Enhancing Biochar-Based Nonradical Persulfate Activation Using Data-Driven Techniques

Revisit the actual roles of catalytic sites in a Fenton-like system

The perception of catalytic site contributions in peroxymonosulfate (PMS)/biochar systems is biased due to the neglect of active site interactions. Here, random forest regression (RF), supporting vector regression (SVR), XGBoost (XGB), and gradient boosting decision tree (GBDT) are selected to construct models using active sites (A model) instead of elements (E model) as input features to revisit the relationship between C, N, and O-containing sites on biochar surface and Fenton-like activity. Consequently, the A models achieve twice the accuracy of the E models. For individual sites, a low CC or high CN concentration promotes the degradation of electron-donating organics in PMS systems, while system activity initially increases and then declines with rising CO concentration. Considering site interactions, CC&CO, CC&CN, and CO&CN show excellent synergy for PMS activation. Specifically, the Gibbs free energy (ΔG) of PMS at CC&CO (0.44 eV), CN&CO (0.78 eV), and CC&CN (0.82 eV) is significantly lower than that of single CC (2.26 eV), CN (1.44 eV), and CO&CO (1.14 eV) during the transition state formation process. The reduced ΔG facilitates OO bond cleavage, enhancing the generation of active species. This study employs A_SVR model and density functional theory (DFT) calculations to clarify the structure-activity relationships of biochar by considering the synergistic effects of active site. The results contribute to the precise design of Fenton-like catalysts for targeted pollutant degradation, improving water purification efficiency.

Machine learning-optimized advanced oxidation for enhanced sludge dewatering: EPS mechanistic insights and predictive modeling

The recalcitrant nature of extracellular polymeric substances (EPS) in sewage sludge severely limits dewatering efficiency. While advanced oxidation processes (AOPs) disrupt EPS matrices, their optimization remains challenging. This study integrates machine learning (ML) with AOPs to establish predictive frameworks for parameter optimization. A Bayesian-optimized XGBoost model (test R² = 0.87, based on a 70/30 train-test split) outperformed other algorithms in predicting optimal AOP configurations, while an AdaBoost-based model (test R² = 0.81) provided mechanistic insights. Radical donor and catalyst concentrations exhibited synergistic effects (r > 0.8) in hydroxyl radical generation, with pH and VS/TS ratio critically influencing EPS dynamics. Soluble EPS (S-EPS) dominated dewaterability control, whereas tightly bound EPS showed negligible impact. SHAP (SHapley Additive exPlanations) analysis identified radical donor dosage, catalyst loading, and pH as pivotal operational parameters, with acidic conditions enhancing EPS disruption. This work advances data-driven AOP optimization for sludge management, highlighting the need for dynamic EPS transformation studies and adaptive control systems to achieve sustainable wastewater treatment.

Efficient methane fermentation from the waste of a novel straw alkali-heat pretreatment-butyric acid fermentation process

ABSTRACTThe butyric acid biorefinery technology for straw is highly significant for environmental protection and the restructuring of the energy system. However, this process produces waste from alkali-heat pretreatment (PW) and butyric acid fermentation (FW). In this study, the feasibility of methane fermentation from the wastes was confirmed, with the methane production from PW and FW of 351.1 ± 11.8 and 741.5 ± 14.2 mLCH4/gVS, respectively. The initial pH and VFW/VPW of methane fermentation using the mixed waste of PW and FW were optimized at 7.5 and 1.8, respectively. The methane fermentation using the mixed waste was also verified by operating two anaerobic digesters in sequencing batch mode. At the VFW/VPW of 0.25 (actual ratio), methane production was 301.20 mLCH4/gVS with the waste load of 0.64 kgVS/m³/d. When the VFW/VPW was 1.8 (optimal ratio), methane production reached 396.45 mLCH4/gVS at the waste load of 1.20 kgVS/m3/d. This study facilitates the comprehensive utilization of all components within rice straw.

Electron transfer tuning for persulfate activation via the radical and non-radical pathways with biochar mediator

Electron mediator-based in-situ chemical oxidation (ISCO) offers a novel strategy for groundwater remediation due to diverse reaction pathways. However, distinguishing and further tuning the reaction pathway remains challenging. Herein, biochar as an electron mediator targeted active peroxysulphate (PDS) via the radical or non-radical pathway. Exemplified by the triazin pesticides removal, the complex radical (•OH and SO4•-) and non-radical active species (electron transfer oxidation) were generated and identified in different biochar/PDS systems. The electron transfer process between biochar and PDS was significantly distinguished via an innovatively in-situ visualization of radical pathway, and the electron transfer oxidation non-radical pathway is directly unveiled via a galvanic cell experiment combined with LC-MS analyses. The electron transfer mechanism was revealed via establishing the quantitative structure-activity relationships between biochar and ln kobs. The redox capacity of biochar was assessed as a key for tuning the atrazine degradation by non-radical pathway, and the surface carbon-centered persistent free radicals (PFRs) were identified as key electron donors for triggering the radical pathway. This study gives new insights into the electron transfer mechanism during tuning radical and non-radical activation pathways and the enhanced utilization of oxidants in ISCO technology.

