plant antioxidant defense systems, and enhance nutrient uptake and water-use efficiency [21]. Their nanoscale dimensions facilitate penetration into plant tissues and efficient translocation within cells, enabling targeted delivery of beneficial effects. As a result, nanoparticle-based approaches are increasingly being explored as sustainable tools for improving crop tolerance to environmental stress, representing an important direction for precision agriculture and plant health management.

This review provides an overview of hydrothermal synthesis strategies for nanostructured metal oxides used in electrochemical H₂O₂ sensing, together with recent advances in metal oxide nanoparticle-assisted mitigation of oxidative stress in plants. Unlike many existing reviews that address electrochemical H₂O₂ sensors, metal oxide nanomaterials, or nanoparticle-assisted stress mitigation as separate research topics, the present work integrates these areas within a unified framework linking oxidative stress monitoring and oxidative stress management in plants.

In contrast to conventional reviews that summarize highly heterogeneous literature data obtained under substantially different synthesis, electrochemical, and analytical conditions, the present work combines a broad analysis of independent literature sources with a focused comparative perspective based on a series of our own studies published in peer-reviewed journals. A key strength of this review is the inclusion of internally consistent datasets obtained using closely comparable synthesis procedures, electrode architectures, electrochemical measurement protocols, calibration approaches, and plant treatment strategies. This enables a more reliable comparison of structure–property relationships and partially addresses a common limitation of review articles in the field, where direct comparison of reported sensor performances is often complicated by differences in precursor chemistry, hydrothermal parameters, electrolytes, and testing methodologies. At the same time, the review maintains a broad and balanced perspective through extensive consideration of results reported by other independent research groups.

Particular emphasis is placed on correlating hydrothermal synthesis conditions, precursor selection, morphology evolution, and electrochemical characterization methods with sensor sensitivity, selectivity, and detection limits. In addition, the review extends beyond the discussion of sensor performance in model laboratory systems by examining the application of metal oxide-based electrochemical sensors for H₂O₂ determination in real plant samples and by evaluating the effectiveness of metal oxide nanoparticles for oxidative stress mitigation. By combining sensor development, plant stress diagnostics, and nanoparticle-assisted stress alleviation within a common analytical framework, this review provides a more comprehensive understanding of the role of metal oxide nanomaterials in plant oxidative stress management and offers practical guidelines for the rational design of future sensing and stress-mitigation technologies.

2. Materials and Methods

This review was prepared through a comprehensive analysis of peer-reviewed scientific literature related to hydrothermally synthesized metal oxide nanostructures for electrochemical H₂O₂ sensing and oxidative stress management in plants. Publications were collected from major international scientific databases, including Scopus and Web of Science, with emphasis placed on articles published in high-quality, peer-reviewed journals. In addition to the general literature survey, the review incorporates a representative dataset derived from the authors’ previously published studies. These works were published in peer-reviewed journals indexed in major international databases and were selected because they were performed under closely related experimental conditions using comparable synthesis procedures, characterization methods, and electrochemical testing protocols. The inclusion of this internally consistent dataset enabled a more reliable comparative analysis of structure–property relationships than is typically possible when comparing results from highly heterogeneous literature sources. The dataset was therefore used as a representative reference framework for evaluating the influence of synthesis conditions, morphology, and material composition on sensor performance.

During the preparation of this manuscript, the authors used AI-assisted graphic generation tools (ChatGPT 5.5) for the preparation of representative schematic illustrations. The use of AI tools was limited exclusively to conceptual and schematic graphical elements. All experiment-related illustrations, experimental designs, analytical plots, scientific visualizations, data interpretation, and figure validation were prepared manually by the authors. The authors reviewed and edited all AI-generated content and take full responsibility for the scientific accuracy and final content of this publication.

3. Electrochemical Detection of H₂O₂

3.1. Enzymatic vs. Non-Enzymatic Electrochemical Sensors

Hydrogen peroxide detection can be achieved using either enzymatic or non-enzymatic sensors, each based on different sensing mechanisms.

Enzymatic sensors rely on biological catalysts, most commonly enzymes such as horseradish peroxidase (HRP), which selectively react with H₂O₂ [22,23,24]. The enzymatic reaction produces an electrochemical signal proportional to the analyte concentration.

In enzymatic systems, detection is typically based on the catalytic activity of HRP, which facilitates the reduction of H₂O₂ through a well-established catalytic cycle [25,26,27,28]. The native enzyme (HRP–Fe³⁺) reacts with H₂O₂ to form an oxidized intermediate, Compound I:

HRP(Fe³⁺) + H₂O₂ → HRP − I(Fe⁴⁺ =O) + H₂O

(1)

Compound I is subsequently reduced in two sequential one-electron steps by an electron donor (Red), forming Compound II and then regenerating the native enzyme:

HRP − I + Red → HRP − II(Fe⁴⁺ = O) + Ox

(2)

HRP − II + Red → HRP(Fe³⁺) + Ox

(3)

In electrochemical sensors, the electron donor may be a redox mediator or the electrode itself. In mediated systems, the oxidized mediator (Ox) is electrochemically reduced at the electrode surface:

Ox + e⁻ → Red

(4)

This cyclic process results in continuous electron transfer between the enzyme and the electrode, producing a current proportional to the H₂O₂ concentration.

Depending on the electrode design and immobilization strategy, these sensors commonly achieve limits of detection (LOD) in the micromolar to nanomolar range [25,29,30], with some advanced nanostructured enzymatic platforms reporting detection limits below 1 nM. Such high sensitivity makes enzymatic sensors particularly suitable for applications requiring precise detection of trace H₂O₂ concentrations. In practice, they are widely used in biomedical diagnostics [31], food quality control [32], environmental monitoring [33], and biosensing systems for clinical analysis, including detection of oxidative stress biomarkers [34], glucose sensing (through coupled oxidase reactions), and monitoring of cellular metabolism. However, despite their excellent analytical performance, their practical use is often limited by poor long-term stability, enzyme denaturation, sensitivity to pH and temperature variations, and relatively high fabrication costs, which has stimulated increasing interest in non-enzymatic alternatives [35].

Non-enzymatic sensors [36,37], in contrast, utilize catalytic materials to directly oxidize or reduce H₂O₂ on the electrode surface and are typically modified with metal oxides, noble metals, or nanostructured materials. Depending on the applied potential, H₂O₂ can undergo either reduction or oxidation [38,39]. In the cathodic pathway, H₂O₂ is reduced:

H₂O₂ + 2e⁻ → 2OH⁻ (in alkaline media)

(5)

H₂O₂ + 2H⁺ + 2e⁻ → 2H₂O (in neutral or acidic media)

(6)

Alternatively, in the anodic pathway, H₂O₂ is oxidized:

H₂O₂ → O₂ + 2H⁺ + 2e⁻

(7)

The presence of catalytic nanomaterials enhances these reactions through surface-mediated redox processes [40,41]. Transition metal centers (Mⁿ⁺) undergo reversible oxidation state changes, facilitating electron transfer:

Mⁿ⁺ + H₂O₂ → M⁽ⁿ⁺¹⁾⁺ + OH⁻+ ⋅OH

(8)

M⁽ⁿ⁺¹⁾⁺ + e⁻ → Mn⁺

(9)

This catalytic cycle lowers the activation energy and accelerates electron transfer kinetics. As a result, the measured current in non-enzymatic sensors arises directly from the electrochemical oxidation or reduction of H₂O₂ and is proportional to its concentration.

In recent years, non-enzymatic electrochemical sensors have become widely studied and increasingly preferred due to several key advantages [10]. First, the absence of biological components eliminates issues related to enzyme denaturation, leading to significantly enhanced long-term stability and operational robustness [42]. Second, advances in nanotechnology and materials science have enabled the development of highly active nanomaterials (nanozymes) [43,44] that can achieve sensitivity comparable to or even exceeding enzymatic systems. Third, these sensors are more suitable for miniaturization and integration into portable or wearable devices due to their structural simplicity [11]. Furthermore, their cost-effectiveness and ease of mass production make them attractive for large-scale and real-world applications, including environmental monitoring, clinical diagnostics, and food safety analysis. As a result, research efforts have increasingly focused on optimizing non-enzymatic sensor performance, particularly in improving selectivity and resistance to interference.

A wide range of materials has been explored to enhance the performance of H₂O₂ sensors, particularly in non-enzymatic systems. These materials are selected based on their catalytic activity, conductivity, and surface properties. Metal oxides are among the most widely studied materials for H₂O₂ detection. Transition metal oxides such as TiO₂ [45], MnO₂ [46,47], CuO [48,49], ZnO [23,50], Co₃O₄ or CoO [51,52,53,54] and Fe₃O₄ [55] exhibit strong catalytic activity toward H₂O₂ redox reactions and are attractive due to their low cost, chemical stability, and ease of synthesis. However, their relatively low electrical conductivity can limit sensor performance, often requiring modification or combination with conductive materials.

Composites combine two or more materials to synergistically improve sensing properties [56,57]. For example, metal oxide–carbon composites (e.g., graphene, carbon nanotubes) enhance conductivity and increase active surface area, leading to improved sensitivity and faster electron transfer [14,15,53,58]. MXene–metal oxide composites have recently emerged as promising sensing materials, where the highly conductive two-dimensional MXene layers facilitate interfacial electron transport and promote synergistic interactions with catalytically active metal oxides [59,60,61]. Polymer-based [15,62,63] and hybrid nanocomposites [45,53,64] are also used to improve mechanical stability and selectivity [11,65]. The main drawback of composites is the increased complexity of synthesis and potential reproducibility issues.

Noble metals, such as silver (Ag) [58,66], platinum (Pt) [13], gold (Au) [14,67], and palladium (Pd) [15,44], are highly effective catalysts for H₂O₂ detection due to their high electrical conductivity (typically 10⁶–10⁷ S·m⁻¹) and excellent electrocatalytic performance, often providing sensitivities in the range of hundreds to several thousand μA·mM⁻¹·cm⁻² and detection limits from the nanomolar to low micromolar range. However, their high cost and susceptibility to poisoning or surface fouling limit their widespread application, especially in large-scale or disposable devices.

The comparative analysis of electrode materials summarized in

Table 1. Non-enzymatic nanostructured sensors for H₂O₂ detection.

