Engineering biochar through surface oxygenation: a green approach for sustainable environmental applications

Engineering biochar through surface oxygenation: a green approach for sustainable environmental applications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Engineering biochar through surface oxygenation: a green approach for sustainable environmental applications Lee Ziwei, Rosazlin Abdullah, Jamilah Syafawati Yaacob, Noor Sharina Mohd Rosli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7897807/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Mar, 2026 Read the published version in Clean Technologies and Environmental Policy → Version 1 posted 12 You are reading this latest preprint version Abstract Hydrogen peroxide (H₂O₂) oxidation has emerged as a promising and sustainable strategy to enhance the surface chemistry and functional performance of biochar due to its environmentally friendly nature compared to other modification methods. This study systematically investigated the effect of controlled H₂O₂ oxidation (1–30%) on palm kernel shell (PKS) biochar (termed OxyAChar), evaluating its physicochemical characteristics, sorption behavior, water retention, and thermal stability. The results revealed that moderate oxidation (3% H₂O₂) produced OxyAChar-3 with the highest BET surface area (520.21 m²·g⁻¹ micropores, 360.48 m²·g⁻¹ mesopores), improved pore connectivity, and enriched oxygen-containing functional groups. These modifications significantly enhanced methylene blue sorption capacity and water retention ability biochar. The observed effects were attributed to persistent free radicals (PFRs) on the biochar surface, which catalyzed H₂O₂ decomposition into reactive •OH and •OOH radicals, promoting delignification and surface oxygenation. However, excessive oxidation (>3%) disrupted structural integrity of pores and reduced functional group density, thereby decreasing sorption and water retention efficiency of biochar. Comparative analysis with existing literature confirmed that optimal H₂O₂ concentrations are both feedstock- and pyrolysis-dependent. These insights highlight the potential of machine learning-assisted biochar design to predict ideal oxidation parameters, minimize experimental trial-and-error, and accelerate the development of high-performance, sustainable biochar materials. Overall, this study provides a mechanistic and practical framework for the rational design of surface-oxidized biochar for future applications in soil remediation, water conservation, and long-term carbon sequestration. biochar hydrogen peroxide surface oxidation oxygen functional group enhanced sorption capacity potential soil remediation Figures Figure 1 Figure 2 Figure 3 Figure 4 Highlights H₂O₂ is green oxidant that enhances biochar with surface oxygenation. OxyAChar has lower pH and EC, indicating enriched oxygen functional groups. OxyAChar-3 has improved mesopore (+35.4%) micropore (+28.8%) surface area. OxyAChar-3 demonstrates superior MB removal efficiency (90.11%) and WHC (168.70%). OxyAChar is promising soil enhancer that improves nutrient and water retention. Machine learning can be applied in the future to precisely design high-performance oxidized biochar. 1.0 Introduction Oil palm ( Elaeis guineensis ) is the world’s most important oil crop, contributing approximately 40% of global vegetable oil production, with over three billion people relying on it daily (Murphy et al., 2021 ). Malaysia, the second-largest producer, generated 16.9 million tons of palm oil in 2024 (MIDA, 2024 ). However, only ~ 10% of the plant contributes to oil production; the remaining 90% including fronds, trunks, shells, and empty fruit bunches forms an underutilized biomass waste stream (Abdullah & Sulaiman, 2013 ) [Supplementary Information (SI)] . Annually, 4.3 million tons of palm kernel shell (PKS) are produced (Tan et al., 2008 ). Rather than allowing this materials going to waste, researchers recognize the potential of PKS as a valuable raw material for biochar production due to its high lignin and carbon content (Daud & Ali, 2004 ; Guo et al., 2008 ). Biochar, a carbon rich product derived from biomass, has a long history of use, tracing back 2500 years ago to the “Terra Preta” soils of the Amazon Basin. These soils are renowned for their exceptional fertility and organic carbon content (Semida et al., 2019 ). Despite extensive research, conventional biochar often exhibit limited surface area and insufficient functional groups, constraining their performance in application (e.g. catalysis, composting, pollutant removal, and soil remediation) (Li et al., 2020 ). Consequently, research on biochar modification has expanded exponentially, with publications in the Web of Science Core Collection increasing from only two in 2010 to nearly one thousand in 2024. This rapid growth reflects the growing global emphasis on tailoring biochar properties for targeted environmental applications (Tomczyk et al., 2023 ). Among the various modification techniques, physical activation effectively enhances biochar porosity but often requires high energy input and extended processing times. In contrast, chemical oxidation offers greater control over surface functionality and can achieve activation at relatively lower temperatures. However, conventional chemical oxidants may introduce secondary pollutants, generate corrosive byproducts, or increase production costs (Tomczyk et al., 2023 ). Therefore, identifying greener oxidants remains a key challenge in sustainable biochar engineering. Hydrogen peroxide (H 2 O 2 ) stands out as a promising chemical for biochar modification due to its low cost and environmentally friendly nature as a green oxidant. H 2 O 2 decomposes into water and oxygen, leaving no residual compounds that could interfere with the performance of biochar in subsequent applications (Huff & Lee, 2016 ; Zhang et al., 2021 ). Through controlled oxidation, H 2 O 2 removes pore-blocking residues and introduces oxygenated functional groups such as hydroxyl and carboxyl moieties (Alves et al., 2021 ; Amin et al., 2020 ; Zuo et al., 2016 ). These structural and chemical modifications collectively increase surface area, porosity, and charge density of biochar, resulting in enhanced sorption and water retention capacities (Tan et al., 2019 ; Zuo et al., 2016 ). While numerous studies have examined H₂O₂-based modification of biochar derived from wood, sludge, and other agricultural residues, the oxidation behavior of palm kernel shell (PKS) biochar, a high-ash, lignin-rich biomass abundant in tropical regions remains largely unexplored. Understanding how such complex feedstocks respond to oxidative treatment is crucial for developing sustainable, high-performance biochar tailored to diverse environmental applications. Therefore, this study systematically investigates the influence of controlled H₂O₂ (1%, 3%, 10%, 20%, and 30%) on the physicochemical properties and sorption performance of PKS biochar. Specifically, it aims to: identify the relationship between oxidation intensity and key structural attributes, including surface functionality, porosity, pH, electrical conductivity (EC) and thermal stability. determine how these transformations govern methylene blue adsorption and water retention performance. The outcomes establish a mechanistic and practical foundation for green biochar engineering and underscore the potential of machine learning-assisted optimization to precisely predict oxidation conditions, prioritize influential physicochemical parameters, and accelerate the design of high-performance biochar for soil, water, and carbon management applications. 2.0 Materials and methods 2.1 Materials procurement and collection All chemicals used in this study were of analytical grade. Methylene blue (MB; C₁₆H₁₈N₃SCl) and hydrogen peroxide (H₂O₂, 30% w/w) were obtained from Merck (Malaysia). Palm kernel shell (PKS) biochar was obtained from the Malaysian Palm Oil Board (MPOB, Bandar Baru Bangi, Selangor, Malaysia). The biochar was ground and sieved through a 0.5–1.0 mm mesh prior to activation. 2.2 Preparation of biochar Biochar activation using H₂O₂ was performed following the methods of Chemerys et al. ( 2020 ), Huff and Lee ( 2016 ), and Sizmur et al. ( 2017 ) with minor modifications. Briefly, PKS biochar was mixed with H₂O₂ solutions of 1%, 3%, 10%, 20%, and 30% (v/v) at a solid-to-liquid ratio of 1:20 (w/v) in 50 mL glass beakers and agitated at 110 rpm for 2 h on a platform shaker. A control sample was prepared using distilled water instead of H₂O₂. After treatment, the samples were filtered, rinsed thoroughly with distilled water, and oven-dried at 105°C overnight. The resulting modified biochar were designated as OxyAChar-1, OxyAChar-3, OxyAChar-10, OxyAChar-20, and OxyAChar-30, while the untreated sample was designated as Biochar. 2.3 Characterization of biochar The pH and electrical conductivity (EC) of biochar samples were determined following Singh et al. ( 2017 ).. For pH, biochar was suspended in distilled water at a ratio of 1:20 (w/v), while EC was measured at a 1:5 (w/v) ratio. Mixtures were shaken at 240 rpm for 1 h and allowed to settle for 30 min before measurement. Thermal stability was analyzed using a Thermogravimetric/Differential Thermal Analyzer (TG/DTA; PerkinElmer TGA6) at a heating rate of 20°C min⁻¹ from 30 to 900°C under N₂ flow (20 mL min⁻¹). Surface functional groups were characterized using Fourier Transform Infrared Spectroscopy (FTIR; Thermo Scientific Nicolet™ Summit) over 550–4000 cm⁻¹. Morphological features were observed via Field Emission Scanning Electron Microscopy (FESEM; Hitachi SU8220). Specific surface area (SSA) was determined by N₂ adsorption–desorption analysis at 77 K using the Brunauer–Emmett–Teller (BET) method (Sorptometric-1990 Analyzer). 2.4 Methylene Blue (MB) Sorption The sorption capacity of biochar samples was evaluated using methylene blue (MB; C₁₆H₁₈N₃SCl) as a model organic dye, following Baharim et al. ( 2022 ) with minor modifications. 1 g of biochar was added to 50 mL of 10 mg L⁻¹ MB solution (pH ≈ 7) and agitated at 120 rpm for 48 h. After equilibrium, supernatants were collected, and absorbance was measured at 665 nm using a UV–Vis spectrophotometer (Shimadzu Mini 1240). The sorption capacity (Qₑ, mg g⁻¹) and removal efficiency (R, %) were calculated using Eqs. ( 1 ) and ( 2 ): $$\:{Q}_{e}=\:\frac{\left({C}_{o}-{C}_{e}\right)V}{W}$$ 1 $$\:R=\:\frac{\left({C}_{o}-{C}_{e}\right)}{{C}_{o}}\:x\:100\text{\%}$$ 2 where \(\:{C}_{0}\) and \(\:{C}_{e}\) are the MB concentrations (mg L⁻¹) at initial and equilibrium, \(\:V\) is the solution volume (L), and \(\:W\) is the mass of biochar (g). 2.5 Water-Holding Capacity (WHC) Water-holding capacity (WHC) was determined following Hien et al. ( 2021 ) and Mimmo et al. ( 2014 ). Approximately 2 g of oven-dried biochar (100°C, overnight) was soaked in a known volume of distilled water for 2 h to ensure full saturation, followed by free drainage for 2 h. WHC was calculated as: $$\:WHC\:=\:\frac{{W}_{w}{-\:W}_{d}}{{W}_{d}}$$ 3 where \(\:{W}_{w}\) and \(\:{W}_{d}\) are the wet and dry weights of biochar, respectively. Measurements were conducted in triplicate. 2.6 Statistical Analysis Statistical analyses were performed using IBM SPSS Statistics (v29, IBM Corp., USA). One-way ANOVA was conducted to assess differences among treatments, followed by Duncan’s post hoc test at a 95% confidence level (p < 0.05). Results are expressed as mean ± standard error. FTIR spectral data were processed and visualized using Spectragryph 1.2 (Menges, Germany). The integrated area under characteristic peaks was obtained using the built-in “Integrate Area Under Curve” function to compare the relative intensity of surface functional groups. Thermogravimetric (TG/DTA) data were analyzed and plotted using OriginPro 2025 (OriginLab Corp., USA) to determine mass loss regions and thermal stability profiles. 