Enhanced iodinated disinfection byproducts formation in iodide/iodate-containing water undergoing UV-chloramine sequential disinfection: Machine learning-aided identification of reaction mechanisms

Restricted to the complex nature of dissolved organic matter (DOM) in various aquatic environments, the mechanisms of enhanced iodinated disinfection byproducts (I-DBPs) formation in water containing both I- and IO3- (designated as I-/IO3- in this study) during the ultraviolet (UV)-chloramine sequential disinfection process remains unclear. In this study, four machine learning (ML) models were established to predict I-DBP formation by using DOM and disinfection features as input variables. Extreme gradient boosting (XGB) algorithm outperformed the others in model development using synthetic waters and in cross-dataset generalization of surface waters. Shapley additive explanation (SHAP) analysis, partial dependence plots (PDPs), and individual conditional expectation (ICE) analysis were then employed to explain the models' workings and feature interactions, aiding in identification and quantification of underlying mechanisms. A type of DOM component (namely DC_b) was found as the greatest contributor and identified as reduced quinones associated with broken-down lignin within higher plant-derived fulvic substance, serving as precursors and electron shuttles for I-DBP formation. Based on the interactional effects acquired from explanation results, the ejection of e-aq from excited DOM and pre-existing I- in the I-/IO3- system were identified responsible for the enhanced generation of I-DBPs compared to that in the I- or IO3- alone systems; extra DOM scavenged reactive iodine species (RIS), contributing to a limited enhancement. These findings and the methodology developed here together enhance our understanding of the mechanisms how DOM limitedly promotes I-DBP formation during UV-chloramine sequential disinfection of I-/IO3--containing water and facilitate effective online monitoring in the future.

Intelligent leaching of Zn and Mn from spent disposable batteries to avoid traditional optimizing experiments

Spent disposable Zn-Mn and Zn-C batteries are important resources for recycling. Acid leaching is the crucial step in the hydrometallurgy process for recycling Zn and Mn from these spent Zn-based batteries. However, to obtain the optimal leaching efficiency, the uncontrollable components in waste feed and various leaching parameters cause numerous replicated optimal experiments, increasing the recovery cost and environmental risks. To solve the issues, we employed machine learning (ML) techniques to construct models to predict Zn and Mn leaching from spent disposable batteries without optimizing experiments. Among four ML algorithms tested, the extreme gradient boosting demonstrated superior predictive performance, achieving an R2 of 0.85-0.98 across the training, test, and verification datasets. An analysis of feature importance indicated that the particle size, waste composition, acid concentration, temperature, and time affected the metal leaching most. This study also revealed the interaction effects of the waste properties and leaching process on the metal leaching. Furthermore, we created a user-friendly graphical user interface (GUI) that enables quick acquisition of metal leaching results, requiring only the measurement of waste particle size and component. Finally, experimental verification confirmed the practicability of the GUI. This study achieves intelligent metal leaching from spent batteries and overcomes the high recovery cost and environmental risks associated with traditional experimental optimizing methods.

Natural factor-based spatial prediction and source apportionment of typical heavy metals in Chinese surface soil: Application of machine learning models

Predicting the natural distribution of heavy metals (HMs) in soil is important to understand the potential risk of pollution. However, suitable technologies are still lacking for wide scale due to the large spatial heterogeneity. In this study, we developed machine learning models for predicting natural contents of five typical HMs in soil, including As, Cd, Cr, Hg and Pb. It was found that the optional random forest (RF) model had the best performance with the R2 up to 0.64. Based on this model, potential distribution of the five HMs explored that elevated contents were mainly concentrated in the southwest and south central of China. Feature analysis illustrated that importance of natural factors followed the order of geological attributes > soil properties > climatic conditions > ecological functions. In particular, lithology of the parent material dominated the content of metals, with the contributions of 18-25%. Moreover, soil properties of pH, cation exchange capacity, profile depth of soil and vegetation coverage had different influences on HMs, due to the variability in the properties of different HMs. This study developed a mapping relationship between natural factors and soil HMs by data science method, which may provide instructive information for pollution control and planning decisions.

Machine learning prediction of fundamental sewage sludge biochar properties based on sludge characteristics and pyrolysis conditions

Sewage sludge biochar (SSBC) has significant potential for resource recovery from sewage sludge (SS) and has been widely studied and applied across various fields. However, the variability in SSBC properties, resulting from the diverse nature of SS and its intricate interaction with pyrolysis conditions, presents notable challenges to its practical use. This research employed machine learning techniques to predict fundamental SSBC properties, including elemental content, proximate compositions, surface area, and yield, which are essential for assessing the applicability of SSBC. The models achieved robust predictive accuracy (test R2 = 0.82-0.95), except for surface area. Notably, analysis of the optimal models revealed SS characteristics had a significant impact on SSBC properties, particularly total and fixed carbon content (combined importance exceeding 80%). This underscores the needs of source analysis and preparation optimization in targeted SS recovery or SSBC applications. To facilitate this, a graphical user interface was developed for strategic analyzation of sludge sources and pyrolysis settings.