Electrode, Morphology	Synthesis Method	Sensitivity	Linear Range	LOD	Selectivity	Reference
TiO2 NTs/Au NPs, nanotubes	Anodic oxidation	~519 µA·mM−1	1–9.97806 μM 19.93–198.47 μM	~104 nM	AA, Glu, UA, NaNO3, KCl, EtOH, AcOH	[45]
NiO NPs/GCE, nanoparticles	Co-precipitation method	–	8.6 nM–433.24 μM	4.28 nM	DA, UA, Glu, FA, AA, 3NT	[68]
SPEs/PBNPs, nanoparticles	Co-precipitation method	762 μA·mM−1·cm−2	0–4.5 mM	0.2 μM	Not reported	[69]
Co3O4/MWCNTs/CPE, nanoparticles	Microwave decomposition method	729.7 μA·mM−1	20–430 μM	2.46 μM	AA, UA, DA, Glu, AcOH	[53]
Co3O4/rGO, nanowires	Hydrothermal synthesis	1140 μA·μM−1·cm−2	15–675 μM	2.4 μM	Not reported	[51]
Co3O4/NiO-NSs/CF-1801, nanosheets	Solvothermal synthesis	7.67 mA·mM−1·cm−2	0.20–4.00 mM	5.51 µM	Glu, AA, UA, DA	[54]
CoO-CoS/NF, nanosheets	Amperometry process in aqueous solution	590 μAm·M−1	2–954 μM	0.890 μM	AA, UA, GL, urea, KCl, Na2SO4, DA, L-Cysteine, OA	[52]
Bi2O3/MnO2, nanoflowers	Redox reaction and hydrothermal treatment	0.914 μA·μM−1·cm−2	0.2–290 μM	0.05 μM	Na+, K+, NH4+, SO42−, Cl−, NO3−, CA, Glu, UA, AA, DA, Cys	[70]
AZO/ZnO nRs, nanorods	Hydrothermal synthesis	1.1 μA·μM−1·cm−2 and 295 nA·μM−1·cm−2	10–700 μM	42 μM and 143.5 μM	Not reported	[50]
Pd/PTH@GCE, porous film	Coating	40.0 μA·mM−1	0.2–7.0 mM	12.3 μM	Not reported	[15]
Rh/GCE, nanoparticles	Electrodeposition	172.24 mM−1·cm−2	5–1000 µM	1.2 µM	Gly, SA, Na-EDTA, H3PO4, K+, Na+, Mg2+, Cl−, NO3−, SO42−	[38]
rROGO-S-Au HS/GCE, hollow spheres	Oxidation in aqueous solution	0.19 mA·mM−1·cm−2	0.005–11.5 mM	5 μM	UA, AA, DA, Glu, NaNO3	[67]
Co2P/ITO, nanoparticles	Hydrothermal synthesis	668.6, 339.0 and 102.3 mA·mM−1·cm−2	0.0001–1.0 mM, 1.0–5.0 mM, 5.0–10.0 mM	0.65 μM	NaCl, KCl, Glu, Fru, urea, L-Gly, L-Arg, L-Lys, AA, DA, UA, APAP	[71]
AgNPs/rGO/GCE, nanoparticles	Electrodeposition	49 μA·mM−1·cm−2	5–620 μM	3.19 μM	DA, NaCl, KCl, Glu, UA	[58]
Ni(OH)2 nPs, nanoparticles	Chemical reduction method	1660 μA·mM−1·cm−2	30–320 μM	26.4 μM	Not reported	[72]
Hb/CoP/CC, nanowires	Calcination	56.2μA·mM−1·cm−2	2–2670 μM	0.67 μM	Glu, AA, UA, DA	[73]
[Co(pbda)(4,4-bpy) (2H2O)]n/GCE, 3D crystals	Hydrothermal synthesis	83.10 mA·mM−1·cm−2	50–9000 μM	3.76 μM	sorbitol, Gly, EtOH, Glu, LA	[63]
GC/Chi- (CXBiFe-1050), nanocomposite	Pyrolysis	4.55 μA·mM−1	50–1000 μM	2.5 μM	Not reported	[62]
Co3O4 nW/N- carbon foam, nanowires	Hydrothermal synthesis	230 μA·mM−1·cm−2	0.01–1.4 mM	1.4 μM	Glu, AA, DA, UA, L-Cys, NaCl	[74]
CuO/CoO, leaf-like	Hydrothermal synthesis	6349 μA·mM−1	2–4000 μM	1.4 μM	NaCl, Glu, Fru, UA, DA, AA	[49]
3DGH/NiO, octahedrons	Hydrothermal synthesis	117.26 µA· mM−1·cm−2	0.01–33.58 mM	5.3 µM	DA, AA, NaNO2, Glu, urea, KCl, UA	[75]
LSG-Ag, nanoparticles	Laser induced	–	0.1–10 mM	7.9 μM	Glu, AA, NaCl, KCl	[66]
Cu-ZnO nR, nanorods	Hydrothermal synthesis	3415 μA·mM−1·cm−2	0.001–11 mM	0.16 μM	Fru, CA, AA, DA, UA, Val, Ala, CA, Phe, Gly, Pen	[64]
THP/SPCE, coating	Chemical synthesis	–	0–1000 μM	0.14 μM	Na+, K+, NO2−, DA, Glu, UA	[76]
CoS, tremella-like	Hydrothermal synthesis	459 µA· mM−1 ·cm−2	0.005–14.82 mM	1.5 μM	Glu, AA, UA, DA, AAP, Fru	[77]

demonstrates that the electrochemical performance of H₂O₂ sensors depends not only on material composition but also on nanostructure morphology, active surface area, and intrinsic catalytic properties.

The data presented in Table 1 demonstrate that hybrid and heterostructured nanomaterials generally provide the highest sensitivities for non-enzymatic H₂O₂ detection, highlighting the importance of synergistic interactions between catalytic and conductive components. Among the reported systems, Co₃O₄/rGO exhibits the highest sensitivity, reaching approximately 1.14 × 10⁶ μA·mM⁻¹·cm⁻² after unit conversion, substantially exceeding the performance of most other electrodes. Similarly, Co₃O₄/NiO nanosheet composites and CuO/CoO heterostructures also demonstrate exceptionally high sensitivities in the range of 10³–10⁴ μA·mM⁻¹·cm⁻². These results strongly indicate that incorporation of conductive carbon phases and formation of multi-component oxide interfaces are highly effective strategies for maximizing electrocatalytic response.

In contrast, the lowest limits of detection are not always associated with the highest sensitivities. The most effective trace-level detection is achieved by NiO nanoparticle electrodes, which exhibit an ultralow LOD of 4.28 nM, outperforming even highly sensitive composite systems. TiO₂ nanotube/Au nanoparticle electrodes also show excellent low-concentration detection capability with detection limits near 100 nM. These observations suggest that while hybrid materials are superior for signal amplification and sensitivity enhancement, carefully engineered single-component nanostructures can provide outstanding detection limits due to favorable surface kinetics and low background noise.

The comparison of linear ranges further demonstrates significant differences in sensor applicability. Several electrodes, including CoS, Co₂P/ITO, and Cu–ZnO nanorods, exhibit exceptionally broad linear ranges extending from the low micromolar region up to 10–15 mM H₂O₂, making them particularly suitable for practical applications requiring detection across a wide concentration window. In contrast, some highly sensitive systems, such as Co₃O₄/rGO, operate over narrower concentration ranges, indicating that extremely high sensitivity may come at the expense of dynamic range. Therefore, broad-range systems such as CoS and Co₂P/ITO appear especially advantageous for real-sample monitoring, where H₂O₂ concentrations can vary significantly.

The results collectively confirm that sensor optimization depends not only on material composition but also on balancing conductivity, catalytic activity, nanostructure morphology, linear range, and interfacial charge-transfer properties.

Beyond sensitivity and detection limit, the practical applicability of H₂O₂ sensors is strongly influenced by selectivity, long-term stability, reproducibility, and anti-interference capability. Most metal oxide-based sensors exhibit good tolerance toward common interfering species found in plant tissues, including ascorbic acid, glucose, organic acids, amino acids, and various inorganic ions. Together, these compounds represent the major classes of potential interferents present in plant matrices, providing a realistic assessment of sensor selectivity. The demonstrated resistance to these interferents confirms the suitability of metal oxide-based sensors for practical oxidative stress monitoring in plants.

Long-term stability was reported to be high in the majority of the reviewed studies. However, stability assessments were performed using substantially different protocols, storage conditions, testing intervals, and evaluation criteria, making direct quantitative comparison difficult. For this reason, stability was not included among the comparative parameters in Table 1.

3.2. Hydrothermal Synthesis Strategies and Morphology Control of Nanostructured Metal Oxides

Hydrothermal synthesis is a versatile method for preparing nanostructured metal oxide electrodes because it enables controlled crystal growth in aqueous media at elevated temperature and pressure [78,79,80]. In general, the process involves dissolution of reactive species, generation of supersaturation, nucleation of a solid phase, and subsequent crystal growth. However, the detailed mechanism depends strongly on the source of metal ions. In one case, metal ions are supplied from the solution through dissolved precursors; in the other, they originate from the substrate itself through surface dissolution and oxidation. This distinction is fundamental because it determines where nucleation occurs, how the nanostructure develops, and how strongly the final coating is attached to the electrode surface.

Figure 1 displays schematic illustration of the two principal hydrothermal growth mechanisms for nanostructured metal oxide electrodes: precursor-derived growth and substrate-derived growth.

When the metal ions are supplied from the solution, hydrothermal synthesis proceeds through a precursor-based route [81]. Dissolved metal salts such as nitrates, chlorides, or acetates release cations into the reaction medium:

M_(aq)ⁿ⁺ ← precursor

(10)

At the same time, additives such as urea or hexamethylenetetramine gradually generate hydroxide ions, which prevents sudden precipitation and allows controlled supersaturation:

Mⁿ⁺ + nOH⁻→M(OH)_n

(11)

Once the local concentration exceeds the solubility limit, nuclei of the hydroxide or hydrated oxide phase form, usually on the substrate surface, where heterogeneous nucleation is energetically favored. These nuclei then grow by continuous supply of ions from the surrounding solution. Thus, the substrate mainly acts as a support and nucleation surface, while the actual metal source is external. After hydrothermal growth, the deposited hydroxide precursor is often thermally converted into the corresponding oxide:

M(OH)_n + T↑ → MO_x + H₂O

(12)

This is the typical mechanism for materials such as ZnO, Co₃O₄ and NiO synthesized from zinc, cobalt or nickel salt precursors [82]. The key feature of this route is that crystal growth is governed by ion diffusion from bulk solution to the substrate, which allows flexible tuning of morphology [83,84]. Among the most important parameters are precursor concentration [85,86], temperature [87], pH [88,89,90], reaction time [91,92], and the presence of additives or structure-directing agents [93,94,95,96,97]. Precursor concentration determines the degree of supersaturation in the solution, which directly affects nucleation density and growth rate. High supersaturation typically leads to rapid nucleation and the formation of smaller, less ordered particles, whereas lower concentrations favor controlled growth and well-defined nanostructures. Temperature plays a critical role by influencing reaction kinetics, diffusion rates, and crystal phase formation; higher temperatures generally promote crystallinity and faster growth, but may also lead to aggregation or structural coarsening. The pH of the reaction medium controls the availability of hydroxide ions and therefore governs the formation of hydroxide intermediates and their subsequent transformation into oxides. Alkaline conditions often favor anisotropic growth and the formation of nanowires, nanosheets, or nanopetals, while neutral or acidic conditions may result in more compact or isotropic structures. Reaction time further determines the extent of crystal growth and structural evolution, with longer durations enabling processes such as Ostwald ripening and hierarchical assembly. Additives such as surfactants like PEI or citrate ions play a crucial role by regulating the release of hydroxide ions and selectively adsorbing on specific crystal facets. This facet-selective adsorption modifies surface energy and directs anisotropic growth, enabling the formation of complex architectures.