3.0 Results 3.1 Physicochemical properties The basic physicochemical characteristics of pristine and surface-oxidized PKS biochar (OxyAChar) are summarized in Table 1 . The pristine biochar exhibited a near-neutral pH of 7.36, whereas H₂O₂ oxidation progressively acidified the surface, reducing the pH to 6.03–6.79, with OxyAChar-3 showing the lowest pH value (p < 0.05). This acidification is likely attributable to the introduction of oxygen-containing acidic functional groups, such as carboxyl and hydroxyl moieties, on the biochar surface (Huff & Lee, 2016 ). Similarly, electrical conductivity (EC) decreased nonlinearly by 30–37% in all oxidized samples relative to the pristine biochar (30.4 µS·cm⁻¹). Table 1 Physicochemical properties (pH and electrical conductivity, EC) of pristine and H₂O₂-modified PKS biochar (OxyAChar) prepared using different H₂O₂ concentrations (1%, 3%, 10%, 20%, and 30%) Treatment pH EC (µS·cm⁻¹) Biochar 7.36 ± 0.086 a 30.4 ± 0.529 a OxyAChar-1 6.26 ± 0.023 e 19.4 ± 0.503 b OxyAChar-3 6.03 ± 0.049 d 19.1 ± 0.819 b OxyAChar-10 6.46 ± 0.038 c 20.13 ± 0.384 b OxyAChar-20 6.58 ± 0.081 c 20.67 ± 0.318 b OxyAChar-30 6.79 ± 0.068 b 21.30 ± 0.819 b Note: OxyAChar- x refers to PKS biochar oxidized using x % (v/v) H₂O₂ solution. Values represent means of three replicates ± standard error. Means within the same column followed by different letters are significantly different (P < 0.05) based on one-way ANOVA followed by Tukey’s HSD test. 3.2 Thermal stability According to the method of Mitchell et al. ( 2013 ), the initial weight loss observed between 30 and 105°C corresponds to the release of residual moisture. The results show that moisture content ranged from 6.77% in OxyAChar-30 to 12.19% in OxyAChar-3, with the control biochar exhibiting an intermediate value of 8.80% as shown in Fig. 1 . Across the full thermal degradation range, total weight loss reflects the proportion of volatile and labile components in the biochar, providing an indication of its relative thermal stability (Nusrat Aman et al., 2023 ). All oxidized biochar demonstrated enhanced thermal stability, with total weight loss decreasing from 44.15% in the pristine sample to 26.8–37.6% in OxyAChar. 3.3 Surface morphology and porosity FESEM and BET analyses were used to investigate the changes in the pore structure of PKS biochar following H₂O₂ oxidation. The pristine biochar exhibited a compact, irregular surface with poorly developed pores (Fig. 2 a). Mild oxidation (1–3% H₂O₂) effectively etched the surface and removed loosely bound organic matter, exposing existing pores, enhancing porosity, and yielding a smoother morphology, as indicated in Fig. 2 b–c. Among the samples, OxyAChar-3 exhibited the most developed pore structure, with the highest micropore surface area (520.21 m²·g⁻¹), mesopore surface area (360.48 m²·g⁻¹), and total pore volume (0.182 cm³·g⁻¹) ( Table 2 ) . Beyond this optimal oxidation level (≥ 10%), the well-defined pores deteriorated, with ruptured walls and irregular surfaces, particularly at 30%, as shown in Fig. 2 f. Consequently, the mesopore surface area (294.74 m²·g⁻¹) and total pore volume (0.150 cm³·g⁻¹) decreased to values approaching those of the pristine biochar (279.91 m²·g⁻¹ and 0.149 cm³·g⁻¹, respectively). Table 2 Textural characteristics of pristine and H₂O₂-modified PKS biochar, including micropore surface area (m² g⁻¹), mesopore surface area (m² g⁻¹), and total pore volume (cm³ g⁻¹) at different oxidant concentrations (1%, 3%, 10%, 20%, and 30%) Treatment Micropore surface area (m² g⁻¹) Mesopore surface area (m² g⁻¹) Total pore volume (cm 3 /g) Biochar 384.12 279.91 0.149 OxyAChar-1 399.67 321.68 0.163 OxyAChar-3 520.21 360.48 0.182 OxyAChar-10 401.15 361.54 0.178 OxyAChar-20 390.65 356.67 0.178 OxyAChar-30 444.14 294.74 0.150 Note: OxyAChar- x refers to PKS biochar oxidized using x % (v/v) H₂O₂ solution. 3.4 Surface functional groups FTIR spectra confirmed significant surface chemical modifications following H₂O₂ oxidation. Key functional groups were identified at 2500–3300 cm⁻¹ (O–H stretching), 3050–3100 cm⁻¹ (C–H stretching), 1735–1750 cm⁻¹ (C = O stretching), 1566–1650 cm⁻¹ (C = C stretching of aromatic rings), and 1085–1150 cm⁻¹ (C–O stretching in alcohols, esters, or ethers). Quantitative integration of the C = C (1566–1650 cm⁻¹) and C–H (2920–2850 cm⁻¹) regions revealed consistent reductions in both functional groups following H₂O₂ oxidation. The pristine biochar exhibited the highest integral values for C = C (96.95) and C–H (51.27) as presented in Table 3 , corresponding to condensed aromatic and aliphatic structures from lignin decomposition (Hwong et al., 2022 ). After modification, these areas decreased to 78.66–84.11 (C = C) and 46.10–49.71 (C–H), reflecting partial oxidation of aromatic and aliphatic carbon structures. The lowest C = C integral (78.66) recorded for OxyAChar-3. Concurrently, the intensified O–H, C-O bands and the emergence of a sharp C = O band in OxyAChar-3 indicate the formation of oxygen functional groups as highlighted in Fig. 3 . Table 3 Integrated band areas of selected FTIR absorption regions for pristine and H₂O₂-oxidized PKS biochar, showing the relative enhancement of oxygen-containing functional groups following surface oxidation. Functional group Wavenumber range (cm⁻¹) Biochar OxyAChar-1 OxyAChar-3 OxyAChar-10 OxyAChar-20 OxyAChar-30 C = C stretching 1566–1650 86.95 78.66 77.23 84.11 84.01 82.87 C-H stretching 3050–3100 51.27 46.1 46.61 49.49 49.71 48..35 Note : Integral values represent relative peak areas calculated from deconvoluted FTIR spectra. OxyAChar- x refers to PKS biochar oxidized using x % (v/v) H₂O₂ solution. 3.5 Adsorption and water retention performance The functional implications of surface oxidation were evaluated using methylene blue (MB) sorption and water-holding capacity (WHC) tests. As summarized in Table 4 , all oxidized samples exhibited enhanced MB sorption compared to the control, increasing from 76.94% in the pristine biochar to a maximum of 90.11% in OxyAChar-3. However, the MB sorption performance plateaued or slightly declined at higher H₂O₂ concentrations (83.50–86.66% for OxyAChar-10 and OxyAChar-20, and 83.54% for OxyAChar-30). A similar trend was observed for WHC, which reached a maximum of 168.7% in OxyAChar-3, compared to 135.46% for the pristine biochar. Biochar oxidized with 10% and 20% H₂O₂ showed no significant difference in WHC relative to the control, whereas a drastic reduction of 69.4% was observed at 30% H₂O₂. Table 4 Methylene blue (MB) removal efficiency (%) and water-holding capacity (WHC, %) of pristine and H₂O₂-modified PKS biochar (OxyAChar) prepared at different H₂O₂ concentrations (1%, 3%, 10%, 20%, and 30%) Treatment MB Removal Efficiency (%), R Water Holding Capacity (%), WHC Biochar 76.94 135.46 OxyAChar-1 89.48 106.64 OxyAChar-3 90.11 168.70 OxyAChar-10 83.50 135.42 OxyAChar-20 86.66 132.31 OxyAChar-30 83.54 41.38 Note: OxyAChar- x refers to PKS biochar oxidized using x % (v/v) H₂O₂ solution. 4.0 Discussion 4.1 Moderate H₂O₂ Oxidation Enhances Biochar Performance The functional implications of H₂O₂ surface oxidation on palm kernel shell (PKS) biochar were evaluated through methylene blue (MB) sorption and water-holding capacity (WHC) experiments. These serve as practical indicators of the environmental and agronomic potential of biochar. MB, a cationic dye with three positively charged nitrogen atoms (Porto et al., 2024 ), is commonly used as a model pollutant to assess sorption capacity and as an indicator of nutrient retention potential of biochar due to its similar charge to essential plant cations (e.g. K⁺, Ca²⁺, Mg²⁺) (Huff & Lee, 2016 ; McNamara, 2023 ). WHC, on the other hand, reflects the hydrophilicity of biochar surfaces and their ability to retain moisture against gravitational forces (Cheatham et al., 2025 ). Both parameters exhibited a nonlinear response to increasing H₂O₂ concentration, with OxyAChar-3 achieving the highest MB sorption (90.11%) and WHC (168.7%). Specifically, MB sorption by OxyAChar-3 increased by 17.22% relative to the control and could retain 1.69 times its dry weight in water. The exceptional MB sorption and WHC of OxyAChar-3 is attributed to the synergistic effect of its enriched surface oxygenated functionalities and optimized pore structure, a relationship further corroborated by BET, FESEM, FTIR, TGA, pH, and EC analyses presented below. BET and FESEM analyses confirmed that moderate oxidation (3% H₂O₂) maximized the development of both micropores (520.21 m²·g⁻¹) and mesopores (360.48 m²·g⁻¹) while maintaining pore connectivity and structural integrity. The well-developed pore network facilitate methylene blue (MB) diffusion and enhance capillary water entrapment by proving readily available sites for pore filling (Zeghioud & Mouhamadou, 2023 ). Beyond improving pore structure, FTIR spectra confirmed the successful oxidation of PKS biochar, with OxyAChar-3 showing the most pronounced signals at 2500–3300 cm⁻¹ (O–H), 1735–1750 cm⁻¹ (C = O), and 1085–1150 cm⁻¹ (C–O), indicative of hydroxyl, carbonyl, and carboxyl groups, respectively (Sahoo, 2011 ). Zuo et al. ( 2016 ) and Cibati et al. ( 2017 ) reported that carboxyl groups are among the most influential functional moieties governing metal adsorption efficiency in biochar. Similarly, for MB sorption, the presence of oxygenated functional groups is essential, since the mechanism of sorption is primarily governed by electrostatic interactions, π–π stacking, hydrogen bonding, and cation exchange (Dai et al., 2022 ; Fan et al., 2016 ; Shahib et al., 2022 ). Mechanistically, the presence of persistent free radicals (PFRs) on biochar facilitates the decomposition of hydrogen peroxide into highly reactive hydroxyl (•OH) and hydroperoxyl (•OOH) radicals as visualized in Fig. 4 (Porto et al., 2024 ). These radicals subsequently attack π-bonds within the biochar matrix, leading to partial delignification and the incorporation of oxygen-containing functional groups (Fang et al., 2014 ). This radical-mediated modification increases surface polarity and introduces negatively charged sites, thereby enhancing dipole–dipole hydrogen bonding between oxygen functional groups on biochar and the nitrogen atoms in MB molecules (Porto et al., 2024 ). The formation of acidic oxygenated groups (e.g., –COOH and –OH) increases the availability of proton donors, explaining the marked reduction in pH observed for OxyAChar-3, which possessed the highest abundance of oxygen-containing functional groups. This finding aligns with previous studies by Chemerys et al. ( 2020 ) and Huff and Lee ( 2016 ), who similarly attributed the pH decrease after H₂O₂ treatment to the enrichment of carboxyl and hydroxyl functionalities. Hence, the decline in pH serves as a clear indicator of successful surface oxidation. The electrical conductivity (EC) results further corroborate the FTIR findings. All OxyAChar samples exhibited lower EC compared to the unmodified control, consistent with observations by Kane et al. ( 2021 ), who reported that a higher oxygen incorporation leads to reduced EC due to an increase in oxygen-to-carbon (O:C) ratio. This enhanced oxygenation increases surface acidity and promotes cation retention (e.g., K⁺, Mg²⁺, and Ca²⁺) through ion exchange, thereby minimizing leaching of exchangeable mineral cations and reducing the measured EC (Qian & Chen, 2013 ). The lowest EC observed at 3% H₂O₂ treatment reflects the most effective fixation of cations onto newly generated oxygenated sites, highlighting the optimal oxidation level for achieving balanced surface functionality and structural preservation. Beyond functional enhancement, the potential impact of H₂O₂ oxidation