Evaluation of the immobilized enzymes function in soil remediation following polycyclic aromatic hydrocarbon contamination

The bioremediation of polycyclic aromatic hydrocarbon (PAHs) from soil utilizing microorganisms, enzymes, microbial consortiums, strains, etc. has attracted a lot of interest due to the environmentally friendly, and cost-effective features. Enzymes can efficiently break down PAHs in soil by hydroxylating the benzene ring, breaking the C-C bond, and catalyze the hydroxylation of a variety of benzene ring compounds via single-electron transfer oxidation. However, the practical application is limited by its instability and ease to loss function under harsh environmental conditions such as pH, temperature, and edaphic stress etc. Therefore, this paper focused on the techniques used to immobilize enzymes and remediate PAHs in soil. Moreover, previous research has not adequately covered this topic, despite the employment of several immobilized enzymes in aqueous solution cultures to remediate other types of organic pollutants. Bibliometric analysis further highlighted the research trends from 2000 to 2023 on this field of growing interest and identified important challenges regarding enzyme stability and interaction with soil matrices. The findings indicated that immobilized enzymes may catalyzed PAHs via oxidation of OH groups in benzene rings, and generate benzyl radicals (i.e., •OH and •O2) that undergo further reaction and release water. As a result, the intermediate products of PAHs further catalyze by enzyme and enzyme induced microbes producing carbon dioxide and water. Meanwhile efficiency, activity, lifetime, resilience, and sustainability of immobilized enzyme need to be further improved for the large-scale and field-scale clean-up of PAHs polluted soils. This could be possible by integrating enzyme-based with microbial and plant-based remediation strategies. It can be coupled with another line of research focused on using a new set of support materials that can be derived from natural resources.

Task decomposition strategy based on machine learning for boosting performance and identifying mechanisms in heterogeneous activation of peracetic acid process

Heterogeneous activation of peracetic acid (PAA) process is a promising method for removing organic pollutants from water. Nevertheless, this process is constrained by several complex factors, such as the selection of catalysts, optimization of reaction conditions, and identification of mechanism. In this study, a task decomposition strategy was adopted by combining a catalyst and reaction condition optimization machine learning (CRCO-ML) model and a mechanism identification machine learning (MI-ML) model to address these issues. The Categorical Boosting (CatBoost) model was identified as the best-performing model for the dataset (1024 sets and 7122 data points) in this study, achieving an R2 of 0.92 and an RMSE of 1.28. Catalyst composition, PAA dosage, and catalyst dosage were identified as the three most important features through SHAP analysis in the CRCO-ML model. The HCO3- is considered the most influential water matrix affecting the k value. The errors between all reverse experiment results and the predictions of the CRCO-ML and MI-ML models were <10 % and 15 %, respectively. This interdisciplinary work provides novel insights into the design and application of the heterogeneous activation of PAA process, significantly contributing to the rapid development of this technology.

Sludge-derived hydrochar modulates complete nonradical electron transfer in peroxydisulfate activation via pyrrolic-N and carbon defect: Implication for degrading electron-rich ionizable anilines compounds

Nonradical electron transfer process (ETP) is a promising pathway for pollutant degradation in peroxydisulfate-based advanced oxidation processes (PDS-AOPs). However, there is a critical bottleneck to trigger ETP by sludge-derived hydrochar due to its negatively charged surface, inferior porosity and electrical conductivity. Herein, pyrrolic-N doped and carbon defected sludge-derived hydrochar (SDHC-N) was constructed for PDS activation to degrade anilines ionizable organic compounds (IOC) through complete nonradical ETP oxidation. Degradation of anilines IOC was not only affected by the electron-donating capacity but also proton concentration in solution because of the ionizable amino group (-NH2). Diverse effects including proton favor, insusceptible and inhibition were observed. Impressively, addition of HCO3 with strong proton binding capacity boosted aniline degradation nearly 10 times. Moreover, characterizations and theoretical calculations demonstrated that pyrrolic-N increased electron density and created positively charged surface, profoundly promoting generation of SDHC-N-S2O82-* complexes. More delocalized electrons around carbon defect could enhance electron mobility. This work guides a rational design of sludge-derived hydrochar to mediate nonradical ETP oxidation, and provides insights into the impacts of proton on anilines IOC degradation.

New Strategies for constructing and analyzing semiconductor photosynthetic biohybrid systems based on ensemble Machine learning Models: Visualizing complex mechanisms and yield prediction

Photosynthetic biohybrid systems (PBSs) composed of semiconductor-microbial hybrids provide a novel approach for converting light into chemical energy. However, comprehending the intricate interactions between materials and microbes that lead to PBSs with high apparent quantum yields (AQY) is challenging. Machine learning holds promise in predicting these interactions. To address this issue, this study employs ensemble learning (ESL) based on Random Forest, Gradient Boosting Decision Tree, and eXtreme Gradient Boosting to predict AQY of PBSs utilizing a dataset comprising 15 influential factors. The ESL model demonstrates exceptional accuracy and interpretability (R2 value of 0.927), offering insights into the impact of these factors on AQY while facilitating the selection of efficient semiconductors. Furthermore, this research propose that efficient charge carrier separation and transfer at the bio-abiotic interface are crucial for achieving high AQY levels. This research provides guidance for selecting semiconductors suitable for productive PBSs while elucidating mechanisms underlying their enhanced efficiency.