In contrast, when the metal ions are supplied by the substrate itself, the mechanism is fundamentally different. In this case, the substrate is not merely a passive support but also the chemical source of the growing oxide [98,99,100,101]. Under hydrothermal conditions, the metal surface undergoes oxidation and partial dissolution:

M_(s) → Mⁿ⁺ + ne⁻

(13)

The released metal ions then react immediately near the interface with hydroxide ions, water, or dissolved oxidants to form oxide or hydroxide nuclei directly on the same surface:

Mⁿ⁺ + nOH⁻ → M(OH)ⁿ

(14)

M(OH)_n → MO_x + H₂O

(15)

or, in a simplified direct form,

M + oxidant + H₂O → MO_x

(16)

Because the metal ions are generated locally at the substrate/electrolyte interface, nucleation and growth occur directly on the substrate, often through a dissolution–oxidation–reprecipitation mechanism. This route is characteristic of TiO₂ grown on titanium [102,103] and CuO grown on copper [104,105,106]. For example, titanium in alkaline hydrothermal media can partially dissolve and react to form titanate or titanium oxide structures directly on the Ti surface, while copper in oxidizing alkaline solution can be converted into CuO through interfacial oxidation. Here, the growth is controlled not by transport of metal ions from the bulk solution but by the rate of substrate oxidation, ion release from the surface, and immediate local reprecipitation.

The difference between these two mechanisms can therefore be stated clearly. In the solution-derived route, metal ions are already present in the reaction medium before nucleation begins, and the substrate mainly provides a surface for heterogeneous deposition. Growth depends on diffusion of precursor species from the bulk solution to the surface. In the substrate-derived route, metal ions are generated in situ by chemical transformation of the substrate itself, so nucleation is inherently interfacial and tightly coupled to substrate oxidation. As a result, substrate-derived growth usually gives stronger adhesion and better electrical contact, since the oxide layer forms directly from the underlying metal. By contrast, solution-derived growth offers greater compositional flexibility and easier control of thickness and morphology, because the metal source, precursor concentration, and additives can be independently adjusted.

The influence of these factors is summarized in

Table 2. General information on the synthesis and characteristics of nanostructures for coating working electrodes.

Oxide	SEM Image	Morphology	XRD Pattern	Synthesis Solution	Synthesis Parameters	Reference
TiO2		Nanowires network, fiber diameter 50–120 nm, pore size 0.2–1.0 μm		Ti metal substrate, NaOH (1–7 M) aqueous solution	1–3 h in autoclave 20–180 °C	[107]
CuO		Nanoflowers, flower diameter 1–4 μm, nanosheet thickness 20–80 nm		Cu wire substrate, 10 M NaOH + 1 M (NH4)2S2O8 aqueous solution	3 h, 90 °C in glass beaker	[108]
Co3O4 (acetate precursor)		Porous nanosheet network, pore size 0.3–1.5 μm, sheet thickness 10–50 nm		Fe wire substrate, 0.1 M (CH3COO)2Co·4H2O + 0.1 M CH4N2O aqueous solution	5 h, 95 °C hydrothermal synthesis in glass beaker; 1 h 450 °C annealing	[109]
Co3O4 (nitrate precursor)		Wrinkled nanosheets, lateral sheet size 0.5–3 μm, sheet thickness 20–70 nm		Fe wire substrate, 0.1 M Co(NO3)2·6H2O + 0.1 M CH4N2O aqueous solution	5 h, 95 °C hydrothermal synthesis in glass beaker; 1 h 450 °C annealing	[110]
Co3O4 (cloride precursor)		Nanowires, diameter 80–200 nm		Fe wire substrate, 0.1 M CoCl2·6H2O + 0.1 M CH4N2O aqueous solution	5 h, 95 °C hydrothermal synthesis in glass beaker; 1 h 450 °C annealing	[109]
NiO		Nanowalls, pore diameter 0.5–2.0 μm, wall thickness 100–300 nm		Fe wire substrate, 0.1M Ni(NO3)2·6H2O + 0.1M C6H12N4 aqueous solution	5 h, 95 °C hydrothermal synthesis in glass beaker; 3 h 450 °C annealing	[111]

. The table presents representative examples of hydrothermal synthesis routes employing both precursor-based and in situ substrate-derived mechanisms, using metal oxide electrodes previously synthesized by our group. The observed variations in morphology, crystallinity, and synthesis conditions demonstrate how subtle differences in growth pathways and reaction parameters translate into distinct structural characteristics, which ultimately determine the electrochemical performance of the electrodes.

The results presented in Table 2 reveal several clear relationships between synthesis mechanism, precursor chemistry, and the resulting nanostructure morphology. Substrate-derived growth mechanisms consistently produce structures that are directly integrated with the metallic substrate and characterized by continuous surface coverage with interconnected architectures. In contrast, precursor-derived systems exhibit substantially greater morphological variability, indicating stronger dependence on solution chemistry and nucleation kinetics.

For Co₃O₄, the precursor anion has a pronounced effect on structural organization. Acetate-containing systems tend to promote formation of denser and less ordered morphologies, whereas nitrate-based synthesis results in more uniform and spatially organized nanostructures with higher apparent crystallinity. Chloride-derived systems exhibit intermediate structural characteristics, suggesting that precursor-dependent differences in hydrolysis rate and ion coordination influence crystal growth pathways. These observations indicate that precursor chemistry affects not only crystal formation but also the balance between nucleation density and anisotropic growth.

Another visible trend is the frequent formation of hierarchical porous architectures regardless of oxide composition. Nanopetals, nanosheets, nanofibers, and flower-like assemblies are observed across multiple systems, indicating that hydrothermal growth under relatively mild conditions favors anisotropic crystal development and self-assembly processes. At the same time, the degree of ordering and compactness differs considerably between materials, suggesting that crystallization kinetics and interfacial growth mechanisms strongly influence final morphology.

The XRD patterns further demonstrate that morphology does not correlate directly with crystallinity. Systems exhibiting highly ordered crystal phases do not necessarily possess the most porous or complex architectures, while partially disordered structures frequently display more developed surface morphologies. This suggests that hydrothermal synthesis conditions simultaneously affect crystal growth and defect formation, leading to structurally distinct materials even within the same oxide system.

Despite its versatility and ability to produce highly ordered nanostructures with controlled morphology, hydrothermal synthesis still faces several important limitations that restrict its large-scale industrial application. One of the major challenges is limited scalability, since hydrothermal processes are typically performed in closed reactors under controlled temperature conditions and, in many cases, elevated autogenous pressure, making large-volume production technically complex and time-consuming. In addition, small variations in synthesis parameters such as precursor concentration, pH, temperature distribution, reaction time, filling ratio, and autoclave geometry can significantly influence nucleation and crystal growth, resulting in batch-to-batch variation in morphology, crystallinity, and electrochemical performance. Such reproducibility issues are particularly critical for sensor fabrication, where small structural differences may lead to substantial changes in sensitivity and detection limits. The process also often requires relatively long synthesis times and additional post-treatment steps such as annealing, increasing energy consumption and overall production cost. Although precursor costs may be relatively high for some complex or less common metal oxide systems, they generally remain substantially lower than those associated with noble metal catalysts or enzyme-based sensing platforms. Furthermore, although metal oxide nanozymes are generally far less sensitive to atmospheric conditions than biological enzymes, are not prone to enzymatic degradation, and often exhibit excellent long-term stability, prolonged exposure to humid environments may still affect their surface properties and gradually reduce performance. Therefore, practical implementation may require appropriate storage conditions, such as hermetically sealed packaging or vacuum desiccators, to ensure long-term stability during transportation and storage. Difficulties in achieving uniform nanostructure growth on large or complex substrates remain a challenge for practical device integration. Another important challenge is the transfer of laboratory-scale synthesis protocols to industrial-scale production. As reactor volume increases, changes in heat and mass transfer, mixing efficiency, and temperature gradients may alter nucleation and crystal growth processes, leading to variations in morphology, uniformity, and material performance. Therefore, although hydrothermal synthesis is highly effective for laboratory-scale preparation of nanostructured metal oxides, further optimization of process standardization, reactor design, and scale-up strategies is necessary for reliable commercial implementation.

3.3. Sensor Fabrication, Electrochemical Testing, and Analytical

The preparation of electrochemical sensors based on nanostructured metal oxides involves several key steps, including substrate selection and treatment, deposition or growth of the active material, and assembly of the electrochemical measurement system. These steps are critical for ensuring good electrical contact, mechanical stability, and efficient electron transfer between the electrode surface and the analyte.

Conductive substrates such as glassy carbon electrodes (GCE), metal wires or foils (e.g., Cu, Ti, Fe), indium tin oxide (ITO), and carbon-based supports are most commonly used as working electrodes. Prior to modification, substrates are typically subjected to mechanical polishing and/or chemical cleaning to remove surface contaminants and increase surface roughness, which enhances adhesion of the nanostructured layer. In the case of in situ hydrothermal growth, the nanostructures are directly formed on the substrate surface, resulting in strong interfacial contact. Alternatively, pre-synthesized nanomaterials can be deposited onto the electrode surface via drop-casting, spin-coating, or electrophoretic deposition, often using binders such as Nafion or chitosan to improve film stability.

Following material deposition, the modified electrode is integrated into an electrochemical cell, most commonly a three-electrode configuration. This system consists of a working electrode (modified with the nanostructured material), a reference electrode (typically Ag/AgCl or saturated calomel electrode), and a counter electrode (commonly platinum wire or graphite). The working electrode is the active sensing element where the redox reaction of H₂O₂ occurs, while the reference electrode provides a stable potential and the counter electrode completes the circuit. The electrolyte solution, often phosphate buffer (PBS) or alkaline media such as NaOH, provides ionic conductivity and influences the reaction pathway of H₂O₂.

Several electrochemical techniques are commonly employed to evaluate sensor performance. Cyclic voltammetry (CV) is widely used for initial characterization, providing information on redox behavior, reaction reversibility, and optimal operating potential. However, CV is relatively less sensitive for quantitative detection. Chronoamperometry is frequently used for analytical measurements, where current is recorded over time at a fixed potential following successive additions of H₂O₂; this method offers high sensitivity and rapid response. Differential pulse voltammetry (DPV) and square wave voltammetry (SWV) are pulse techniques that improve sensitivity and lower detection limits by minimizing capacitive current contributions. Among these, DPV is particularly effective for detecting low concentrations of analytes.