on the long-term stability of biochar is an important consideration. Biochar is known to persist in soils for hundreds to thousands of years (Lehmann, 2007 ), making it a promising material for climate change mitigation, as it stabilizes carbon that would otherwise be released into the atmosphere through natural biomass decomposition. Therefore, assessing the stability of oxidized biochar is crucial not only for understanding its durability in soil remediation but also for evaluating its potential contributions to carbon sequestration and long-term soil carbon storage (Wang et al., 2024 ). To evaluate this, thermogravimetric analysis (TGA) was performed. Results revealed that all OxyAChar exhibited enhanced thermal stability, with lower total weight losses (26.8–37.6%) than the unmodified control (44.15%). The most stable sample, OxyAChar-1 (27.3% weight loss), demonstrated that mild oxidation effectively removed labile organic fractions and volatile compounds while preserving the integrity of the carbon backbone. These findings are consistent with Zhao et al. ( 2024 ) observed that H₂O₂-modified biochar maintained structural resilience across five adsorption–desorption cycles for bisphenol A removal, underscoring its durability and reusability. Collectively, these results indicate that carefully controlled H₂O₂ oxidation not only enhances surface functionality but also sustains, or even improves, the intrinsic stability of biochar, preserving its dual role as an effective adsorbent and a long-term carbon sink. 4.2 Excess H₂O₂ Oxidation Compromises Biochar Functionality Having established that moderate oxidation (3% H₂O₂) optimizes both surface reactivity and structural stability, it is essential to evaluate how further increases in oxidant concentration influence these properties. At higher H₂O₂ concentrations (> 3%), the physicochemical and structural properties of PKS biochar exhibited a declining trend. FESEM and BET analyses revealed that excessive oxidation led to structural deterioration, likely due to the rapid release of excessive amount of oxygen that attacked and disrupted the existing carbon framework (Amin et al., 2020 ). This over-oxidation caused pore rupture, wall thinning, and partial collapse, resulting in reduced surface area and pore volume. Consequently, OxyAChar-10, OxyAChar-20, and OxyAChar-30 displayed markedly lower mesopore surface areas and total pore volumes, with OxyAChar-30 approaching values similar to the unmodified control, indicating a reversal of the structural enhancements observed at moderate oxidation levels. FTIR spectra further supported these observations, showing diminished intensities of O–H, C = O, and C–O bands at higher oxidation levels, which suggests a loss of surface functional groups. This behavior arises from the dual nature of the H₂O₂ oxidation mechanism: at moderate concentrations (e.g., 3%), reactive •OH and •OOH radicals attack aromatic carbon sites, introducing oxygen-containing functional groups and enhancing surface reactivity. However, at excessive concentrations, over-oxidation promotes the decomposition of aromatic structures into volatile products such as CO₂ and low-molecular-weight organic acids (Sahoo, 2011 ). This self-limiting oxidation process not only decreases the density of surface functionalities but also compromises the carbon framework, reducing both chemical and structural integrity. This reduced acidic oxygen functional groups at high oxidation levels also explains the partial rebound in pH observed for OxyAChar-10, OxyAChar-20, and OxyAChar-30, as well as the corresponding increase in electrical conductivity—both indicative of fewer acidic oxygenated sites and reduced ion exchange capacity. These findings are consistent with those of Zhang et al. ( 2023 ) and Nguyen et al. ( 2021 ), who reported that moderate oxidation optimizes surface polarity and functional group density of biochar, while excessive oxidation induces structural degradation and functional loss. Taken together, these results demonstrate that 3% H₂O₂ represents the optimal oxidation condition for PKS biochar, achieving a favorable balance between pore development, surface functionality, and structural stability. Beyond this threshold, the detrimental effects of over-oxidation outweigh the benefits, leading to decreased sorption capacity for methylene blue (MB) and reduced water-holding capacity (WHC). 4.3 Cross-Study Insights into H₂O₂ Oxidation of Biochar To contextualize the present findings and identify generalizable trends across different feedstocks and experimental conditions, a comparative analysis of nine published studies was performed and summarized in Table 5 . This synthesis aimed to determine whether the enhancement patterns observed in H₂O₂-modified palm kernel shell (PKS) biochar are consistent with those reported for other biochar and to elucidate how feedstock composition and treatment intensity influence physicochemical and adsorption performance. Across the reviewed studies, H₂O₂ oxidation consistently emerged as an effective, low-cost, and environmentally benign strategy to enhance biochar performance (Xue et al., 2012 ). In agreement with the present results, moderate oxidation generally increased BET surface area (Chemerys et al., 2020 ; Porto et al., 2024 ) and introduced oxygen-containing functional groups such as carboxyl (–COOH), hydroxyl (–OH), and carbonyl (C = O), which play critical roles in improving adsorption and cation exchange capacity of biochar (Zhang et al., 2023 ; Zuo et al., 2016 ). Conversely, several studies have reported that excessive oxidation leads to structural degradation and loss of surface functionality (Nguyen et al., 2021 ; Zhang et al., 2023 ). This observation aligns with the decline in surface area, pore volume, methylene blue adsorption capacity, and water-holding capacity observed in PKS biochar treated with H₂O₂ concentrations above 3%, confirming that controlled oxidation is essential to preserve biochar structural integrity. Nevertheless, the optimal H₂O₂ concentration reported in the literature varies considerably among feedstocks, and even pyrolysis parameters can markedly influence the effectiveness of H₂O₂ oxidation. For instance, Cibati et al. ( 2017 ) observed that Miscanthus × giganteus biochar produced at 350°C and treated with 10% H₂O₂ exhibited enhanced Cu²⁺ and Zn²⁺ adsorption, whereas biochar produced at 600°C under identical treatment suffered pore collapse due to over-oxidation. Table 5 Comparative literature-reported H₂O₂ modifications of various biochar, including feedstock type, H₂O₂ treatment conditions, optimal oxidation concentrations, target adsorbates, and major findings. Biochar Hydrogen peroxide (H₂O₂) treatment Optimal Concentration Target Adsorbate Key findings Reference Pinewood 1, 3, 10, 20, 30% w/w 1% w/w MB Higher H₂O₂ decreased MB adsorption beyond optimum; MB adsorption did not increase proportionally with CEC Huff and Lee ( 2016 ) Sewage biosolids 0,10, 20, 30, 40 vol % 20 vol % MB H₂O₂ increased surface area & pore volume via degradation; oxygen groups formed during activation contribute to adsorption Porto et al. ( 2024 ) Cymbopogon schoenanthus L. Spreng 0,10, 20, 30% v/v 20% v/v Cu Carboxyl groups responsible for enhanced Cu sorption Zuo et al. ( 2016 ) Peanut shell 10, 20, 30% 20% Cu H₂O₂ oxidation produces carboxyl and hydroxyl groups; excessive oxidation decomposes aromatic carbon, reducing binding sites Zhang et al. ( 2023 ) Phragmites australis 5,15,30% 5% Bisphenol A H₂O₂ increases micropore & mesopore volumes, disrupts microcrystalline order; improved reusability (up to 5 cycles); follow pseudo-second-order kinetics and Freundlich isotherm; enhanced C = O, C–O, –OH bonds Zhao et al. ( 2024 ) Typha orientalis Peanut Hull 10% NR Heavy metals (Pb, Cu, Cd, Ni) After H₂O₂ modification, Pb removal comparable to commercial agents, using low-cost feedstock Xue et al. ( 2012 ) Miscanthus x giganteus 10% w/v NR Cu, Zn Carboxyl groups enhance metal adsorption for biochar produced at 350°C, but at 600°C, severe oxidation destroys porosity, reducing adsorption capacity Cibati et al. ( 2017 ) Paper waste sludge 10,20,30,40,50,60% w/v 40% w/v NH₄⁺ H₂O₂ modification increases surface functional groups; adsorption controlled by cation exchange, electrostatic attraction; mechanisms include π–cation, complexation, ion exchange; kinetics pseudo-second-order; isotherm Langmuir Nguyen et al. ( 2021 ) Birch wood 3, 15, 30% w/w 30% w/w NR CEC increased ~ 6×; BET specific surface area increased ~ 15× Chemerys et al. ( 2020 ) Palm kernel shell 0,1,3,10,20,30% v/v 3% v/v MB H₂O₂ oxidation enhanced BET surface area, porosity (micro- and mesopores), and the abundance of oxygen-containing functional groups (–COOH, –OH, C = O), improving methylene blue adsorption and water retention. This study Note : MB = Methylene Blue; Cu = Copper; Pb = Lead; Cd = Cadmium; Ni = Nickel; NH₄⁺ = Ammonium; “NR” = Data not relevant to the study. 4.4 Toward AI-Guided Design of High-Performance H₂O₂-Modified Biochar The inconsistency observed across studies clearly demonstrates that there is no universal or “one-size-fits-all” optimal H₂O₂ concentration for biochar modification. Such variability arises from the complex interplay among feedstock composition, pyrolysis temperature, oxidation intensity, and post-treatment conditions. Relying solely on conventional trial-and-error experimentation to optimize these parameters is both labor-intensive and inefficient, given the vast number of possible variable combinations. To address this challenge, recent studies have begun integrating machine learning (ML) as a data-driven tool to model, predict, and optimize biochar properties. Machine learning techniques can capture nonlinear interactions among biochar production parameters (e.g., feedstock composition, pyrolysis temperature, activation conditions, and oxidant concentration) and their corresponding functional outcomes (e.g., adsorption capacity, cation exchange capacity, or surface oxygenation). For instance, Alabdrabalnabi et al. ( 2022 ) applied the XGBoost algorithm to predict biochar yield with high accuracy (R² = 0.96, RMSE = 1.77), while Leng et al. ( 2022 ) used gradient boosting regression to predict nitrogen content across multiple feedstock types, achieving robust performance across forestry, agricultural, manure, and algae-based biochar. El Hanandeh et al. ( 2021 ) and Nguyen et al. ( 2022 ) demonstrated the capability of machine learning models to predict heavy metal and nutrient adsorption with accuracies exceeding 99%, underscoring the strong predictive potential of machine learning in complex sorption systems. These findings highlight a transformative opportunity to extend data-driven modeling to biochar oxidation, particularly for optimizing H₂O₂ modification processes. In this context, machine learning can be trained on comprehensive datasets encompassing parameters such as surface functional group abundance, BET surface area, pore structure (micro-, meso-, and macropores), and oxidation conditions to identify the most influential variables governing adsorption capacity and water retention. Feature-importance analysis within machine learning frameworks can quantitatively rank these variables, revealing which physicochemical attributes most strongly determine biochar performance. Such insight enables the rational selection of modification parameters, streamlining process design and reducing empirical trial-and-error experimentation. Once trained, machine learning models can predict optimal H₂O₂ concentrations across diverse conditions, considering feedstock type, pyrolysis temperature, oxidation intensity, and even field-specific factors such as soil type or crop requirements. This predictive capability is difficult to achieve through traditional trial-and-error studies. Therefore, transitioning toward machine learning-assisted biochar design represents a powerful pathway to accelerate optimization, minimize experimental cost and time, and facilitate the large-scale adoption of biochar in sustainable agriculture. 