Asymmetric defective sites-mediated high-valent cobalt-oxo species in self-suspension aerogel platform for efficient peroxymonosulfate activation

The main pressing problems should be solved for heterogeneous catalysts in activation of peroxymonosulfate (PMS) are sluggish mass transfer kinetics and low intrinsic activity. Here, oxygen vacancies (Vo)-rich of Co3O4 nanosheets were anchored on the superficies of spirulina-based reduced graphene oxide-konjac glucomannan (KGM) aerogel (R-Co3O4-x/SRGA). The porous structure and superhydrophilicity conferred by KGM maximized the diffusion and transport of reactant. More interestingly, R-Co3O4-x/SRGA came true self-suspension rather than conventional self-floating without the aid of external force, maximizing space utilization and facilitating catalysts recovery. Anchored R-Co3O4-x nanosheets acted as "engines" to drive the reaction. Density functional theory (DFT) manifested Vo was capable of breaking the symmetry of the electronic structure of Co3O4. The formation of asymmetric active sites (Vo) was revealed to modulate the d-band center, enhanced affinity for PMS, and promoted evolution of high-valent cobalt-oxo (Co(IV)=O) species. R-Co3O4-x/SRGA achieved complete removal of sulfamethoxazole (SMX) within 12 min. Furthermore, R-Co3O4-x/SRGA demonstrated exceptional stability in the presence of various environmental interference factors and continuous flow device. This insightful work cleverly integrates the macroscopic design of structure, and the microscopic regulation of active sites is expected to open up new opportunities for the development of water treatment.

Machine learning-assisted optimization of food-grade spirulina cultivation in seawater-based media: From laboratory to large-scale production

The shortage of food and freshwater sources threatens human health and environmental sustainability. Spirulina grown in seawater-based media as a healthy food is promising and environmentally friendly. This study used three machine learning techniques to identify important cultivation parameters and their hidden interrelationships and optimize the biomass yield of Spirulina grown in seawater-based media. Through optimization of hyperparameters and features, eXtreme Gradient Boosting, along with the recursive feature elimination (RFE) model demonstrated optimal performance and identified 28 important features. Among them, illumination intensity and initial pH value were critical determinants of biomass, which impacted other features. Specifically, high initial pH values (>9.0) mainly increased biomass but also increased nutrient sedimentation and ammonia (NH3) losses. Both batch and continuous additions could decrease nutrient losses by increasing their availability in the seawater-based media. When illumination intensity exceeded 200 μmol photons/m2/s, it amplified the growth of Spirulina by mitigating the light attenuation caused by a high initial inoculum level and counteracted the negative effect of low temperature (<25 °C). In large-scale cultivation, production efficiency would be reduced if illumination was not maintained at a high level. High salinity and sodium bicarbonate (NaHCO3) addition promoted carbohydrate accumulation, but suitable dilution could keep the required protein content in Spirulina with relatively low media and production costs. These findings reveal the interactive influence of cultivation parameters on biomass yield and help us determine the optimal cultivation conditions for large-scale cultivation of Spirulina-based seawater system based on a developed graphical user interface website.

Hollow Nanoreactors Unlock New Possibilities for Persulfate-Based Advanced Oxidation Processes

As a novel type of catalytic material, hollow nanoreactors are expected to bring new development opportunities in the field of persulfate-based advanced oxidation processes due to their peculiar void-confinement, spatial compartmentation, and size-sieving effects. For such materials, however, further clarification on basic concepts and construction strategies, as well as a discussion of the inherent correlation between structure and catalytic activity are still required. In this context, this review aims to provide a state-of-the-art overview of hollow nanoreactors for activating persulfate. Initially, hollow nanoreactors are classified according to the constituent components of the shell structure and their dimensionality. Subsequently, the different construction strategies of hollow nanoreactors are described in detail, while common synthesis methods for these construction strategies are outlined. Furthermore, the most representative advantages of hollow nanoreactors are summarized, and their intrinsic connections to the nanoreactor structure are elucidated. Finally, the challenges and future prospects of hollow nanoreactors are presented.

Machine learning and experimentally exploring the controversial role of nitrogen in CO2 uptake by waste-derived nitrogen-containing porous carbons

Waste-derived nitrogen-containing porous carbons were widely accepted as promising carbon capture materials. However, roles of nitrogen in CO2 uptake were highly controversial, posing a challenge in designing high CO2 uptake porous carbons. Herein, nitrogen-containing species was firstly introduced into machine learning (ML) models to uncover the complex relationship of nitrogen, micropore and CO2 uptake by combining ML models, DFT computations and experiments. The results revealed that micropore volume (Vmicro) was the most important property influencing CO2 uptake, but was not the only determinant factor. Nitrogen-containing species (pyrrolic/pyridonic-N (N5) and pyridinic-N (N6)) rather than total nitrogen content, also played an essential role. On the one hand, they can enhanced CO2 adsorption by Lewis acid-base and hydrogen bonding. On the other hand, they promoted development of micropores by participating in activation reactions. The model further indicated that excessive N5 (>1.5 wt%) or N6 (>1.7 wt%) led to restriction on developments of micropores, which was attributed to enlargement of pore size, collapses or blockage of micropores. The double edged-sword effect of N5 and N6 on changes of microporous structures was responsible for the long-standing controversy over nitrogen. The result was further verified by synthesizing eight porous carbons with different textural and chemical properties. This study provided not only a new perspective for resolving the controversy of nitrogen in CO2 uptake, but also a graphical user interface prediction software meaningful for designing porous carbons.