In practice, the choice of electrochemical method depends on the desired balance between sensitivity, detection limit, and measurement speed. CV is typically used for mechanistic studies and electrode optimization, while CA and DPV are preferred for quantitative sensing applications.

Table 3. CV graphs for metal oxide based nanostructured electrodes for H₂O₂ detection *.

Oxide	Morphology	CV Graph	Supporting Electrolyte	Measurement Potential (vs. Ag/AgCl)	Sensitivity (mA·mM−1)	LOD (µM)	Linear Detection Range (mM)	Selectivity	Reference
TiO2	Nanowires network		PBS (pH = 7.4)	−1.1 V	2.91	31	0–5	NaCl, KNO3, Glu, CA, AA	[107]
CuO	Nanoflowers		NaOH (pH = 13)	−0.7 V	2.00	12.3	0–3	CA, AA, NaCl, Glu, KNO3, urea	[108]
Co3O4 (acetate precursor)	Porous nanosheet network		NaOH (pH = 13)	−1.23 V	1.61	55	0–5	NaCl, KNO3, Glu, CA, AA	[109]
Co3O4 (nitrate precursor)	Wrinkled nanosheets		NaOH (pH = 13)	−1.23 V	0.16	33	0–2	AA, UA, NaCl, Glu	[110]
Co3O4 (cloride precursor)	Nanowires		NaOH (pH = 13)	−1.23 V	1.71	45	0–5	AA, UA, NaCl, Glu	[109]
NiO	Nanowalls		NaOH (pH = 13)	−1.3 V	3.7	25	0–2	AA, UA, NaCl, Glu	[111]

* In the cyclic voltammetry images, the concentration range differs from the reported linear range because it was truncated at 2 mM, the lowest upper-limit value among the studies considered, to enable consistent visual comparison of the sensor responses.

summarizes CV-based sensing performance of representative nanostructured electrodes described in Table 2. These results are included as a model dataset to illustrate structure–property relationships under relatively consistent experimental conditions, which is often difficult to achieve when comparing data across independent literature sources.

Table 3 demonstrates that the sensor response obtained by cyclic voltammetry is determined not only by the oxide composition but also by the interplay between morphology, electrolyte conditions, and applied potential. The most pronounced analytical response is observed for NiO, indicating efficient participation of surface Ni²⁺/Ni³⁺ redox centers in the electrocatalytic conversion of H₂O₂. At the same time, CuO exhibits a more balanced combination of sensitivity and detection limit, which may be associated with strong electrical contact between the oxide layer and the copper substrate together with good accessibility of active surface sites.

For Co₃O₄, a clear precursor-dependent effect is observed: identical chemical composition does not result in identical electrochemical behavior. The differences between acetate-, nitrate-, and chloride-derived samples indicate that surface morphology and defect structure strongly influence the CV response. Importantly, higher crystallinity alone does not necessarily lead to improved sensitivity; instead, the accessibility of electroactive sites and the efficiency of charge transfer appear to play a more critical role in H₂O₂ detection under cyclic voltammetry conditions.

Overall, Table 3 confirms that cyclic voltammetry is an informative technique for the initial comparison of electrocatalytic activity among nanostructured metal oxides. However, the obtained sensitivity and LOD values should be interpreted primarily as indicators of the intrinsic electrochemical behavior of the material rather than the maximum achievable analytical performance of the sensor.

While CV is widely used for fundamental electrochemical characterization, its applicability for quantitative sensing is often limited by relatively high capacitive currents and lower signal-to-noise ratios, particularly at low analyte concentrations. For this reason, alternative electrochemical techniques such as DPV and chronoamperometry are frequently employed to enhance analytical performance. DPV operates by superimposing potential pulses onto a linear sweep, effectively minimizing non-faradaic current contributions and significantly improving sensitivity and detection limits. This makes DPV particularly suitable for trace-level detection. In contrast, chronoamperometry involves measuring the current response at a fixed potential over time following analyte addition (method of standard additions (MSA)), allowing rapid, steady-state detection with high temporal resolution. This technique is especially advantageous for real-time monitoring and evaluating sensor response dynamics.

The effectiveness of these methods compared to CV (Table 3) is illustrated in

Table 4. Additional electrochemical techniques for nanostructured sensor sensitivity improvement.

Oxide	Method of Measurement	Supporting Electrolyte	Sensitivity (mA·mM−1)	LOD (µM)	Changes of Sensitivity (Compared to CV)	Changes in LOD (Compared to CV)	Reference
TiO2	Chronoamperometry	PBS (pH = 7.4)	0.04	2.8	−98.63%	90.97%	[107]
CuO	DPV	NaOH (pH = 13)	11.9	1.9	+495.00%	84.55%	[108]
Co3O4 (nitrate precursor)	Chronoamperometry	NaOH (pH = 13)	0.19	5.2	+18.75%	84.22%	[110]
Co3O4 (cloride precursor)	Chronoamperometry	NaOH (pH = 13)	0.51	1.59	−70.18%	96.47%	[109]
NiO	Chronoamperometry	NaOH (pH = 13)	2.48	1.05	−32.97%	95.80%	[111]

, which summarizes the performance of the nanostructured electrodes discussed above when evaluated using DPV and chronoamperometry.

To better compare the analytical behavior of the investigated nanostructured electrodes, the corresponding calibration curves obtained using different electrochemical techniques are presented in Figure 2. The figure enables direct visualization of differences in signal response, linearity, and concentration dependence between conventional cyclic voltammetry and more advanced techniques such as DPV and chronoamperometry. These comparisons further illustrate how both material composition and measurement method influence the analytical performance of H₂O₂ sensors.

The results summarized in Table 4 and Figure 2 clearly demonstrate that the choice of electrochemical technique substantially influences the analytical performance of nanostructured H₂O₂ sensors. Compared to cyclic voltammetry, alternative methods such as differential pulse voltammetry and chronoamperometry generally provide improved detection capability due to suppression of capacitive current contributions and more favorable signal-to-noise characteristics.

The most pronounced improvement is observed for CuO, where DPV leads to a major increase in sensitivity together with a lower detection limit. This behavior indicates that CuO possesses sufficiently high intrinsic electrocatalytic activity, but its response under CV conditions is partially masked by background currents. Pulse-based measurements therefore allow more efficient utilization of the active surface and better resolution of faradaic processes. In contrast, chronoamperometric measurements for TiO₂, Co₃O₄, and NiO mainly improve the limit of detection rather than sensitivity, suggesting that steady-state current monitoring is more effective for low-concentration analysis than for signal amplification.

The comparison also reveals that the effect of technique optimization strongly depends on the electrode material and surface structure. NiO maintains high analytical performance under both CV and chronoamperometric conditions, indicating stable charge-transfer behavior and efficient catalytic activity independent of the measurement mode. Conversely, Co₃O₄ systems exhibit more variable responses, again confirming that precursor-dependent morphology and defect structure significantly influence electrochemical behavior. In particular, nitrate-derived Co₃O₄ remains less responsive even after switching to chronoamperometry, implying intrinsic limitations associated with its surface properties rather than solely the measurement technique.

Overall, the combined analysis of Table 4 and Figure 2 indicates that optimization of the electrochemical method is as important as optimization of the electrode material itself. While CV remains highly valuable for mechanistic evaluation and comparative analysis of catalytic activity, DPV and chronoamperometry are more effective for practical H₂O₂ sensing applications requiring low detection limits and improved analytical reliability.

The overall workflow of nanostructured electrochemical sensor fabrication and subsequent analytical application is summarized in Figure 3.

Figure 3A illustrates a nanostructured electrode preparation steps. Figure 3B presents the general analytical workflow used in plant stress studies, including sample preparation and electrochemical measurements employing techniques such as cyclic voltammetry, differential pulse voltammetry, and chronoamperometry.

Despite the promising analytical performance reported for many nanostructured metal oxide electrodes, several important challenges still limit their practical implementation in real-world sensing applications. One of the key concerns is long-term sensor stability. Although non-enzymatic metal oxide sensors generally exhibit higher operational stability than enzymatic systems, prolonged electrochemical cycling, surface oxidation, and gradual restructuring of active sites may reduce catalytic activity over time [112]. In addition, adsorption of reaction intermediates or biological contaminants on the electrode surface can lead to fouling effects, causing signal deterioration and reduced analytical reliability during continuous operation [113]. Another critical issue involves selectivity and interference effects. In practical biological and environmental samples, H₂O₂ is commonly accompanied by electroactive compounds such as ascorbic acid, uric acid, glucose, dopamine, phenolic molecules, and dissolved inorganic ions. These species may generate overlapping electrochemical responses or influence interfacial charge-transfer processes, thereby affecting sensor accuracy [114,115]. Although many published studies report excellent sensitivity under controlled laboratory conditions, interference studies are often limited to simplified model systems and do not fully represent the complexity of real samples. Real-world applicability of nanostructured electrochemical H₂O₂ sensors therefore remains an important challenge. Many sensor systems are evaluated primarily in standard buffer electrolytes rather than in complex biological matrices, plant extracts, soil-derived solutions, or environmental samples. Under practical conditions, matrix effects, pH fluctuations, ionic strength variations, and biofouling can significantly alter electrochemical behavior and sensor response [116,117,118]. Consequently, future research should focus not only on improving sensitivity and lowering detection limits, but also on enhancing long-term operational stability, anti-interference capability, and analytical reliability during real-sample measurements.

4. Real-Sample H₂O₂ Analysis and Nanoparticle-Assisted Oxidative Stress Monitoring in Plants

Application of electrochemical H₂O₂ sensors to real plant samples represents a critical step beyond laboratory calibration studies and an important transition toward practical in-field applications. Since H₂O₂ is one of the key biomarkers of oxidative stress, its accurate determination in plant tissues enables direct assessment of plant physiological status under environmental stress conditions. Unlike standard laboratory solutions, plant extracts constitute chemically complex matrices containing sugars, organic acids, amino acids, phenolic compounds, pigments, and inorganic ions that may influence electrochemical responses. Therefore, successful H₂O₂ detection in real plant samples requires not only high sensitivity but also sufficient selectivity and resistance to matrix effects. The ability to quantify H₂O₂ in complex biological samples is essential for translating electrochemical sensing technologies from laboratory development to practical applications in plant physiology, crop monitoring, and stress diagnostics.