5.0 Conclusion This study provides the first systematic evidence that controlled H₂O₂ oxidation at 3% optimizes the surface chemistry and structure of PKS biochar. Moderate H₂O₂ oxidation produces OxyAChar-3 with the highest BET surface area (520.21 m²·g⁻¹ micropores, 360.48 m²·g⁻¹ mesopores), well-developed pore connectivity, and abundant oxygen-containing functional groups (–COOH, –OH, C = O), thereby enhancing methylene blue adsorption and water retention. In contrast, excessive oxidation (> 3%) compromises structural integrity and functional performance, highlighting the importance of controlled treatment. Comparative analysis with the literature reveals the feedstock- and process-dependent variability that governs H₂O₂ oxidation effectiveness, emphasizing the potential of machine learning-assisted biochar design to predict optimal modification conditions, reduce trial-and-error experimentation, and accelerate the development of high-performance, sustainable biochar for soil remediation, water retention, and carbon sequestration. Declarations Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by Fundamental Research Grant Scheme FRGS/1/2023/STG01/UM/02/2 (Grant number FRGS: FP053-2023). Associate Professor Dr. Rosazlin Abdullah has received research support from Ministry of Education Malaysia. Author Contribution All authors contributed to the study conception and design. 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11:30:22","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144643,"visible":true,"origin":"","legend":"","description":"","filename":"e318547703f44f2b8141b22793c2eacd1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7897807/v1/d7d8ea75062a69c635b657af.xml"},{"id":95827429,"identity":"052cec80-1bd0-4c00-82c7-90f42792e517","added_by":"auto","created_at":"2025-11-13 11:30:23","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151232,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7897807/v1/1fa2397f42e3ea9f7c8a4022.html"},{"id":95827399,"identity":"852b5608-9017-470e-8bdd-4d0a7d9953c4","added_by":"auto","created_at":"2025-11-13 11:30:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141691,"visible":true,"origin":"","legend":"\u003cp\u003eThermogravimetric analysis (TGA) profiles of (a) pristine biochar and H₂O₂-modified OxyAChar prepared using (b) 1%, (c) 3%, (d) 10%, (e) 20%, and (f) 30% H₂O₂ solutions.\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7897807/v1/dfd00aa808cbfcd618f4cd5a.jpg"},{"id":96239753,"identity":"26d4f066-89a4-4558-a6b8-163565023af4","added_by":"auto","created_at":"2025-11-19 07:07:32","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1706419,"visible":true,"origin":"","legend":"\u003cp\u003eField Emission Scanning Electron Microscopy (FESEM) micrographs of pristine and H₂O₂-modified PKS biochar: (a) Biochar, (b) OxyAChar-1, (c) OxyAChar-3, (d) OxyAChar-10, (e) OxyAChar-20, and (f) OxyAChar-30\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7897807/v1/1bfc88a9385998646c68394b.jpeg"},{"id":95827419,"identity":"015c2d66-f00d-4a26-8dd3-2969e9da4c8d","added_by":"auto","created_at":"2025-11-13 11:30:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172300,"visible":true,"origin":"","legend":"\u003cp\u003eFourier-transform infrared (FTIR) spectra of pristine and H₂O₂-modified PKS biochars (OxyAChar-\u003cem\u003ex\u003c/em\u003e, where \u003cem\u003ex\u003c/em\u003erepresents the H₂O₂ concentration, % v/v). The highlighted regions indicate characteristic functional groups associated with O–H stretching (grey), C=O stretching (brown), and C–O vibrations (pink)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7897807/v1/025a315a8326ed150d495331.png"},{"id":95827412,"identity":"bc603b7a-3380-4dc1-9626-75a3b1a2a7e6","added_by":"auto","created_at":"2025-11-13 11:30:22","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":105696,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic illustration of the proposed mechanism for H₂O₂-induced surface oxidation of biochar. (1) Persistent free radicals (PFRs) present on the biochar surface catalyze the decomposition of H₂O₂, (2) generating highly reactive hydroxyl (•OH) and hydroperoxyl (•OOH) radicals. (3) These radicals subsequently attack the carbon matrix, leading to structural modification, and (4) promote partial delignification and the incorporation of oxygen-containing functional groups such as –COOH, –OH, and C=O.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7897807/v1/bbbdd0bf0fe6d868700ccc0b.jpeg"},{"id":106343614,"identity":"bb000896-c57a-4c08-9321-afa5302cbdf0","added_by":"auto","created_at":"2026-04-07 16:07:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3271710,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7897807/v1/f4edc249-ac18-49cb-a453-9baa60d3bb67.pdf"},{"id":95827402,"identity":"630be8b9-f82a-4244-b987-989711023ecf","added_by":"auto","created_at":"2025-11-13 11:30:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1022558,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7897807/v1/dbe93be9674ba7153e6381d0.docx"},{"id":95827408,"identity":"ad61bec1-09bb-41c7-8b25-113adea33aef","added_by":"auto","created_at":"2025-11-13 11:30:22","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":402804,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7897807/v1/40bcabe4cd6111fb8f958a63.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Engineering biochar through surface oxygenation: a green approach for sustainable environmental applications","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eH₂O₂ is green oxidant that enhances biochar with surface oxygenation.\u003c/li\u003e\n \u003cli\u003eOxyAChar has lower pH and EC, indicating enriched oxygen functional groups.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOxyAChar-3 has improved mesopore (+35.4%) micropore (+28.8%) surface area.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOxyAChar-3 demonstrates superior MB removal efficiency (90.11%) and WHC (168.70%).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOxyAChar is promising soil enhancer that improves nutrient and water retention.\u003c/li\u003e\n \u003cli\u003eMachine learning can be applied in the future to precisely design high-performance oxidized biochar.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1.0 Introduction","content":"\u003cp\u003eOil palm (\u003cem\u003eElaeis guineensis\u003c/em\u003e) is the world\u0026rsquo;s most important oil crop, contributing approximately 40% of global vegetable oil production, with over three billion people relying on it daily (Murphy et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Malaysia, the second-largest producer, generated 16.9\u0026nbsp;million tons of palm oil in 2024 (MIDA, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, only\u0026thinsp;~\u0026thinsp;10% of the plant contributes to oil production; the remaining 90% including fronds, trunks, shells, and empty fruit bunches forms an underutilized biomass waste stream (Abdullah \u0026amp; Sulaiman, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) \u003cb\u003e[Supplementary Information (SI)]\u003c/b\u003e. Annually, 4.3\u0026nbsp;million tons of palm kernel shell (PKS) are produced (Tan et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Rather than allowing this materials going to waste, researchers recognize the potential of PKS as a valuable raw material for biochar production due to its high lignin and carbon content (Daud \u0026amp; Ali, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBiochar, a carbon rich product derived from biomass, has a long history of use, tracing back 2500 years ago to the \u0026ldquo;Terra Preta\u0026rdquo; soils of the Amazon Basin. These soils are renowned for their exceptional fertility and organic carbon content (Semida et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite extensive research, conventional biochar often exhibit limited surface area and insufficient functional groups, constraining their performance in application (e.g. catalysis, composting, pollutant removal, and soil remediation) (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, research on biochar modification has expanded exponentially, with publications in the Web of Science Core Collection increasing from only two in 2010 to nearly one thousand in 2024. This rapid growth reflects the growing global emphasis on tailoring biochar properties for targeted environmental applications (Tomczyk et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong the various modification techniques, physical activation effectively enhances biochar porosity but often requires high energy input and extended processing times. In contrast, chemical oxidation offers greater control over surface functionality and can achieve activation at relatively lower temperatures. However, conventional chemical oxidants may introduce secondary pollutants, generate corrosive byproducts, or increase production costs (Tomczyk et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, identifying greener oxidants remains a key challenge in sustainable biochar engineering.\u003c/p\u003e\u003cp\u003eHydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) stands out as a promising chemical for biochar modification due to its low cost and environmentally friendly nature as a green oxidant. H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e decomposes into water and oxygen, leaving no residual compounds that could interfere with the performance of biochar in subsequent applications (Huff \u0026amp; Lee, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Through controlled oxidation, H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e removes pore-blocking residues and introduces oxygenated functional groups such as hydroxyl and carboxyl moieties (Alves et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Amin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zuo et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These structural and chemical modifications collectively increase surface area, porosity, and charge density of biochar, resulting in enhanced sorption and water retention capacities (Tan et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zuo et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile numerous studies have examined H₂O₂-based modification of biochar derived from wood, sludge, and other agricultural residues, the oxidation behavior of palm kernel shell (PKS) biochar, a high-ash, lignin-rich biomass abundant in tropical regions remains largely unexplored. Understanding how such complex feedstocks respond to oxidative treatment is crucial for developing sustainable, high-performance biochar tailored to diverse environmental applications. Therefore, this study systematically investigates the influence of controlled H₂O₂ (1%, 3%, 10%, 20%, and 30%) on the physicochemical properties and sorption performance of PKS biochar. Specifically, it aims to:\u003c/p\u003e\u003cp\u003e\u003col style=\"list-style-type:lower-roman;\"\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eidentify the relationship between oxidation intensity and key structural attributes, including surface functionality, porosity, pH, electrical conductivity (EC) and thermal stability.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003edetermine how these transformations govern methylene blue adsorption and water retention performance.