Progress of metal-loaded biochar-activated persulfate for degradation of emerging organic contaminants

In recent years, studies on the degradation of emerging organic contaminants by sulfate radical (SO4-·) based advanced oxidation processes (SR-AOPs) have triggered increasing attention. Metal-loaded biochar (Me-BC) can effectively prevent the agglomeration and leaching of transition metals, and its good physicochemical properties and abundant active sites induce outstanding in activating persulfate (PS) for pollutant degradation, which is of great significance in the field of advanced oxidation. In this paper, we reviewed the preparation method and stability of Me-BC, the effect of metal loading on the physicochemical properties of biochar, the pathways of pollutant degradation by Me-BC-activated PS (including free radical pathways: SO4-·, hydroxyl radical (·OH), superoxide radicals (O2-·); non-free radical pathways: singlet oxygen (1O2), direct electron transfer), and discussed the activation of different active sites (including metal ions, persistent free radicals, oxygen-containing functional groups, defective structures, etc.) in the SR-AOPs system. Finally, the prospect was presented for the current research progress of Me-BC in SR-AOPs technology.

Optimization of a Novel Engineered Ecosystem Integrating Carbon, Nitrogen, Phosphorus, and Sulfur Biotransformation for Saline Wastewater Treatment Using an Interpretable Machine Learning Approach

The denitrifying sulfur (S) conversion-associated enhanced biological phosphorus removal (DS-EBPR) process for treating saline wastewater is characterized by its unique microbial ecology that integrates carbon (C), nitrogen (N), phosphorus (P), and S biotransformation. However, operational instability arises due to the numerous parameters and intricates bacterial interactions. This study introduces a two-stage interpretable machine learning approach to predict S conversion-driven P removal efficiency and optimize DS-EBPR process. Stage one utilized the XGBoost regression model, achieving an R2 value of 0.948 for predicting sulfate reduction (SR) intensity from anaerobic parameters with feature engineering. Stage two involved the CatBoost classification and regression model integrating anoxic parameters with the predicted SR values for predicting P removal, reaching an accuracy of 94% and an R2 value of 0.93, respectively. This study identified key environmental factors, including SR intensity (20-45 mg S/L), influent P concentration (<9.0 mg P/L), mixed liquor volatile suspended solids (MLVSS)/mixed liquor suspended solids (MLSS) ratio (0.55-0.72), influent C/S ratio (0.5-1.0), anoxic reaction time (5-6 h), and MLSS concentration (>6.50 g/L). A user-friendly graphic interface was developed to facilitate easier optimization and control. This approach streamlines the determination of optimal conditions for enhancing P removal in the DS-EBPR process.

Lignocellulosic biomass for biochar production: A green initiative on biowaste conversion for pharmaceutical and other emerging pollutant removal

Lignocellulosic waste generation and their improper disposal has accelerated the problems associated with increased greenhouse gas emissions and associated environmental pollution. Constructive ways to manage and mitigate the pollution associated with lignocellulosic waste has propelled the research on biochar production using lignocellulose-based substrates. The sustainability of various biochar production technologies in employing lignocellulosic biomass as feedstock for biochar production not only aids in the lignocellulosic biomass valorization but also helps in carbon neutralization and carbon utilization. Functionalization of biochar through various physicochemical methods helps in improving their functional properties majorly by reducing the size of the biochar particles to nanoscale and modifying their surface properties. The usage of engineered biochar as nano adsorbents for environmental applications like dye absorption, removal of organic pollutants and endocrine disrupting compounds from wastewater has been the thrust areas of research in the past few decades. This review presents a comprehensive outlook on the up-to-date research findings related to the production and engineering of biochar from lignocellulosic biomass and their applications in environmental remediation especially with respect to wastewater treatment. Further a detailed discussion on various biochar activation methods and the future scope of biochar research is presented in this review work.

Factors driving antimony accumulation in soil-pakchoi and wheat agroecosystems: Insights and predictive models

The accumulation of antimony (Sb) in plants and its potential effects on human health are of increasing concern. Nevertheless, only a few countries or regions have established soil Sb thresholds for agricultural purposes, and soil properties have not been taken into account. This study investigated the accumulation of Sb in the edible parts of pakchoi and wheat grain by adding exogenous Sb to 21 soils with varying properties. The results revealed a positive correlation between bioavailable Sb (Sbava, extracted by 0.1 M K2HPO4) in soil and Sb in the edible parts of pakchoi (R2 = 0.77, p < 0.05) and wheat grain (R2 = 0.54, p < 0.05). Both machine learning and traditional multiple regression analysis indicated Sbava was the most critical feature and the main soil properties that contributed to Sb uptake by pakchoi and wheat were CaCO3 and clay, respectively. The advisory food limits for Sb in pakchoi and wheat were estimated based on health risk assessment, and used to derive soil thresholds for safe pakchoi and wheat production based on Sbtot and Sbava, respectively. These findings hold potential for predicting Sb uptake by crops with different soil properties and informing safe production management strategies.