A particularly attractive feature of metal oxide nanomaterials is their dual functionality in plant stress research. Nanostructured metal oxides in the form of two-dimensional and three-dimensional architectures serve as highly efficient electrochemical platforms for H₂O₂ detection, whereas the same materials can also be applied as nanoparticles for stress mitigation. Metal oxides such as ZnO, Fe₃O₄, TiO₂, MgO, and CeO₂ have been reported to reduce ROS accumulation, enhance antioxidant defense systems, and improve plant stress tolerance. Consequently, metal oxide nanomaterials provide an integrated approach in which the same class of materials can be used both for monitoring oxidative stress through H₂O₂ detection and for mitigating its physiological effects.

4.1. Stress Mitigation Strategies in Plants

Plants are continually exposed to a wide range of abiotic and biotic stresses, including drought [78], salinity [18,19], extreme temperatures, and pathogen attack, all of which can severely impair growth and productivity. To cope with these challenges, plants have evolved a complex network of stress mitigation strategies that operate at molecular, cellular, physiological, and whole-plant levels. A central component of these strategies is the perception of stress signals through membrane-bound receptors and subsequent activation of signal transduction pathways involving calcium ions, ROS, and phytohormones such as abscisic acid, salicylic acid, and jasmonates [119,120,121]. These signaling cascades ultimately regulate the expression of stress-responsive genes, enabling plants to adjust their metabolism and development accordingly.

At the biochemical level, plants enhance their antioxidant defense systems to maintain cellular redox homeostasis under stress conditions. Enzymatic antioxidants, including superoxide dismutase, catalase, and peroxidases, work in concert with non-enzymatic molecules such as ascorbate, glutathione, and flavonoids to scavenge excess ROS generated during stress. In parallel, the accumulation of compatible solutes such as proline, glycine betaine, and soluble sugars helps stabilize proteins and membranes while contributing to osmotic adjustment [122,123,124]. These osmoprotectants also play roles in maintaining enzyme activity and protecting cellular structures.

Morphological and physiological adaptations further enhance plant resilience. For instance, plants may alter root architecture to improve water and nutrient uptake under drought or salinity stress [125], while stomatal regulation helps minimize water loss [126]. Some species develop thicker cuticles or trichomes to reduce transpiration and shield against environmental extremes [127]. Additionally, stress memory and priming mechanisms allow plants to respond more efficiently to recurring stress by maintaining a “ready state” of defense-related pathways [128].

Advances in biotechnology and breeding have also contributed to improving plant stress tolerance. Genetic engineering approaches targeting key regulatory genes, transcription factors, and stress-responsive proteins have shown promise in enhancing resilience [129,130]. Similarly, the use of beneficial microorganisms, such as plant growth-promoting rhizobacteria and mycorrhizal fungi, represents an eco-friendly strategy to boost plant performance under adverse conditions [131,132]. Together, these integrated stress mitigation strategies highlight the remarkable plasticity of plants and provide valuable insights for developing sustainable agricultural systems in the face of increasing environmental stress.

In recent years, nanotechnology has emerged as a promising tool to enhance plant tolerance to environmental stresses. Nanoparticles owing to their small size, high surface area, and unique physicochemical properties, can interact with plant systems at cellular and molecular levels, thereby modulating stress responses.

Nanoparticles can be introduced into plants through several application methods, including foliar spraying [133,134,135], irrigation [85,136], hydroponic supplementation, soil incorporation [137,138], and seed priming [9,139,140]. The choice of application strategy strongly influences nanoparticle uptake, translocation, and physiological effects. Foliar spraying enables direct interaction of nanoparticles with leaf tissues and stomata, providing rapid activation of antioxidant responses and improved photosynthetic performance. Irrigation and hydroponic application primarily promote root uptake and systemic transport through vascular tissues, often enhancing nutrient acquisition and osmotic regulation under stress conditions. Seed priming with nanoparticles is widely used to improve germination, early seedling development, and stress preparedness by activating antioxidant enzymes and stress-responsive signaling pathways prior to stress exposure.

A wide range of nanoparticles has been investigated for stress mitigation in plants, broadly categorized into metal nanoparticles, metal oxide nanoparticles, carbon-based nanomaterials, and polymeric or composite nanoparticles. Among metal nanoparticles, silver (Ag) [75,141], gold (Au) [142], copper (Cu) [137], and iron (Fe) [136] nanoparticles are commonly used. Metal oxide nanoparticles represent one of the most extensively studied groups due to their relative stability and functional versatility, including zinc oxide (ZnO) [87,93,143,144], titanium dioxide (TiO₂) [20,145,146], silicon dioxide (SiO₂) [147], iron oxide (Fe₂O₃ or Fe₃O₄) [79], cerium oxide (CeO₂) [134], aluminum oxide (Al₂O₃) [148], copper oxide (CuO) [149], and magnesium oxide (MgO) [150]. Carbon-based nanomaterials such as carbon nanotubes (CNTs) [151], graphene, graphene oxide (GO) [152], and fullerene derivatives [153], as well as polymeric nanoparticles like chitosan-based systems [154], further expand the toolbox for stress mitigation through targeted delivery and physiological modulation.

At the mechanistic level, metal oxide nanostructures used in plant applications, predominantly in the form of dispersed nanoparticles, influence stress mitigation pathways through multiple interconnected processes. One of the primary mechanisms involves modulation of ROS homeostasis [18,19,20]. Nanoparticles such as CeO₂ and TiO₂ can directly scavenge ROS due to their redox-active surfaces, while others indirectly enhance antioxidant defenses by upregulating genes encoding enzymes like superoxide dismutase, catalase, ascorbate peroxidase, and glutathione reductase. This dual role—direct ROS neutralization and activation of antioxidant systems—helps maintain cellular redox balance. Nanoparticles also interact with plant signaling networks [155,156]. Upon entry into plant tissues through roots or stomata, NPs can trigger calcium (Ca²⁺) influx and activate mitogen-activated protein kinase (MAPK) cascades, which are central to stress signal transduction. These signaling events lead to transcriptional reprogramming mediated by stress-responsive transcription factors such as DREB, NAC, WRKY, and bZIP families. In parallel, nanoparticles can influence phytohormone biosynthesis and signaling pathways, particularly abscisic acid (ABA), which regulates stomatal closure and drought response, as well as salicylic acid (SA) and jasmonic acid (JA), which are involved in defense and stress adaptation [157].

Another critical mechanism is the regulation of ion transport and homeostasis, especially under salinity and heavy metal stress [141,158]. Nanoparticles such as SiO₂ and ZnO have been shown to reduce sodium (Na⁺) uptake and enhance potassium (K⁺) retention by modulating ion transporter activity (e.g., HKT, SOS, and NHX transporters) [159]. This helps maintain ionic balance and protects cellular functions. Similarly, iron oxide nanoparticles can improve iron availability and uptake, thereby supporting chlorophyll synthesis and photosynthetic efficiency.

Nanoparticles further contribute to osmotic adjustment and metabolic regulation. They can promote the accumulation of compatible solutes such as proline, soluble sugars, and glycine betaine, which stabilize cellular structures and maintain turgor under drought or salinity stress. Additionally, certain nanoparticles enhance photosynthetic performance by improving chloroplast structure, increasing chlorophyll content, and facilitating electron transport in photosystems. For example, TiO₂ nanoparticles have been reported to enhance light harvesting and Rubisco activity, thereby improving carbon assimilation under stress conditions [160].

At the cellular level, nanoparticles may interact with membranes and cell walls, altering their permeability and mechanical properties [21]. This can improve water uptake and nutrient transport, but also requires careful control to avoid toxicity. Nanoparticles can also act as carriers for the controlled release of nutrients, antioxidants, or phytohormones, ensuring sustained availability and targeted action during stress exposure [161]. Furthermore, emerging evidence suggests that nanoparticles can influence epigenetic regulation and stress memory by modulating DNA methylation patterns and histone modifications, thereby priming plants for enhanced tolerance to recurring stress [162].

Despite the considerable potential of nanoparticles for electrochemical sensing and plant stress mitigation, increasing attention has been directed toward their possible toxicity and environmental impact. The biological effects of nanoparticles strongly depend on their physicochemical properties, including particle size, morphology, surface charge, concentration, chemical composition, solubility, and surface functionalization. While low nanoparticle concentrations often promote plant growth and stress tolerance, excessive accumulation may induce phytotoxicity through membrane damage, oxidative stress, inhibition of photosynthesis, and disruption of cellular metabolism [163,164]. In some cases, nanoparticles may themselves stimulate excessive ROS production, resulting in growth inhibition, chlorophyll degradation, and impaired nutrient balance [165,166,167].

Environmental safety is another important concern associated with large-scale agricultural application of nanomaterials. After application, nanoparticles may accumulate in soil and water systems [168], interact with microorganisms [169], and enter food chains through plant uptake and trophic transfer [170]. Such interactions can alter soil microbial diversity, enzymatic activity, nutrient cycling, and rhizosphere ecology. Persistent nanoparticles may also undergo transformation processes such as aggregation, dissolution, oxidation, or interaction with organic matter, producing secondary effects that remain insufficiently understood under real environmental conditions [171,172]. In addition, long-term accumulation of metal-based nanoparticles raises concerns regarding potential contamination of agricultural ecosystems and possible impacts on animal and human health [173,174,175].

Another major limitation is the lack of standardized protocols for evaluating nanoparticle safety and environmental behavior. Toxicity studies often differ substantially in experimental design, nanoparticle characterization, exposure conditions, and analytical methodology, making direct comparison between studies difficult. Furthermore, most available studies focus on short-term laboratory experiments, whereas information regarding chronic exposure, long-term environmental persistence, bioaccumulation, and multigenerational effects remains limited.

Regulatory frameworks for nanoparticle application in agriculture and environmental systems are also still under development in many countries. The absence of harmonized international guidelines for nanoparticle classification, risk assessment, environmental monitoring, and permissible exposure levels complicates commercialization and large-scale implementation. Current regulations frequently treat nanomaterials similarly to their bulk counterparts despite significant differences in reactivity and biological behavior at the nanoscale. Consequently, further research is required to establish standardized safety assessment strategies, environmentally sustainable nanoparticle formulations, and clear regulatory policies that balance technological benefits with environmental and human health protection.

4.2. Real Analysis and Stress Mitigation Study

A generalized approach for real sample analysis in plant stress studies typically involves controlled cultivation of plant material followed by exposure to defined stress conditions and, where relevant, application of stress-mitigating agents such as nanoparticles. Plants are commonly grown under standardized conditions to ensure reproducibility, after which they are divided into experimental groups subjected to abiotic stresses (e.g., salinity, drought, or chemical exposure) and/or protective treatments. In many studies, including our own investigations, metal oxide nanoparticles such as Fe₃O₄, ZnO, CuO, and MgO have been used as representative stress-mitigating agents.

Following the treatment period, plant tissues—most often leaves—are harvested for biochemical analysis. Sample preparation generally involves mechanical homogenization of fresh tissue to release intracellular components, particularly ROS markers such as H₂O₂, which is widely used as an indicator of oxidative stress. The homogenized material is subsequently extracted using aqueous media (e.g., alkaline solutions or buffer systems), and the resulting extracts are clarified by filtration or centrifugation to remove solid residues. This type of preparation yields a representative liquid phase suitable for further analytical measurements.