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe outcomes establish a mechanistic and practical foundation for green biochar engineering and underscore the potential of machine learning-assisted optimization to precisely predict oxidation conditions, prioritize influential physicochemical parameters, and accelerate the design of high-performance biochar for soil, water, and carbon management applications.\u003c/p\u003e"},{"header":"2.0 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Materials procurement and collection\u003c/h2\u003e\u003cp\u003eAll chemicals used in this study were of analytical grade. Methylene blue (MB; C₁₆H₁₈N₃SCl) and hydrogen peroxide (H₂O₂, 30% w/w) were obtained from Merck (Malaysia). Palm kernel shell (PKS) biochar was obtained from the Malaysian Palm Oil Board (MPOB, Bandar Baru Bangi, Selangor, Malaysia). The biochar was ground and sieved through a 0.5\u0026ndash;1.0 mm mesh prior to activation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Preparation of biochar\u003c/h2\u003e\u003cp\u003eBiochar activation using H₂O₂ was performed following the methods of Chemerys et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Huff and Lee (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and Sizmur et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) with minor modifications. Briefly, PKS biochar was mixed with H₂O₂ solutions of 1%, 3%, 10%, 20%, and 30% (v/v) at a solid-to-liquid ratio of 1:20 (w/v) in 50 mL glass beakers and agitated at 110 rpm for 2 h on a platform shaker. A control sample was prepared using distilled water instead of H₂O₂. After treatment, the samples were filtered, rinsed thoroughly with distilled water, and oven-dried at 105\u0026deg;C overnight. The resulting modified biochar were designated as OxyAChar-1, OxyAChar-3, OxyAChar-10, OxyAChar-20, and OxyAChar-30, while the untreated sample was designated as Biochar.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Characterization of biochar\u003c/h2\u003e\u003cp\u003eThe pH and electrical conductivity (EC) of biochar samples were determined following Singh et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).. For pH, biochar was suspended in distilled water at a ratio of 1:20 (w/v), while EC was measured at a 1:5 (w/v) ratio. Mixtures were shaken at 240 rpm for 1 h and allowed to settle for 30 min before measurement. Thermal stability was analyzed using a Thermogravimetric/Differential Thermal Analyzer (TG/DTA; PerkinElmer TGA6) at a heating rate of 20\u0026deg;C min⁻\u0026sup1; from 30 to 900\u0026deg;C under N₂ flow (20 mL min⁻\u0026sup1;). Surface functional groups were characterized using Fourier Transform Infrared Spectroscopy (FTIR; Thermo Scientific Nicolet\u0026trade; Summit) over 550\u0026ndash;4000 cm⁻\u0026sup1;. Morphological features were observed via Field Emission Scanning Electron Microscopy (FESEM; Hitachi SU8220). Specific surface area (SSA) was determined by N₂ adsorption\u0026ndash;desorption analysis at 77 K using the Brunauer\u0026ndash;Emmett\u0026ndash;Teller (BET) method (Sorptometric-1990 Analyzer).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Methylene Blue (MB) Sorption\u003c/h2\u003e\u003cp\u003eThe sorption capacity of biochar samples was evaluated using methylene blue (MB; C₁₆H₁₈N₃SCl) as a model organic dye, following Baharim et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with minor modifications. 1 g of biochar was added to 50 mL of 10 mg L⁻\u0026sup1; MB solution (pH\u0026thinsp;\u0026asymp;\u0026thinsp;7) and agitated at 120 rpm for 48 h. After equilibrium, supernatants were collected, and absorbance was measured at 665 nm using a UV\u0026ndash;Vis spectrophotometer (Shimadzu Mini 1240). The sorption capacity (Qₑ, mg g⁻\u0026sup1;) and removal efficiency (R, %) were calculated using Eqs.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Q}_{e}=\\:\\frac{\\left({C}_{o}-{C}_{e}\\right)V}{W}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:R=\\:\\frac{\\left({C}_{o}-{C}_{e}\\right)}{{C}_{o}}\\:x\\:100\\text{\\%}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{0}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{e}\\)\u003c/span\u003e\u003c/span\u003e are the MB concentrations (mg L⁻\u0026sup1;) at initial and equilibrium, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V\\)\u003c/span\u003e\u003c/span\u003e is the solution volume (L), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:W\\)\u003c/span\u003e\u003c/span\u003e is the mass of biochar (g).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Water-Holding Capacity (WHC)\u003c/h2\u003e\u003cp\u003eWater-holding capacity (WHC) was determined following Hien et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Mimmo et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Approximately 2 g of oven-dried biochar (100\u0026deg;C, overnight) was soaked in a known volume of distilled water for 2 h to ensure full saturation, followed by free drainage for 2 h. WHC was calculated as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:WHC\\:=\\:\\frac{{W}_{w}{-\\:W}_{d}}{{W}_{d}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{w}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{d}\\)\u003c/span\u003e\u003c/span\u003e are the wet and dry weights of biochar, respectively. Measurements were conducted in triplicate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics (v29, IBM Corp., USA). One-way ANOVA was conducted to assess differences among treatments, followed by Duncan\u0026rsquo;s post hoc test at a 95% confidence level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Results are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error. FTIR spectral data were processed and visualized using Spectragryph 1.2 (Menges, Germany). The integrated area under characteristic peaks was obtained using the built-in \u0026ldquo;Integrate Area Under Curve\u0026rdquo; function to compare the relative intensity of surface functional groups. Thermogravimetric (TG/DTA) data were analyzed and plotted using OriginPro 2025 (OriginLab Corp., USA) to determine mass loss regions and thermal stability profiles.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Physicochemical properties\u003c/h2\u003e\u003cp\u003eThe basic physicochemical characteristics of pristine and surface-oxidized PKS biochar (OxyAChar) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The pristine biochar exhibited a near-neutral pH of 7.36, whereas H₂O₂ oxidation progressively acidified the surface, reducing the pH to 6.03\u0026ndash;6.79, with OxyAChar-3 showing the lowest pH value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This acidification is likely attributable to the introduction of oxygen-containing acidic functional groups, such as carboxyl and hydroxyl moieties, on the biochar surface (Huff \u0026amp; Lee, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, electrical conductivity (EC) decreased nonlinearly by 30\u0026ndash;37% in all oxidized samples relative to the pristine biochar (30.4 \u0026micro;S\u0026middot;cm⁻\u0026sup1;).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePhysicochemical properties (pH and electrical conductivity, EC) of pristine and H₂O₂-modified PKS biochar (OxyAChar) prepared using different H₂O₂ concentrations (1%, 3%, 10%, 20%, and 30%)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEC (\u0026micro;S\u0026middot;cm⁻\u0026sup1;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiochar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.086\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.529\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.503\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.049\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.819\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.038\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.384\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.081\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.318\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.068\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.819\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: OxyAChar-\u003cem\u003ex\u003c/em\u003e refers to PKS biochar oxidized using \u003cem\u003ex\u003c/em\u003e % (v/v) H₂O₂ solution. Values represent means of three replicates\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error. Means within the same column followed by different letters are significantly different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) based on one-way ANOVA followed by Tukey\u0026rsquo;s HSD test.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Thermal stability\u003c/h2\u003e\u003cp\u003eAccording to the method of Mitchell et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the initial weight loss observed between 30 and 105\u0026deg;C corresponds to the release of residual moisture. The results show that moisture content ranged from 6.77% in OxyAChar-30 to 12.19% in OxyAChar-3, with the control biochar exhibiting an intermediate value of 8.80% as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Across the full thermal degradation range, total weight loss reflects the proportion of volatile and labile components in the biochar, providing an indication of its relative thermal stability (Nusrat Aman et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). All oxidized biochar demonstrated enhanced thermal stability, with total weight loss decreasing from 44.15% in the pristine sample to 26.8\u0026ndash;37.6% in OxyAChar.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Surface morphology and porosity\u003c/h2\u003e\u003cp\u003eFESEM and BET analyses were used to investigate the changes in the pore structure of PKS biochar following H₂O₂ oxidation. The pristine biochar exhibited a compact, irregular surface with poorly developed pores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Mild oxidation (1\u0026ndash;3% H₂O₂) effectively etched the surface and removed loosely bound organic matter, exposing existing pores, enhancing porosity, and yielding a smoother morphology, as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u0026ndash;c. Among the samples, OxyAChar-3 exhibited the most developed pore structure, with the highest micropore surface area (520.21 m\u0026sup2;\u0026middot;g⁻\u0026sup1;), mesopore surface area (360.48 m\u0026sup2;\u0026middot;g⁻\u0026sup1;), and total pore volume (0.182 cm\u0026sup3;\u0026middot;g⁻\u0026sup1;) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Beyond this optimal oxidation level (\u0026ge;\u0026thinsp;10%), the well-defined pores deteriorated, with ruptured walls and irregular surfaces, particularly at 30%, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef. Consequently, the mesopore surface area (294.74 m\u0026sup2;\u0026middot;g⁻\u0026sup1;) and total pore volume (0.150 cm\u0026sup3;\u0026middot;g⁻\u0026sup1;) decreased to values approaching those of the pristine biochar (279.91 m\u0026sup2;\u0026middot;g⁻\u0026sup1; and 0.149 cm\u0026sup3;\u0026middot;g⁻\u0026sup1;, respectively).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTextural characteristics of pristine and H₂O₂-modified PKS biochar, including micropore surface area (m\u0026sup2; g⁻\u0026sup1;), mesopore surface area (m\u0026sup2; g⁻\u0026sup1;), and total pore volume (cm\u0026sup3; g⁻\u0026sup1;) at different oxidant concentrations (1%, 3%, 10%, 20%, and 30%)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMicropore surface area (m\u0026sup2; g⁻\u0026sup1;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMesopore surface area (m\u0026sup2; g⁻\u0026sup1;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal pore volume (cm\u003csup\u003e3\u003c/sup\u003e/g)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiochar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e384.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e279.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e399.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e321.