Application of machine learning in the study of cobalt-based oxide catalysts for antibiotic degradation: An innovative reverse synthesis strategy

This study addresses antibiotic pollution in global water bodies by integrating machine learning and optimization algorithms to develop a novel reverse synthesis strategy for inorganic catalysts. We meticulously analyzed data from 96 studies, ensuring quality through preprocessing steps. Employing the AdaBoost model, we achieved 90.57% accuracy in classification and an R²value of 0.93 in regression, showcasing strong predictive power. A key innovation is the Sparrow Search Algorithm (SSA), which optimizes catalyst selection and experimental setup tailored to specific antibiotics. Empirical experiments validated SSA's efficacy, with degradation rates of 94% for Levofloxacin and 97% for Norfloxacin, aligning closely with predictions within a 2% margin of error. This research advances theoretical understanding and offers practical applications in material science and environmental engineering, significantly enhancing catalyst design efficiency and accuracy through the fusion of advanced machine learning techniques and optimization algorithms.

Machine learning-based exploration of biochar for environmental management and remediation

Biochar has a wide range of applications, including environmental management, such as preventing soil and water pollution, removing heavy metals from water sources, and reducing air pollution. However, there are several challenges associated with the usage of biochar for these purposes, resulting in an abundance of experimental data in the literature. Accordingly, the purpose of this study is to examine the use of machine learning in biochar processes with an eye toward the potential of biochar in environmental remediation. First, recent developments in biochar utilization for the environment are summarized. Then, a bibliometric analysis is carried out to illustrate the major trends (demonstrating that the top three keywords are heavy metal, wastewater, and adsorption) and construct a comprehensive perspective for future studies. This is followed by a detailed review of machine learning applications, which reveals that adsorption efficiency and capacity are the primary utilization targets in biochar utilization. Finally, a comprehensive perspective is provided for the future. It is then concluded that machine learning can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties which can be later used for decision-making, resource allocation, and environmental management.

Machine-Learning-Assisted Descriptors Identification for Indoor Formaldehyde Oxidation Catalysts

The development of highly efficient catalysts for formaldehyde (HCHO) oxidation is of significant interest for the improvement of indoor air quality. Up to 400 works relating to the catalytic oxidation of HCHO have been published to date; however, their analysis for collective inference through conventional literature search is still a challenging task. A machine learning (ML) framework was presented to predict catalyst performance from experimental descriptors based on an HCHO oxidation catalysts database. MnOx, CeO2, Co3O4, TiO2, FeOx, ZrO2, Al2O3, SiO2, and carbon-based catalysts with different promoters were compiled from the literature. Notably, 20 descriptors including reaction catalyst composition, reaction conditions, and catalyst physical properties were collected for data mining (2263 data points). Furthermore, the eXtreme Gradient Boosting algorithm was employed, which successfully predicted the conversion efficiency of HCHO with an R-square value of 0.81. Shapley additive analysis suggested Pt/MnO2 and Ag/Ce-Co3O4 exhibited excellent catalytic performance of HCHO oxidation based on the analysis of the entire database. Validated by experimental tests and theoretical simulations, the key descriptor identified by ML, i.e., the first promoter, was further described as metal-support interactions. This study highlights ML as a useful tool for database establishment and the catalyst rational design strategy based on the importance of analysis between experimental descriptors and the performance of complex catalytic systems.

Improving prediction of N2O emissions during composting using model-agnostic meta-learning

Nitrous oxide (N2O) represents a significant environmental challenge as a harmful, long-lived greenhouse gas that contributes to the depletion of stratospheric ozone and exacerbates global anthropogenic greenhouse warming. Composting is considered a promising and economically feasible strategy for the treatment of organic waste. However, recent research indicates that composting is a source of N2O, contributing to atmospheric pollution and greenhouse effect. Consequently, there is a need for the development of effective, cost-efficient methodologies to quantify N2O emissions accurately. In this study, we employed the model-agnostic meta-learning (MAML) method to improve the performance of N2O emissions prediction during manure composting. The highest R2 and lowest root mean squared error (RMSE) values achieved were 0.939 and 18.42 mg d-1, respectively. Five machine learning methods including the backpropagation neural network, extreme learning machine, integrated machine learning method based on ELM and random forest, gradient boosting decision tree, and extreme gradient boosting were adopted for comparison to further demonstrate the effectiveness of the MAML prediction model. Feature analysis showed that moisture content of structure material and ammonium concentration during composting process were the two most significant features affecting N2O emissions. This study serves as proof of the application of MAML during N2O emissions prediction, further giving new insights into the effects of manure material properties and composting process data on N2O emissions. This approach helps determining the strategies for mitigating N2O emissions.

Active Learning-Based Guided Synthesis of Engineered Biochar for CO2 Capture

Biomass waste-derived engineered biochar for CO2 capture presents a viable route for climate change mitigation and sustainable waste management. However, optimally synthesizing them for enhanced performance is time- and labor-intensive. To address these issues, we devise an active learning strategy to guide and expedite their synthesis with improved CO2 adsorption capacities. Our framework learns from experimental data and recommends optimal synthesis parameters, aiming to maximize the narrow micropore volume of engineered biochar, which exhibits a linear correlation with its CO2 adsorption capacity. We experimentally validate the active learning predictions, and these data are iteratively leveraged for subsequent model training and revalidation, thereby establishing a closed loop. Over three active learning cycles, we synthesized 16 property-specific engineered biochar samples such that the CO2 uptake nearly doubled by the final round. We demonstrate a data-driven workflow to accelerate the development of high-performance engineered biochar with enhanced CO2 uptake and broader applications as a functional material.