For CV and DPV measurements, H₂O₂ concentration is typically determined directly from previously established calibration curves by correlating the measured current response with analyte concentration. However, in complex biological matrices such as plant extracts, the standard addition method is frequently employed to minimize matrix-related effects and improve analytical accuracy. In this approach, known amounts of H₂O₂ are added directly to the sample, and the resulting increase in electrochemical response is used to calculate the endogenous H₂O₂ concentration. The obtained values serve as quantitative indicators of oxidative stress intensity in plant tissues.

Beyond analytical quantification, electrochemical determination of H₂O₂ provides a practical tool for evaluating the effectiveness of stress mitigation strategies in plants. Comparative analysis of control, stressed, and treated samples enables direct assessment of oxidative stress dynamics through changes in endogenous H₂O₂ levels. Across numerous studies, nanoparticle-treated plants consistently exhibit lower H₂O₂ accumulation compared to untreated stressed controls, indicating suppression of ROS overproduction and partial restoration of redox balance under stress conditions.

The representative data summarized in

Table 5. Electrochemically determined H₂O₂ concentrations in crop plants under drought, salinity, and herbicide stress with and without metal oxide nanoparticle treatment (MgO, ZnO, Fe₃O₄). The table summarizes representative model data from our previous studies illustrating nanoparticle-assisted stress mitigation.

Crop	Stress	nPs for Mitigation	Morphology	H2O2 Found (µM) Control	H2O2 Found (µM) nPs 50 mg·L−1	H2O2 Found (µM) nPs 100 mg·L−1	Stress H2O2 Found (µM)	H2O2 Found (µM) Stress nPs 50 mg·L−1	H2O2 Found (µM) Stress nPs 100 mg·L−1	Reference
Oat (Avena sativa)	Drought	MgO	Irregular particles, up to 20 nm, composed in clusters up to 200 nm	52	50	3	262	126	98	[108]
Rye (Secale cereale)	Drought	MgO	Irregular particles, up to 20 nm, composed in clusters up to 200 nm	51	60	8	102	75	40	[108]
Rye (Secale cereale)	Salinity	ZnO	Spherical particles, 10–25 nm agglomerated into submicron rice-like clusters	42	58	17	479	154	94	[111]
Barley (Hordeum vulgare)	Salinity	Fe3O4	Spherical particles, up to 10 nm	5	30	6	217	13	3	[110]
Rye (Secale cereale)	Herbicide	−		3	−	−	223	−	−	[176]
Rye (Secale cereale)	Salinity	−		3	−	−	130	−	−	[176]
Crop	Stress	nPs for Mitigation	Morphology	H2O2 Increase Under Stress (vs. Unstressed Control)		H2O2 Mitigation by 50 mg·L−1 nPs (vs. Stressed Control)		H2O2 Mitigation by 100 mg·L−1 nPs (vs. Stressed Control)		Reference
Oat (Avena sativa)	Drought	MgO	Irregular particles, up to 20 nm, composed in clusters up to 200 nm	403.8%		51.9%		62.6%		[108]
Rye (Secale cereale)	Drought	MgO	Irregular particles, up to 20 nm, composed in clusters up to 200 nm	100%		26.5%		60.8%		[108]
Rye (Secale cereale)	Salinity	ZnO	Spherical particles, 10–25 nm agglomerated into submicron rice-like clusters	1040.5%		67.8%		80.4%		[111]
Barley (Hordeum vulgare)	Salinity	Fe3O4	Spherical particles, up to 10 nm	4240.0%		94.0%		98.6%		[110]
Rye (Secale cereale)	Herbicide	−				−		−		[176]
Rye (Secale cereale)	Salinity	−				−		−		[176]

are based on our previously performed experiments and are included here as a model system for comparative analysis of electrochemical H₂O₂ monitoring under different stress and nanoparticle-treatment conditions. The table compiles results obtained for several crop species exposed to drought, salinity, and herbicide stress in combination with metal oxide nanoparticle treatments. Because these experiments were carried out using relatively consistent analytical and experimental protocols, they provide a useful framework for evaluating trends in oxidative stress development and mitigation efficiency across different plant systems.

Abiotic stress resulted in a substantial increase in H₂O₂ accumulation compared with unstressed controls. Depending on the crop species and stress type, H₂O₂ levels increased by approximately 100–4240%, corresponding to a 2.0–43.4-fold elevation. The most pronounced increase was observed in salinity-stressed barley, whereas drought-stressed rye exhibited the smallest relative increase in H₂O₂ content.

A comparison of the nanoparticle systems summarized in Table 5 suggests a relationship between particle size, aggregation state, and stress-mitigation efficiency. MgO nanoparticles formed relatively large agglomerates (up to ~200 nm) and exhibited the lowest mitigation efficiency, resulting in H₂O₂ reductions of 26.5–62.6%. In contrast, ZnO nanoparticles, consisting of smaller primary particles (10–25 nm), produced substantially greater reductions in H₂O₂ accumulation (67.8–80.4%). The highest mitigation efficiency was observed for Fe₃O₄ nanoparticles, which were represented by the smallest and most uniformly dispersed particles with minimal aggregation, leading to H₂O₂ reductions of up to 94.0–98.6%. These observations suggest that decreasing particle size and reducing aggregation improve nanoparticle–plant interactions and enhance oxidative stress mitigation. In addition, all investigated nanoparticle systems exhibited a clear dose-dependent response, with 100 mg·L⁻¹ treatments consistently producing greater reductions in H₂O₂ levels than 50 mg·L⁻¹ treatments, indicating that mitigation efficiency increased with nanoparticle concentration within the studied range. The data summarized in

Table 6. Literature comparison of nanoparticle-assisted stress mitigation in plants based on changes in H₂O₂ accumulation under different abiotic stress conditions.

Plant (Crop), Stress	Nanoparticle (Formulation) & Conc. (Application)	H2O2 Detection Method	Reported % Decrease in H2O2 (NPs vs. Stressed Control)	Reference
Rapeseed (Brassica Napus), salt stress	PNC (CeO2; 0.05 mM ≈ 5.6 mg/L), foliar; PMO (Mn3O4; 300 mg/L), foliar	H2O2 kit (A04-1-1 and 20210903)	44.5% (CeO2 PNC) or 38.6% (Mn3O4 PMO)	[18]
Cotton (Gossypium hirsutum L.), salt stress	PNC (CeO2 polyacrylic acid coated), 0.1 mL, 0.9 mM, foliar	Ti(SO4)2 precipitation method	79%	[19]
Rice (Oryza sativa L.), salt stress	SiO2 SNPs, 120 mg·L−1, foliar spray	Modified Góth method	8% to 31%	[147]
Sorghum (Sorghum bicolor), drought stress	CeO2 nPs, foliar spray 10 mg·L−1	Spectrophotometric H2O2 assay	36%	[8]
Maize (Zea mays L.), drought stress	ZnSe QDs, 20 mg·L−1, foliar spray	Patterson method [177]	23%	[178]
Tomato (Solanum lycopersicum), salt stress	ZnO nPs, foliar spray 75 mg·L−1 and 150 mg·L−1	KI colorimetric H2O2 assay	41.1% for 75 mg·L−1 nPs and 51.8% for 150 mg·L−1 nPs	[7]
Wheat (Triticum aestivum), salt stress	Ag nPs, foliar spray—300 ppm	Biochemical colorimetric assay	56%	[179]
Moldavian balm (Dracocephalum moldavica L.), salt stress	TiO2 nPs, 0–200 mg·L−1, irrigation	KI spectrophotometry (A390)	20–26%	[20]
Rapeseed (Brassica Napus), salt stress	0.1 mM PNC CeO2, seed priming	H2O2 kit (A04-1-1)	23%	[139]
Tomato (Solanum lycopersicum), drought stress	SeNPs, 25–100 ppm, seed priming	Spectrophotometric H2O2 assay	39.3%	[9]
Maize (Zea mays L.), drought stress	FeO nPs, MnO nPs, CuO nPs 25–100 ppm, seed priming	Biochemical H2O2 assay	23–27% depending on drought level	[140]
Mungbean (Vigna radiata), drought stress	CeO2 nPs, foliar spray 100 mg·L−1	Spectrophotometric H2O2 assay	28%	[180]
Barley (Hordeum vulgare L.), salt stress	TiO2 nPs, 500, 1000 and 2000 mg·kg−1, powder added to the soil	KI spectrophotometry (A390)	25.8–43.1% depending on nPs dose	[145]
Tobaccco (Nicotiana tabacum L.), Cd-induced stress	50 μM Ag nPs suspension, irrigation	Guaiacol method and ultraviolet absorption spectrometry	-	[141]
Rice (Oryza sativa L.), salt stress	TiO2 nPs, 15–60 mg·L−1, water spray	Titanium salt colorimetric method	22.1–24.7%	[146]

provide a comparative overview of literature reports on nanoparticle-assisted mitigation of oxidative stress in plants, with particular emphasis on changes in H₂O₂ content as a biomarker of stress response. The selected studies include different crop species, nanoparticle types, application methods (e.g., foliar spray, seed priming, irrigation), and stress conditions, including salinity, drought, heavy metal exposure, and herbicide-induced stress.

Although the reported efficiencies vary considerably between studies, a general trend can be observed in which metal and metal oxide nanoparticles contribute to suppression of oxidative stress across different crop species and stress models. The observed variability reflects differences in nanoparticle composition, plant species, stress severity, application method, and analytical protocols used for H₂O₂ determination.

Among the investigated nanomaterials, CeO₂-based systems generally exhibit some of the strongest stress-mitigating effects. This trend is likely related to the redox-active nature of cerium oxide nanoparticles, which enables reversible Ce³⁺/Ce⁴⁺ cycling and efficient ROS scavenging. ZnO nanoparticles also demonstrate relatively high mitigation efficiency under salinity and drought stress, particularly in tomato and rye systems, suggesting that Zn-containing nanomaterials may simultaneously contribute to antioxidant regulation and micronutrient supplementation. In contrast, TiO₂-, FeO-, MnO-, and CuO-based systems generally show more moderate reductions in H₂O₂ accumulation, although their effects remain consistently beneficial across different crops.

The comparison further indicates that nanoparticle application strategy strongly influences stress mitigation efficiency. Foliar application is the most frequently employed approach and often produces pronounced reductions in H₂O₂ accumulation, likely due to direct interaction of nanoparticles with leaf tissues and photosynthetically active regions. Seed priming and soil-based treatments also demonstrate positive effects, although the reported responses are generally more variable. These differences suggest that nanoparticle uptake, transport, and localization within plant tissues play an important role in determining the final physiological response.