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e520.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e360.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e401.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e361.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e390.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e356.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e444.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e294.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: OxyAChar-\u003cem\u003ex\u003c/em\u003e refers to PKS biochar oxidized using \u003cem\u003ex\u003c/em\u003e % (v/v) H₂O₂ solution.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Surface functional groups\u003c/h2\u003e\u003cp\u003eFTIR spectra confirmed significant surface chemical modifications following H₂O₂ oxidation. Key functional groups were identified at 2500\u0026ndash;3300 cm⁻\u0026sup1; (O\u0026ndash;H stretching), 3050\u0026ndash;3100 cm⁻\u0026sup1; (C\u0026ndash;H stretching), 1735\u0026ndash;1750 cm⁻\u0026sup1; (C\u0026thinsp;=\u0026thinsp;O stretching), 1566\u0026ndash;1650 cm⁻\u0026sup1; (C\u0026thinsp;=\u0026thinsp;C stretching of aromatic rings), and 1085\u0026ndash;1150 cm⁻\u0026sup1; (C\u0026ndash;O stretching in alcohols, esters, or ethers). Quantitative integration of the C\u0026thinsp;=\u0026thinsp;C (1566\u0026ndash;1650 cm⁻\u0026sup1;) and C\u0026ndash;H (2920\u0026ndash;2850 cm⁻\u0026sup1;) regions revealed consistent reductions in both functional groups following H₂O₂ oxidation. The pristine biochar exhibited the highest integral values for C\u0026thinsp;=\u0026thinsp;C (96.95) and C\u0026ndash;H (51.27) as presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, corresponding to condensed aromatic and aliphatic structures from lignin decomposition (Hwong et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). After modification, these areas decreased to 78.66\u0026ndash;84.11 (C\u0026thinsp;=\u0026thinsp;C) and 46.10\u0026ndash;49.71 (C\u0026ndash;H), reflecting partial oxidation of aromatic and aliphatic carbon structures. The lowest C\u0026thinsp;=\u0026thinsp;C integral (78.66) recorded for OxyAChar-3. Concurrently, the intensified O\u0026ndash;H, C-O bands and the emergence of a sharp C\u0026thinsp;=\u0026thinsp;O band in OxyAChar-3 indicate the formation of oxygen functional groups as highlighted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIntegrated band areas of selected FTIR absorption regions for pristine and H₂O₂-oxidized PKS biochar, showing the relative enhancement of oxygen-containing functional groups following surface oxidation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunctional group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWavenumber range (cm⁻\u0026sup1;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBiochar\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOxyAChar-1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOxyAChar-3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOxyAChar-10\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOxyAChar-20\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOxyAChar-30\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;C stretching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1566\u0026ndash;1650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e84.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e84.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e82.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-H stretching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3050\u0026ndash;3100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e49.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e49.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e48..35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: Integral values represent relative peak areas calculated from deconvoluted FTIR spectra. OxyAChar-\u003cem\u003ex\u003c/em\u003e refers to PKS biochar oxidized using \u003cem\u003ex\u003c/em\u003e % (v/v) H₂O₂ solution.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Adsorption and water retention performance\u003c/h2\u003e\u003cp\u003eThe functional implications of surface oxidation were evaluated using methylene blue (MB) sorption and water-holding capacity (WHC) tests. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, all oxidized samples exhibited enhanced MB sorption compared to the control, increasing from 76.94% in the pristine biochar to a maximum of 90.11% in OxyAChar-3. However, the MB sorption performance plateaued or slightly declined at higher H₂O₂ concentrations (83.50\u0026ndash;86.66% for OxyAChar-10 and OxyAChar-20, and 83.54% for OxyAChar-30). A similar trend was observed for WHC, which reached a maximum of 168.7% in OxyAChar-3, compared to 135.46% for the pristine biochar. Biochar oxidized with 10% and 20% H₂O₂ showed no significant difference in WHC relative to the control, whereas a drastic reduction of 69.4% was observed at 30% H₂O₂.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMethylene blue (MB) removal efficiency (%) and water-holding capacity (WHC, %) of pristine and H₂O₂-modified PKS biochar (OxyAChar) prepared at different H₂O₂ concentrations (1%, 3%, 10%, 20%, and 30%)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMB Removal Efficiency (%), R\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWater Holding Capacity (%), WHC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiochar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e168.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e132.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxyAChar-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: OxyAChar-\u003cem\u003ex\u003c/em\u003e refers to PKS biochar oxidized using \u003cem\u003ex\u003c/em\u003e % (v/v) H₂O₂ solution.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Moderate H₂O₂ Oxidation Enhances Biochar Performance\u003c/h2\u003e\u003cp\u003eThe functional implications of H₂O₂ surface oxidation on palm kernel shell (PKS) biochar were evaluated through methylene blue (MB) sorption and water-holding capacity (WHC) experiments. These serve as practical indicators of the environmental and agronomic potential of biochar. MB, a cationic dye with three positively charged nitrogen atoms (Porto et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), is commonly used as a model pollutant to assess sorption capacity and as an indicator of nutrient retention potential of biochar due to its similar charge to essential plant cations (e.g. K⁺, Ca\u0026sup2;⁺, Mg\u0026sup2;⁺) (Huff \u0026amp; Lee, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; McNamara, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). WHC, on the other hand, reflects the hydrophilicity of biochar surfaces and their ability to retain moisture against gravitational forces (Cheatham et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Both parameters exhibited a nonlinear response to increasing H₂O₂ concentration, with OxyAChar-3 achieving the highest MB sorption (90.11%) and WHC (168.7%). Specifically, MB sorption by OxyAChar-3 increased by 17.22% relative to the control and could retain 1.69 times its dry weight in water. The exceptional MB sorption and WHC of OxyAChar-3 is attributed to the synergistic effect of its enriched surface oxygenated functionalities and optimized pore structure, a relationship further corroborated by BET, FESEM, FTIR, TGA, pH, and EC analyses presented below.\u003c/p\u003e\u003cp\u003eBET and FESEM analyses confirmed that moderate oxidation (3% H₂O₂) maximized the development of both micropores (520.21 m\u0026sup2;\u0026middot;g⁻\u0026sup1;) and mesopores (360.48 m\u0026sup2;\u0026middot;g⁻\u0026sup1;) while maintaining pore connectivity and structural integrity. The well-developed pore network facilitate methylene blue (MB) diffusion and enhance capillary water entrapment by proving readily available sites for pore filling (Zeghioud \u0026amp; Mouhamadou, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Beyond improving pore structure, FTIR spectra confirmed the successful oxidation of PKS biochar, with OxyAChar-3 showing the most pronounced signals at 2500\u0026ndash;3300 cm⁻\u0026sup1; (O\u0026ndash;H), 1735\u0026ndash;1750 cm⁻\u0026sup1; (C\u0026thinsp;=\u0026thinsp;O), and 1085\u0026ndash;1150 cm⁻\u0026sup1; (C\u0026ndash;O), indicative of hydroxyl, carbonyl, and carboxyl groups, respectively (Sahoo, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eZuo et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Cibati et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that carboxyl groups are among the most influential functional moieties governing metal adsorption efficiency in biochar. Similarly, for MB sorption, the presence of oxygenated functional groups is essential, since the mechanism of sorption is primarily governed by electrostatic interactions, π\u0026ndash;π stacking, hydrogen bonding, and cation exchange (Dai et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shahib et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Mechanistically, the presence of persistent free radicals (PFRs) on biochar facilitates the decomposition of hydrogen peroxide into highly reactive hydroxyl (\u0026bull;OH) and hydroperoxyl (\u0026bull;OOH) radicals as visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (Porto et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These radicals subsequently attack π-bonds within the biochar matrix, leading to partial delignification and the incorporation of oxygen-containing functional groups (Fang et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This radical-mediated modification increases surface polarity and introduces negatively charged sites, thereby enhancing dipole\u0026ndash;dipole hydrogen bonding between oxygen functional groups on biochar and the nitrogen atoms in MB molecules (Porto et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe formation of acidic oxygenated groups (e.g., \u0026ndash;COOH and \u0026ndash;OH) increases the availability of proton donors, explaining the marked reduction in pH observed for OxyAChar-3, which possessed the highest abundance of oxygen-containing functional groups. This finding aligns with previous studies by Chemerys et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Huff and Lee (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), who similarly attributed the pH decrease after H₂O₂ treatment to the enrichment of carboxyl and hydroxyl functionalities. Hence, the decline in pH serves as a clear indicator of successful surface oxidation. The electrical conductivity (EC) results further corroborate the FTIR findings. All OxyAChar samples exhibited lower EC compared to the unmodified control, consistent with observations by Kane et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who reported that a higher oxygen incorporation leads to reduced EC due to an increase in oxygen-to-carbon (O:C) ratio. This enhanced oxygenation increases surface acidity and promotes cation retention (e.g., K⁺, Mg\u0026sup2;⁺, and Ca\u0026sup2;⁺) through ion exchange, thereby minimizing leaching of exchangeable mineral cations and reducing the measured EC (Qian \u0026amp; Chen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The lowest EC observed at 3% H₂O₂ treatment reflects the most effective fixation of cations onto newly generated oxygenated sites, highlighting the optimal oxidation level for achieving balanced surface functionality and structural preservation.