A Meta-Analysis to Revisit the Property-Aggregation Relationships of Carbon Nanomaterials: Experimental Observations versus Predictions of the DLVO Theory

Contradicting relationships between physicochemical properties of nanomaterials (e.g., size and ζ-potential) and their aggregation behavior have been constantly reported in previous literature, and such contradictions deviate from the predictions of the classic Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. To resolve such controversies, in this work, we employed a meta-analytic approach to synthesize the data from 46 individual studies reporting the critical coagulation concentration (CCC) of two carbon nanomaterials, namely, graphene oxide (GO) and carbon nanotube (CNT). The correlations between CCC and material physicochemical properties (i.e., size, ζ-potential, and surface functionalities) were examined and compared to the theoretical predictions. Results showed that the CCC of electrostatically stabilized carbon nanomaterials increased with decreasing nanomaterial size when their hydrodynamic sizes were smaller than ca. 200 nm. This is qualitatively consistent with the prediction of the DLVO theory but with a smaller threshold size than the predicted 2 μm. Above the threshold size, the material ζ-potential can be correlated to CCC for nanomaterials with moderate/low surface charge, in agreement with the DLVO theory. The correlation was not observed for highly charged nanomaterials because of their underestimated surface potential by the ζ-potential. Furthermore, a correlation between the C/O ratio and CCC was observed, where a lower C/O ratio resulted in a higher CCC. Overall, our findings rationalized the inconsistency between experimental observation and theoretical prediction and provided essential insights into the aggregation behavior of nanomaterials in water, which could facilitate their rational design.

Biochar and surfactant synergistically enhanced PFAS destruction in UV/sulfite system at neutral pH

The UV/sulfite-based advanced reduction process (ARP) emerges as an effective strategy to combat per- and polyfluoroalkyl substances (PFAS) pollution in water. Yet, the UV/sulfite-ARP typically operates at highly alkaline conditions (e.g., pH > 9 or even higher) since the generated reductive radicals for PFAS degradation can be quickly sequestered by protons (H+). To overcome the associated challenges, we prototyped a biochar-surfactant-system (BSS) to synergistically enhance PFAS sorption and degradation by UV/sulfite-ARP. The degradation and defluorination efficiencies of perfluorooctanoic acid (PFOA) depended on solution pH, and concentrations of surfactant (cetyltrimethylammonium bromide; CTAB), sulfite, and biochar. At high pH (8-10), adding biochar and BSS showed no or even small inhibitory effect on PFOA degradation, since the degradation efficiencies were already high enough that cannot be differentiated. However, at acidic and neutral pH (6-7), an evident enhancement of PFOA degradation and defluorination efficiencies occurred. This is due to the synergies between biochar and CTAB that create favorable microenvironments for enhanced PFOA sorption and deeper destruction by prolonging the longevity of reductive radicals (e.g., SO3•-), which is less affected by ambient pH conditions. The performance of UV/sulfite/BSS was further optimized and used for the degradation of four PFAS. At the optimal experimental condition, the UV/sulfite/BSS system can completely degrade PFOA with >30% defluorination efficiency for up to five continuous cycles (n = 5). Overall, our BSS provides a cost-effective and sustainable technique to effectively degrade PFAS in water under environmentally relevant pH conditions. The BSS-enabled ARP technique can be easily tied into PFAS treatment train technology (e.g., advanced oxidation process) for more efficient and deeper defluorination of various PFAS in water.

Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands

Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also evidence indicating that biochar promotes, rather than reduces, N2O emissions. Thus, the effect of biochar on N2O remains uncertain in constructed wetlands (CWs), and there is not a characterization metric for this effect, which increases the difficulty and inaccuracy of biochar-driven alleviation effect projections. Here, we provide new insight by utilizing machine learning-based, tree-structured Parzen Estimator (TPE) optimization assisted by a meta-analysis to estimate the potency of biochar-driven N2O mitigation. We first synthesized datasets that contained 80 studies on global biochar-amended CWs. The mitigation effect size was then calculated and further introduced as a new metric. TPE optimization was then applied to automatically tune the hyperparameters of the built extreme gradient boosting (XGBoost) and random forest (RF), and the optimum TPE-XGBoost obtained adequately achieved a satisfactory prediction accuracy for N2O flux (R2 = 91.90%, RPD = 3.57) and the effect size (R2 = 92.61%, RPD = 3.59). Results indicated that a high influent chemical oxygen demand/total nitrogen (COD/TN) ratio and the COD removal efficiency interpreted by the Shapley value significantly enhanced the effect size contribution. COD/TN ratio made the most and the second greatest positive contributions among 22 input variables to N2O flux and to the effect size that were up to 18% and 14%, respectively. By combining with a structural equation model analysis, NH4+-N removal rate had significant negative direct effects on the N2O flux. This study implied that the application of granulated biochar derived from C-rich feedstocks would maximize the net climate benefit of N2O mitigation driven by biochar for future biochar-based CWs.