An additional trend observed across the literature is the dependence of mitigation efficiency on nanoparticle concentration. Several studies report improved stress tolerance only within specific concentration ranges, whereas excessive nanoparticle levels may reduce the beneficial effect or produce inconsistent responses. This observation highlights the importance of careful optimization of nanoparticle dosage and treatment conditions.

Overall, the literature data presented in Table 6 are commonly associated with reduced H₂O₂ accumulation under stress conditions. Despite differences in experimental design and analytical methodology, the collective findings indicate that nanoparticle-assisted approaches represent a promising strategy for modulation of oxidative stress and improvement of plant stress tolerance.

Comparison of the literature data summarized in Table 6 with the results obtained in our studies (Table 5) reveals several common trends as well as some notable differences in mitigation efficiency. In both datasets, nanoparticle treatment is consistently associated with reduced H₂O₂ accumulation under abiotic stress conditions, confirming the general role of nanoparticles in modulation of oxidative stress responses in plants.

The literature data show that most nanoparticle systems reduce H₂O₂ levels within a moderate range, typically corresponding to partial alleviation of oxidative stress. Similar trends are observed in our studies for MgO-treated oat and rye under drought stress, where reductions in H₂O₂ accumulation are comparable to those reported for TiO₂, SiO₂, FeO, MnO, and CuO systems in the literature. ZnO-treated rye in our experiments demonstrates mitigation efficiency comparable to or exceeding many previously reported ZnO- and TiO₂-based systems under salinity stress.

The most pronounced difference is observed for Fe₃O₄-treated barley under salinity stress in Table 5, where the decrease in H₂O₂ accumulation is substantially greater than that reported in most literature studies summarized in Table 6. While CeO₂ nanoparticles in the literature exhibit some of the highest reported mitigation efficiencies, the Fe₃O₄ systems investigated in our studies demonstrate similarly strong or even greater reductions in oxidative stress markers under specific experimental conditions.

Another common feature between Table 5 and Table 6 is the strong dependence of mitigation efficiency on nanoparticle composition and treatment conditions. In both datasets, the effectiveness of stress alleviation varies considerably between nanoparticle types and experimental systems, indicating that nanoparticle-induced stress mitigation is highly system-dependent. Furthermore, both literature reports and our studies demonstrate that increasing nanoparticle concentration frequently enhances the reduction in H₂O₂ accumulation, although the magnitude of improvement differs between materials and crops.

In addition to H₂O₂ monitoring, evaluation of total chlorophyll content is frequently used to assess the physiological state of plants under stress conditions. Because chlorophyll concentration directly reflects the functional integrity of the photosynthetic apparatus, it provides complementary information to oxidative stress measurements. The data summarized in

Table 7. Total chlorophyll content in crop plants subjected to abiotic stress with and without metal oxide nanoparticle treatment. The data were obtained from the same plant samples used for electrochemical H₂O₂ determination (Table 5) and illustrate the relationship between oxidative stress and photosynthetic performance under nanoparticle-assisted stress mitigation.

Crop	Stress	nPs for Mitigation	Total Chlorophyll Control	Total Chlorophyll nPs 50 mg·L−1	Total Chlorophyll nPs 100 mg·L−1	Total Chlorophyll Stress	Total Chlorophyll Stress nPs 50 mg·L−1	Total Chlorophyll Stress nPs 100 mg·L−1	Reference
Oat (Avena sativa)	Drought	MgO	1.1493	2.1490	2.5595	0.5570	1.7450	2.0653	[108]
Rye (Secale cereale)	Drought	MgO	2.3035	2.9008	2.3889	1.2363	2.9894	2.7833	[108]
Rye (Secale cereale)	Salinity	ZnO	1.0689	1.3428	1.3506	0.5280	0.9424	1.0303	[111]
Barley (Hordeum vulgare)	Salinity	Fe3O4	0.5121	0.4976	0.5568	0.3478	0.6167	0.7326	[110]
Rye (Secale cereale)	Herbicide	−	0.6375	−	−	0.4579	−	−	[176]
Rye (Secale cereale)	Salinity	−	0.6375	−	−	0.4931	−	−	[176]
Crop	Stress	nPs for Mitigation	Chlorophyll Decrease Under Stress (vs. Unstressed Control)		Chlorophyll Mitigation by 50 mg·L−1 nPs (vs. Stressed Control)		Chlorophyll Mitigation by 100 mg·L−1 nPs (vs. Stressed Control)		Reference
Oat (Avena sativa)	Drought	MgO	−59%		213%		271%		[108]
Rye (Secale cereale)	Drought	MgO	−46%		142%		125%		[108]
Rye (Secale cereale)	Salinity	ZnO	−51%		79%		95%		[111]
Barley (Hordeum vulgare)	Salinity	Fe3O4	−32%		77%		111%		[110]
Rye (Secale cereale)	Herbicide	−	28%		−		−		[176]
Rye (Secale cereale)	Salinity	−	23%		−		−		[176]

were obtained from the same plant samples used for electrochemical H₂O₂ determination in Table 6, enabling direct comparison between oxidative stress intensity and photosynthetic performance under identical experimental conditions.

The results show a consistent decrease in chlorophyll content under drought, salinity, and herbicide stress, accompanied by increased H₂O₂ accumulation. Elevated H₂O₂ levels are associated with oxidative damage to chloroplast membranes, destabilization of pigment–protein complexes, and inhibition of chlorophyll biosynthesis pathways. In addition, excessive ROS accumulation promotes lipid peroxidation and degradation of photosystem components, leading to reduced photosynthetic efficiency and accelerated chlorophyll loss. These processes are reflected in the inverse relationship observed between H₂O₂ concentration and chlorophyll content across all investigated plant systems.

In nanoparticle-treated samples, chlorophyll levels are generally higher than in untreated stressed plants, indicating partial preservation of photosynthetic activity. This effect is observed for all investigated nanoparticle systems, including MgO, ZnO, and Fe₃O₄, although the magnitude of recovery depends on plant species, stress type, and nanoparticle concentration. Overall, the combined H₂O₂ and chlorophyll data demonstrate that reduction in oxidative stress is accompanied by improved maintenance of the photosynthetic system under nanoparticle treatment.

Among the investigated systems, drought stress caused the most pronounced reduction in chlorophyll content, particularly in oat and rye, indicating high sensitivity of these crops to oxidative damage under water deficit conditions. Treatment with metal oxide nanoparticles increased chlorophyll content in all investigated systems, demonstrating partial restoration of photosynthetic activity. The highest mitigation efficiency was observed for MgO nanoparticles under drought stress, resulting in chlorophyll recovery of up to 213–271% relative to stressed controls. ZnO nanoparticles in salinity-stressed rye and Fe₃O₄ nanoparticles in salinity-stressed barley also produced substantial chlorophyll restoration, with mitigation efficiencies reaching 79–95% and 77–111%, respectively. These results indicate that nanoparticle treatment effectively counteracts stress-induced chlorophyll loss, while the magnitude of recovery depends on nanoparticle composition, plant species, and stress conditions. Furthermore, all investigated nanoparticle systems exhibited a clear concentration-dependent response, with higher nanoparticle concentrations generally providing greater chlorophyll recovery. Unlike the H₂O₂ mitigation data, the chlorophyll results do not reveal a simple correlation between nanoparticle size and stress-mitigation efficiency. Although smaller and less aggregated nanoparticles generally exhibited stronger reductions in H₂O₂ accumulation, the highest chlorophyll recovery was observed for MgO nanoparticles, which formed the largest agglomerated structures among the investigated systems. In contrast, ZnO and Fe₃O₄ nanoparticles, despite their smaller size and higher dispersion, produced more moderate improvements in chlorophyll content. These observations suggest that chlorophyll restoration is governed not only by nanoparticle size and morphology but also by material composition and its specific physiological role in plant metabolism. In particular, Mg-containing nanoparticles may contribute directly to chlorophyll biosynthesis because magnesium is the central metal ion in the chlorophyll molecule. Nevertheless, all investigated nanoparticle systems exhibited a clear dose-dependent response, with higher nanoparticle concentrations generally resulting in greater chlorophyll recovery under stress conditions.

The relationship between oxidative stress and photosynthetic activity in plants subjected to different treatments is illustrated in Figure 4, where solid lines represent H₂O₂ content and dashed lines correspond to chlorophyll concentration. The data clearly demonstrate an inverse correlation between these two parameters across all studied plant systems and treatment conditions.

Under control conditions, plants generally exhibit lower H₂O₂ concentrations and higher chlorophyll content compared to stressed samples. Exposure to abiotic stress results in increased H₂O₂ accumulation accompanied by reduced chlorophyll levels, indicating enhanced oxidative stress and impairment of the photosynthetic apparatus.

Across the investigated systems, drought and salinity stress produced the highest H₂O₂ concentrations together with the lowest chlorophyll values, particularly in rye and oat samples. The application of metal oxide nanoparticles, including MgO, ZnO, and Fe₃O₄, generally reduced H₂O₂ accumulation and partially restored chlorophyll content compared to untreated stressed plants.

Among the investigated treatments, ZnO nanoparticles in rye and MgO nanoparticles in oat and rye were associated with noticeable reductions in H₂O₂ levels together with improved chlorophyll content under stress conditions.

Table 8. Literature comparison of nanoparticle-assisted stress mitigation in plants based on relative changes in chlorophyll under different abiotic stress conditions.