\u003c/p\u003e\u003cp\u003eBeyond functional enhancement, the potential impact of H₂O₂ oxidation on the long-term stability of biochar is an important consideration. Biochar is known to persist in soils for hundreds to thousands of years (Lehmann, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), making it a promising material for climate change mitigation, as it stabilizes carbon that would otherwise be released into the atmosphere through natural biomass decomposition. Therefore, assessing the stability of oxidized biochar is crucial not only for understanding its durability in soil remediation but also for evaluating its potential contributions to carbon sequestration and long-term soil carbon storage (Wang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To evaluate this, thermogravimetric analysis (TGA) was performed. Results revealed that all OxyAChar exhibited enhanced thermal stability, with lower total weight losses (26.8\u0026ndash;37.6%) than the unmodified control (44.15%). The most stable sample, OxyAChar-1 (27.3% weight loss), demonstrated that mild oxidation effectively removed labile organic fractions and volatile compounds while preserving the integrity of the carbon backbone. These findings are consistent with Zhao et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) observed that H₂O₂-modified biochar maintained structural resilience across five adsorption\u0026ndash;desorption cycles for bisphenol A removal, underscoring its durability and reusability. Collectively, these results indicate that carefully controlled H₂O₂ oxidation not only enhances surface functionality but also sustains, or even improves, the intrinsic stability of biochar, preserving its dual role as an effective adsorbent and a long-term carbon sink.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Excess H₂O₂ Oxidation Compromises Biochar Functionality\u003c/h2\u003e\u003cp\u003eHaving established that moderate oxidation (3% H₂O₂) optimizes both surface reactivity and structural stability, it is essential to evaluate how further increases in oxidant concentration influence these properties. At higher H₂O₂ concentrations (\u0026gt;\u0026thinsp;3%), the physicochemical and structural properties of PKS biochar exhibited a declining trend. FESEM and BET analyses revealed that excessive oxidation led to structural deterioration, likely due to the rapid release of excessive amount of oxygen that attacked and disrupted the existing carbon framework (Amin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This over-oxidation caused pore rupture, wall thinning, and partial collapse, resulting in reduced surface area and pore volume. Consequently, OxyAChar-10, OxyAChar-20, and OxyAChar-30 displayed markedly lower mesopore surface areas and total pore volumes, with OxyAChar-30 approaching values similar to the unmodified control, indicating a reversal of the structural enhancements observed at moderate oxidation levels.\u003c/p\u003e\u003cp\u003eFTIR spectra further supported these observations, showing diminished intensities of O\u0026ndash;H, C\u0026thinsp;=\u0026thinsp;O, and C\u0026ndash;O bands at higher oxidation levels, which suggests a loss of surface functional groups. This behavior arises from the dual nature of the H₂O₂ oxidation mechanism: at moderate concentrations (e.g., 3%), reactive \u0026bull;OH and \u0026bull;OOH radicals attack aromatic carbon sites, introducing oxygen-containing functional groups and enhancing surface reactivity. However, at excessive concentrations, over-oxidation promotes the decomposition of aromatic structures into volatile products such as CO₂ and low-molecular-weight organic acids (Sahoo, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This self-limiting oxidation process not only decreases the density of surface functionalities but also compromises the carbon framework, reducing both chemical and structural integrity.\u003c/p\u003e\u003cp\u003eThis reduced acidic oxygen functional groups at high oxidation levels also explains the partial rebound in pH observed for OxyAChar-10, OxyAChar-20, and OxyAChar-30, as well as the corresponding increase in electrical conductivity\u0026mdash;both indicative of fewer acidic oxygenated sites and reduced ion exchange capacity. These findings are consistent with those of Zhang et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Nguyen et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who reported that moderate oxidation optimizes surface polarity and functional group density of biochar, while excessive oxidation induces structural degradation and functional loss. Taken together, these results demonstrate that 3% H₂O₂ represents the optimal oxidation condition for PKS biochar, achieving a favorable balance between pore development, surface functionality, and structural stability. Beyond this threshold, the detrimental effects of over-oxidation outweigh the benefits, leading to decreased sorption capacity for methylene blue (MB) and reduced water-holding capacity (WHC).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Cross-Study Insights into H₂O₂ Oxidation of Biochar\u003c/h2\u003e\u003cp\u003eTo contextualize the present findings and identify generalizable trends across different feedstocks and experimental conditions, a comparative analysis of nine published studies was performed and summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. This synthesis aimed to determine whether the enhancement patterns observed in H₂O₂-modified palm kernel shell (PKS) biochar are consistent with those reported for other biochar and to elucidate how feedstock composition and treatment intensity influence physicochemical and adsorption performance. Across the reviewed studies, H₂O₂ oxidation consistently emerged as an effective, low-cost, and environmentally benign strategy to enhance biochar performance (Xue et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In agreement with the present results, moderate oxidation generally increased BET surface area (Chemerys et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Porto et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and introduced oxygen-containing functional groups such as carboxyl (\u0026ndash;COOH), hydroxyl (\u0026ndash;OH), and carbonyl (C\u0026thinsp;=\u0026thinsp;O), which play critical roles in improving adsorption and cation exchange capacity of biochar (Zhang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zuo et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Conversely, several studies have reported that excessive oxidation leads to structural degradation and loss of surface functionality (Nguyen et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This observation aligns with the decline in surface area, pore volume, methylene blue adsorption capacity, and water-holding capacity observed in PKS biochar treated with H₂O₂ concentrations above 3%, confirming that controlled oxidation is essential to preserve biochar structural integrity. Nevertheless, the optimal H₂O₂ concentration reported in the literature varies considerably among feedstocks, and even pyrolysis parameters can markedly influence the effectiveness of H₂O₂ oxidation. For instance, Cibati et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) observed that \u003cem\u003eMiscanthus \u0026times; giganteus\u003c/em\u003e biochar produced at 350\u0026deg;C and treated with 10% H₂O₂ exhibited enhanced Cu\u0026sup2;⁺ and Zn\u0026sup2;⁺ adsorption, whereas biochar produced at 600\u0026deg;C under identical treatment suffered pore collapse due to over-oxidation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative literature-reported H₂O₂ modifications of various biochar, including feedstock type, H₂O₂ treatment conditions, optimal oxidation concentrations, target adsorbates, and major findings.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiochar\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHydrogen peroxide (H₂O₂) treatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOptimal Concentration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTarget Adsorbate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKey findings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePinewood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1, 3, 10, 20, 30% w/w\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1% w/w\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigher H₂O₂ decreased MB adsorption beyond optimum; MB adsorption did not increase proportionally with CEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHuff and Lee (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSewage biosolids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,10, 20, 30, 40 vol %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 vol %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eH₂O₂ increased surface area \u0026amp; pore volume via degradation; oxygen groups formed during activation contribute to adsorption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePorto et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCymbopogon schoenanthus\u003c/em\u003e\u0026nbsp;L. Spreng\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,10, 20, 30% v/v\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20% v/v\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarboxyl groups responsible for enhanced Cu sorption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eZuo et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeanut shell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10, 20, 30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eH₂O₂ oxidation produces carboxyl and hydroxyl groups; excessive oxidation decomposes aromatic carbon, reducing binding sites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eZhang et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePhragmites australis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e5,15,30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBisphenol A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eH₂O₂ increases micropore \u0026amp; mesopore volumes, disrupts microcrystalline order; improved reusability (up to 5 cycles); follow pseudo-second-order kinetics and Freundlich isotherm; enhanced C\u0026thinsp;=\u0026thinsp;O, C\u0026ndash;O, \u0026ndash;OH bonds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eZhao et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTypha orientalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeanut Hull\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeavy metals (Pb, Cu, Cd, Ni)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAfter H₂O₂ modification, Pb removal comparable to commercial agents, using low-cost feedstock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eXue et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMiscanthus x giganteus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10% w/v\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCu, Zn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarboxyl groups enhance metal adsorption for biochar produced at 350\u0026deg;C, but at 600\u0026deg;C, severe oxidation destroys porosity, reducing adsorption capacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCibati et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaper waste sludge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,20,30,40,50,60% w/v\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40% w/v\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNH₄⁺\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eH₂O₂ modification increases surface functional groups; adsorption controlled by cation exchange, electrostatic attraction; mechanisms include π\u0026ndash;cation, complexation, ion exchange; kinetics pseudo-second-order; isotherm Langmuir\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNguyen et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirch wood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3, 15, 30% w/w\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30% w/w\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCEC increased\u0026thinsp;~\u0026thinsp;6\u0026times;; BET specific surface area increased\u0026thinsp;~\u0026thinsp;15\u0026times;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChemerys et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePalm kernel shell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,1,3,10,20,30% v/v\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3% v/v\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eH₂O₂ oxidation enhanced BET surface area, porosity (micro- and mesopores), and the abundance of oxygen-containing functional groups (\u0026ndash;COOH, \u0026ndash;OH, C\u0026thinsp;=\u0026thinsp;O), improving methylene blue adsorption and water retention.