Plant (Crop), Stress	Reported Chlorophyll Decrease Under Stress (vs. Unstressed Control)	Nanoparticle (Formulation) & Conc. (Application)	Reported Chlorophyll Mitigation by NPs (vs. Stressed Control)	Reference
Rice (Oryza sativa cv. Pusa Basmati 1), Salinity	Total chlorophyll −18.15% (reported)	Se nP 10 mg·kg−1, soil + ZnO nP 20 mg·kg−1, soil	Total chlorophyll +35.38% (reported)	[138]
Bell pepper (Capsicum annuum), Salinity	Total chlorophyll −55% (reported)	Se NPs 10 and 50 mg·L−1, Si NPs 200 and 1000 mg·L−1, Cu NPs 100 and 500 mg·L−1, soil	From +52% to +72% depending on stress conditions and nanoparticle concentration (reported)	[181]
Canola (Brassica napus), Drought	Not reported	Fe nPs (1.5 and 3 mg·L−1), soil	Total chlorophyll +24.43% (reported)	[136]
Soybean (Glycine max), Drought	Chlorophyll a −47.7% and Chlorophyll b −41.4% (reported)	ZnO nPs, foliar spray	Chlorophyll a +33.1% and Chlorophyll b +20.7% (reported)	[133]
Apple (Malus domestica cv. Red Delicious on M9), Drought	Total chlorophyll −19.9% (reported)	CeO2 NPs 50 or 100 mg·L−1, foliar spray	Total chlorophyll +7.14% and +15.88% depending on nPs concentration (reported)	[134]
Mungbean (Vigna radiata), Cadmium stress	Total chlorophyll −26.48% (reported)	SeNP 75 mg·L−1, foliar spray,	Best level of foliar applied SeNPs 3.30 mg·g−1 of total chlorophyll (reported in text), + 235% (calculated from graph)	[135]
Pepper (Capsicum annum L.), Salinity	Total chlorophyll −39% and −50% depending on stress level (calculated from table data)	C QDs-GO, foliar spray	+40% and +54,8% depending on stress level (calculated from table data)	[153]
Barley (Hordeum vulgare), Salinity	Chlorophyll a −42.3% and Chlorophyll b −37.5% (calculated from graph)	Cu nPs 50 mg·L−1, soil	Chlorophyll a 80% and Chlorophyll b 62% (calculated from graph)	[137]
Faba bean (Vicia faba), Salinity	Total chlorophyll −43.4%	ZnO nPs (50 mg·L−1 and 100 mg·L−1), foliar spray	Total Chlorophyll +17.5% and −20.5% depending on nanoparticle concentration	[143]
Tomato (Lycopersicon esculentum Mill.), Salinity	SPAD chlorophyll −29.2% (calculated from graph)	ZnO nPs (10 mg·L−1, 50 mg·L−1, 100 mg·L−1), foliar	SPAD chlorophyll 23.5%, 70.6% and 35.3% (calculated from graph)	[182]
Cucumber (Cucumis sativus L.), Drought	Total chlorophyll −71.4% (calculated from graph)	ZnO (25 mg·L−1 and 100 mg·L−1), foliar	+75% and +275% depending on nanoparticle dose (calculated from graph)	[144]

summarizes the literature data on nanoparticle-assisted mitigation of abiotic stress in plants using chlorophyll content as a physiological indicator. The table compares different crops, stress types, nanoparticle formulations, application methods, reported chlorophyll decrease under stress relative to unstressed controls, and chlorophyll recovery after nanoparticle treatment relative to stressed controls.

The literature data demonstrate a consistent decrease in chlorophyll content under abiotic stress conditions, confirming stress-induced impairment of the photosynthetic apparatus. Drought and salinity stress generally produced the strongest negative effects on chlorophyll accumulation across the investigated crops.

Nanoparticle treatment was generally associated with increased chlorophyll content in stressed plants, indicating mitigation of stress-induced damage and partial preservation of photosynthetic activity. Although the magnitude of the response varied considerably between studies, the overall trend remained consistent across different crops, stress models, and application methods.

Among the investigated nanomaterials, ZnO- and Se-based nanoparticles generally demonstrated the strongest chlorophyll-restoring effects, particularly under drought and salinity stress. Cu nanoparticles also showed strong mitigation effects in barley under salinity conditions. In contrast, CeO₂ nanoparticles produced more moderate chlorophyll recovery despite their well-known antioxidant properties.

The data further indicate that mitigation efficiency depends strongly on nanoparticle composition, plant species, stress severity, and treatment conditions. In several studies, increasing nanoparticle concentration enhanced chlorophyll recovery, suggesting a concentration-dependent mitigation effect within the investigated ranges. Foliar application was one of the most frequently used approaches and was commonly associated with pronounced chlorophyll preservation under stress conditions.

Comparison of the literature data summarized in Table 8 with the results obtained in our studies for unified experimental conditions (Table 7) reveals several consistent trends regarding the relationship between abiotic stress, chlorophyll degradation, and nanoparticle-assisted stress mitigation. In both datasets, stress conditions were associated with reduced chlorophyll content, confirming stress-induced impairment of photosynthetic activity.

Both the literature reports and our experimental results demonstrate that nanoparticle treatment alleviates stress-induced chlorophyll loss and contributes to preservation of the photosynthetic apparatus. The observed chlorophyll recovery is consistent with the simultaneous reduction in H₂O₂ accumulation discussed in previous sections, supporting the relationship between oxidative stress suppression and improved photosynthetic stability.

In both datasets, mitigation efficiency strongly depends on nanoparticle composition, plant species, and stress type. ZnO-based systems repeatedly demonstrate pronounced protective effects under drought and salinity stress, while drought and salinity generally produce the strongest chlorophyll decrease across the investigated crops. Both literature data and our results also indicate a concentration-dependent effect, where higher nanoparticle concentrations within the investigated range often improve chlorophyll preservation.

At the same time, the nanoparticle systems investigated in our studies frequently demonstrate mitigation efficiencies comparable to or exceeding many literature reports summarized in Table 8. MgO-, ZnO-, and Fe₃O₄-based systems showed particularly strong chlorophyll preservation together with reduced H₂O₂ accumulation, indicating effective alleviation of oxidative stress.

Figure 5 summarizes the generalized mechanism of nanoparticle-assisted stress mitigation discussed throughout this review.

Abiotic stress conditions promote excessive ROS generation and H₂O₂ accumulation, leading to oxidative damage, chlorophyll degradation, and reduced photosynthetic activity. Metal oxide nanoparticles contribute to ROS scavenging, activation of antioxidant defense systems, improved ion homeostasis, and stabilization of cellular metabolism, resulting in reduced H₂O₂ accumulation, preservation of chlorophyll content, and improved stress tolerance.

5. Perspectives and Critical Outlook

Recent progress in nanostructured electrochemical sensors and nanoparticle-assisted stress mitigation demonstrates considerable potential for plant stress monitoring; however, several methodological and practical limitations remain unresolved. One of the major challenges is the limited comparability between independent studies. Significant variations in nanomaterial synthesis procedures, electrode fabrication methods, electrolyte composition, electrochemical parameters, and plant treatment protocols frequently complicate direct evaluation of sensor performance and mitigation efficiency. Even when similar nanomaterials are investigated, differences in morphology, crystallinity, defect density, or measurement conditions can lead to substantial discrepancies in reported analytical characteristics and biological responses.

Another important issue concerns the complexity of plant oxidative stress itself. Most studies focus primarily on H₂O₂ as a single stress biomarker, although oxidative stress involves interconnected ROS signaling pathways, antioxidant enzymes, osmotic regulation, and phytohormonal responses. Consequently, interpretation of H₂O₂ data alone may not fully reflect the physiological state of the plant. Integration of electrochemical H₂O₂ detection with complementary biochemical or physiological markers may therefore provide a more reliable assessment of stress development and mitigation efficiency.

From the electrochemical perspective, non-enzymatic sensors offer advantages in stability and structural simplicity, but their selectivity in complex biological matrices remains an important limitation. Plant extracts contain numerous electroactive compounds, including phenolics, organic acids, and reducing metabolites, which may influence the electrochemical response. Further optimization of electrode architecture, surface functionalization, and measurement protocols is therefore required to improve selectivity and long-term operational stability under real sample conditions.

For nanoparticle-assisted mitigation approaches, the mechanisms governing nanoparticle–plant interactions remain insufficiently understood. Although many studies report reduced H₂O₂ accumulation and partial restoration of photosynthetic activity after nanoparticle treatment, the relative contribution of ROS scavenging, antioxidant activation, nutrient delivery, and signaling modulation remains difficult to distinguish experimentally. In addition, nanoparticle uptake, translocation, and accumulation strongly depend on particle size, surface chemistry, plant species, and application strategy, leading to highly system-dependent responses.

Potential environmental and toxicological aspects also require further investigation. Beneficial effects are commonly observed within relatively narrow concentration ranges, whereas excessive nanoparticle exposure may induce phytotoxicity or disturb cellular homeostasis. Moreover, the long-term behavior of nanoparticles in soil–plant systems, including persistence, transformation, and interactions with microbial communities, remains insufficiently characterized. These factors may become particularly important for large-scale agricultural applications.

Future studies should therefore focus on improving methodological standardization and establishing more comparable experimental frameworks for both electrochemical sensing and plant stress evaluation. Greater emphasis should also be placed on real-time and minimally invasive monitoring approaches capable of tracking dynamic changes in oxidative stress directly in living plant tissues. Integration of nanostructured electrochemical sensors with microfluidic systems, wireless data acquisition, or precision agriculture technologies may further improve applicability under field conditions. At the same time, systematic investigation of nanoparticle safety, transport behavior, and long-term environmental impact will be necessary before broader agricultural implementation can be considered.

6. Conclusions

This review demonstrates that metal oxide nanostructures are highly effective materials for electrochemical H₂O₂ sensing and plant oxidative stress monitoring. Analysis of the reported sensors showed that the analytical performance of metal oxide-based systems is frequently comparable to that of other nanomaterials, while metal oxides incorporated into hybrid and heterostructured architectures often provide superior sensing characteristics, combining high sensitivity, low detection limits, and excellent operational stability. The collected literature further confirms that metal oxides remain among the most versatile and widely investigated materials for non-enzymatic H₂O₂ detection. Importantly, metal oxide-based sensors have been successfully applied for H₂O₂ determination not only in standard solutions but also in complex plant samples with chemically challenging matrices.

Among the available fabrication methods, hydrothermal synthesis emerges as one of the most versatile approaches for the preparation of metal oxide sensing materials. In comparison with other synthesis techniques, it enables the formation of the broadest range of morphologies, including nanosheets, nanowires, nanopetals, nanorods, and hierarchical architectures, while simultaneously providing strong adhesion of the active material to the electrode surface. The reviewed studies clearly demonstrate that nanostructure morphology plays a critical role in determining sensor performance through its influence on active surface area, charge-transfer resistance, and electrocatalytic activity.

The analyzed plant studies confirm that H₂O₂ is a reliable biomarker of oxidative stress induced by drought, salinity, herbicide exposure, and other abiotic stress factors. Representative datasets obtained under comparable experimental conditions revealed that stressed plants exhibited several-fold increases in H₂O₂ accumulation, reaching up to a 43-fold elevation relative to unstressed controls, accompanied by substantial reductions in chlorophyll content. A consistent inverse relationship between H₂O₂ concentration and chlorophyll content was observed, indicating that increased oxidative stress is directly associated with deterioration of the photosynthetic apparatus.

Metal oxide nanoparticles effectively mitigated oxidative stress by reducing H₂O₂ accumulation in a concentration-dependent manner. Smaller and less aggregated nanoparticles generally exhibited higher ROS-scavenging efficiency, whereas chlorophyll recovery was influenced predominantly by nanoparticle composition rather than particle size. Overall, higher nanoparticle concentrations provided greater reductions in oxidative stress and improved preservation of photosynthetic activity.

Overall, the combined evidence demonstrates that metal oxide nanomaterials can serve both as highly effective electrochemical sensing platforms and as active agents for oxidative stress mitigation in plants. The integration of nanostructured metal oxide sensors with nanoparticle-assisted stress management strategies represents a promising approach for plant diagnostics, real-time stress monitoring, and sustainable crop production.