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThis study\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: MB\u0026thinsp;=\u0026thinsp;Methylene Blue; Cu\u0026thinsp;=\u0026thinsp;Copper; Pb\u0026thinsp;=\u0026thinsp;Lead; Cd\u0026thinsp;=\u0026thinsp;Cadmium; Ni\u0026thinsp;=\u0026thinsp;Nickel; NH₄⁺ = Ammonium; \u0026ldquo;NR\u0026rdquo; = Data not relevant to the study.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Toward AI-Guided Design of High-Performance H₂O₂-Modified Biochar\u003c/h2\u003e\u003cp\u003eThe inconsistency observed across studies clearly demonstrates that there is no universal or \u0026ldquo;one-size-fits-all\u0026rdquo; optimal H₂O₂ concentration for biochar modification. Such variability arises from the complex interplay among feedstock composition, pyrolysis temperature, oxidation intensity, and post-treatment conditions. Relying solely on conventional trial-and-error experimentation to optimize these parameters is both labor-intensive and inefficient, given the vast number of possible variable combinations. To address this challenge, recent studies have begun integrating machine learning (ML) as a data-driven tool to model, predict, and optimize biochar properties. Machine learning techniques can capture nonlinear interactions among biochar production parameters (e.g., feedstock composition, pyrolysis temperature, activation conditions, and oxidant concentration) and their corresponding functional outcomes (e.g., adsorption capacity, cation exchange capacity, or surface oxygenation). For instance, Alabdrabalnabi et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) applied the XGBoost algorithm to predict biochar yield with high accuracy (R\u0026sup2; = 0.96, RMSE\u0026thinsp;=\u0026thinsp;1.77), while Leng et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) used gradient boosting regression to predict nitrogen content across multiple feedstock types, achieving robust performance across forestry, agricultural, manure, and algae-based biochar. El Hanandeh et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Nguyen et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated the capability of machine learning models to predict heavy metal and nutrient adsorption with accuracies exceeding 99%, underscoring the strong predictive potential of machine learning in complex sorption systems. These findings highlight a transformative opportunity to extend data-driven modeling to biochar oxidation, particularly for optimizing H₂O₂ modification processes.\u003c/p\u003e\u003cp\u003eIn this context, machine learning can be trained on comprehensive datasets encompassing parameters such as surface functional group abundance, BET surface area, pore structure (micro-, meso-, and macropores), and oxidation conditions to identify the most influential variables governing adsorption capacity and water retention. Feature-importance analysis within machine learning frameworks can quantitatively rank these variables, revealing which physicochemical attributes most strongly determine biochar performance. Such insight enables the rational selection of modification parameters, streamlining process design and reducing empirical trial-and-error experimentation. Once trained, machine learning models can predict optimal H₂O₂ concentrations across diverse conditions, considering feedstock type, pyrolysis temperature, oxidation intensity, and even field-specific factors such as soil type or crop requirements. This predictive capability is difficult to achieve through traditional trial-and-error studies. Therefore, transitioning toward machine learning-assisted biochar design represents a powerful pathway to accelerate optimization, minimize experimental cost and time, and facilitate the large-scale adoption of biochar in sustainable agriculture.\u003c/p\u003e\u003c/div\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eThis study provides the first systematic evidence that controlled H₂O₂ oxidation at 3% optimizes the surface chemistry and structure of PKS biochar. Moderate H₂O₂ oxidation produces OxyAChar-3 with the highest BET surface area (520.21 m\u0026sup2;\u0026middot;g⁻\u0026sup1; micropores, 360.48 m\u0026sup2;\u0026middot;g⁻\u0026sup1; mesopores), well-developed pore connectivity, and abundant oxygen-containing functional groups (\u0026ndash;COOH, \u0026ndash;OH, C\u0026thinsp;=\u0026thinsp;O), thereby enhancing methylene blue adsorption and water retention. In contrast, excessive oxidation (\u0026gt;\u0026thinsp;3%) compromises structural integrity and functional performance, highlighting the importance of controlled treatment. Comparative analysis with the literature reveals the feedstock- and process-dependent variability that governs H₂O₂ oxidation effectiveness, emphasizing the potential of machine learning-assisted biochar design to predict optimal modification conditions, reduce trial-and-error experimentation, and accelerate the development of high-performance, sustainable biochar for soil remediation, water retention, and carbon sequestration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by Fundamental Research Grant Scheme FRGS/1/2023/STG01/UM/02/2 (Grant number FRGS: FP053-2023). Associate Professor Dr. Rosazlin Abdullah has received research support from Ministry of Education Malaysia.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Conceptualization and funding acquisition were performed by R.A., J.S.Y, and N.S.M.R. Methodology was developed jointly by R.A. and J.S.Y. Material preparation, data curation, formal analysis, and investigation were carried out by L.Z.. Project administration and resources were managed by R.A. and L.Z.. Software and visualization were handled by L.Z. under the supervision of R.A. and J.S.Y. The original draft was written by L.Z., and all authors contributed to writing\u0026mdash;review and editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdullah, N., \u0026amp; Sulaiman, F. (2013). The Oil Palm Wastes in Malaysia. In M. D. Matovic (Ed.), \u003cem\u003eBiomass Now - Sustainable Growth and Use\u003c/em\u003e. IntechOpen. https://doi.org/10.5772/55302 \u003c/li\u003e\n\u003cli\u003eAlabdrabalnabi, A., Gautam, R., \u0026amp; Sarathy, S. M. (2022). Machine learning to predict biochar and bio-oil yields from co-pyrolysis of biomass and plastics. \u003cem\u003eFuel\u003c/em\u003e,\u003cem\u003e 328\u003c/em\u003e, 125303. \u003c/li\u003e\n\u003cli\u003eAlves, B. S. Q., Fernandes, L. A., \u0026amp; Southard, R. J. (2021). 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Effect of H2O2 concentrations on copper removal using the modified hydrothermal biochar. \u003cem\u003eBioresource Technology\u003c/em\u003e,\u003cem\u003e 207\u003c/em\u003e, 262-267. https://doi.org/https://doi.org/10.1016/j.biortech.2016.02.032 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"biochar, hydrogen peroxide, surface oxidation, oxygen functional group, enhanced sorption capacity, potential soil remediation","lastPublishedDoi":"10.21203/rs.3.rs-7897807/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7897807/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHydrogen peroxide (H₂O₂) oxidation has emerged as a promising and sustainable strategy to enhance the surface chemistry and functional performance of biochar due to its environmentally friendly nature compared to other modification methods. This study systematically investigated the effect of controlled H₂O₂ oxidation (1–30%) on palm kernel shell (PKS) biochar (termed OxyAChar), evaluating its physicochemical characteristics, sorption behavior, water retention, and thermal stability. The results revealed that moderate oxidation (3% H₂O₂) produced OxyAChar-3 with the highest BET surface area (520.21 m²·g⁻¹ micropores, 360.48 m²·g⁻¹ mesopores), improved pore connectivity, and enriched oxygen-containing functional groups. These modifications significantly enhanced methylene blue sorption capacity and water retention ability biochar. The observed effects were attributed to persistent free radicals (PFRs) on the biochar surface, which catalyzed H₂O₂ decomposition into reactive •OH and •OOH radicals, promoting delignification and surface oxygenation. However, excessive oxidation (\u0026gt;3%) disrupted structural integrity of pores and reduced functional group density, thereby decreasing sorption and water retention efficiency of biochar. Comparative analysis with existing literature confirmed that optimal H₂O₂ concentrations are both feedstock- and pyrolysis-dependent. These insights highlight the potential of machine learning-assisted biochar design to predict ideal oxidation parameters, minimize experimental trial-and-error, and accelerate the development of high-performance, sustainable biochar materials. Overall, this study provides a mechanistic and practical framework for the rational design of surface-oxidized biochar for future applications in soil remediation, water conservation, and long-term carbon sequestration.\u003c/p\u003e","manuscriptTitle":"Engineering biochar through surface oxygenation: a green approach for sustainable environmental applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 11:30:17","doi":"10.21203/rs.3.rs-7897807/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T17:28:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T19:00:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T11:02:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-08T02:07:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140872731062415185550837433336646787906","date":"2025-11-05T17:57:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"340024794693582820953561259484662590562","date":"2025-11-04T00:06:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173232256338130281905044980316675707840","date":"2025-11-03T23:14:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111987050639149682422928079924134630699","date":"2025-11-03T17:48:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-03T17:02:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-26T16:12:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-21T12:44:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clean Technologies and Environmental Policy","date":"2025-10-19T10:02:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"071bc4bc-10e7-4723-a658-2a355525dae2","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:03:24+00:00","versionOfRecord":{"articleIdentity":"rs-7897807","link":"https://doi.org/10.1007/s10098-026-03469-w","journal":{"identity":"clean-technologies-and-environmental-policy","isVorOnly":false,"title":"Clean Technologies and Environmental Policy"},"publishedOn":"2026-03-31 15:58:18","publishedOnDateReadable":"March 31st, 2026"},"versionCreatedAt":"2025-11-13 11:30:17","video":"","vorDoi":"10.1007/s10098-026-03469-w","vorDoiUrl":"https://doi.org/10.1007/s10098-026-03469-w","workflowStages":[]},"version":"v1","identity":"rs-7897807","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7897807","identity":"rs-7897807","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}