Adsorption performance of Ni(II) by KOH-modified biochar derived from different microalgae species

Microalgae biochar is potential adsorbents to remove heavy metals from wastewater due to abundant functional groups, high porosity and wide sources, but performance is not fully developed since it depends on microalgae species attributing to distinct morphology and biomass compositions. Here, two microalgae species Chlorella Pyrenoidosa and Scenedesmus Obliquus were used for biochar preparation via KOH-modification, biochar properties and their influences on Ni(II) adsorption were investigated. Ni(II) adsorption performances responding to biochar properties and operating conditions were upgraded via progressive optimization and response surface methodology. Together, adsorption isotherms and kinetics were analyzed to obtain significant factors for Ni(II) removal. As results, 100 % of Ni(II) removal was achieved under 100 mg/L initial Ni(II) concentration as pH was higher than the biochar zero-charge point of 6.87 with low biochar dosage (0.5 g/L), which provides an efficient approach for heavy metal removal from wastewater with microalgae biochar.

Machine learning applications for biochar studies: A mini-review

Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.

A review of microbial responses to biochar addition in anaerobic digestion system: Community, cellular and genetic level findings

The biochar is a well-developed porous material with various excellent properties, that has been proven with excellent ability in anaerobic digestion (AD) efficiency promotion. Current research is usually focused on the macro effects of biochar on AD, while the systematic review about the mechanisms of biochar on microbial behavior are still lacking. This review summarizes the effects and potential mechanisms of biochar on microorganisms in AD systems, and found that biochar addition can provide habitats for microbial colonization, alleviate toxins stress, supply essential nutrients, and accelerate interspecies electron transferring. Moreover, it also improves microbial community diversity, facilitates EPS secretion, enhances functional enzyme activity, promotes functional genes expression, and inhibits the antibiotic resistance genes transformation. Future research directions including biochar targeted design, in-depth microbial mechanisms revelation, and modified model development were suggested, which could promote the widely practical application of of biochar-amended AD technology.

Preparation and application of metal-modified biochar in the purification of micro-polystyrene polluted aqueous environment

Microplastics (MPs) have already spread across the globe and have been found in drinking water and human tissues. This may pose severe threats to human health and water environment. Therefore, this study accurately evaluated the removal effect of metal-modified biochar on polystyrene microplastics (PS-MPs) (1.0 μm) in the water environment using a high-throughput fluorescence quantification method. The results indicated that Fe-modified biochar (FeBC) and Fe/Zn-modified biochar (Fe/ZnBC) had good removal efficiencies for PS-MPs under the dosage of 3 g/L, which were 96.24% and 84.77%, respectively. Although pore effects were observed (such as "stuck", "trapped"), the electrostatic interaction was considered the main mechanism for the adsorption of PS-MPs on metal-modified biochar, whereas the formation of metal-O-PS-MPs may also contribute to the adsorption process. The removal efficiency of PS-MPs by FeBC was significantly reduced under alkaline conditions (pH = 9 and 11) or in the presence of weak acid ions (PO43-, CO32-, HCO3-). A removal efficiency of 72.39% and 78.33% of PS-MPs was achieved from tap water (TW) and lake water (LW) using FeBC when the initial concentration was 20 mg/L. However, FeBC had no removal effect on PS-MPs in biogas slurry (BS) and brewing wastewater (BW) due to the direct competitive adsorption of high concentrations of chemical oxygen demand (COD). The findings of this study highlighted that metal-modified biochar had a potential application in purifying tap water or lake water which contaminated by MPs.

Regulation of tourmaline-mediated Fenton-like system by biochar: Free radical pathway to non-free radical pathway

The heterogeneous Fenton-like systems induced by Fe-containing minerals have been largely applied for the degradation of organic pollutants. However, few studies have been conducted on biochar (BC) as an additive to Fenton-like systems mediated by iron-containing minerals. In this study, the addition of BC prepared at different temperatures was found to significantly enhance the degradation of contaminants in the tourmaline-mediated Fenton-like system (TM/H2O2) using Rhodamine B (RhB) as the target contaminant. Furthermore, the hydrochloric acid-modified BC prepared at 700 °C (BC700(HCl)) could achieve complete degradation of high concentrations of RhB in the BC700(HCl)/TM/H2O2 system. Free radical quenching experiments showed that TM/H2O2 system removed contaminants mainly mediated by the free radical pathway. After adding BC, the removal of contaminants is mainly mediated by the non-free radical pathway in BC700(HCl)/TM/H2O2 system which was confirmed by the Electron paramagnetic resonance (EPR) experiments and electrochemical impedance spectroscopy (EIS). In addition, BC700(HCl) had broad feasibility in the degradation of other organic pollutants (Methylene Blue (MB) 100%, Methyl Orange (MO) 100%, and tetracycline (TC) 91.47%) in the tourmaline-mediated Fenton-like system. Possible pathways for the degradation of RhB by the BC700(HCl)/TM/H2O2 system were also proposed.

Recent advancements and challenges in emerging applications of biochar-based catalysts

The sustainable utilization of biochar produced from biomass waste could substantially promote the development of carbon neutrality and a circular economy. Due to their cost-effectiveness, multiple functionalities, tailorable porous structure, and thermal stability, biochar-based catalysts play a vital role in sustainable biorefineries and environmental protection, contributing to a positive, planet-level impact. This review provides an overview of emerging synthesis routes for multifunctional biochar-based catalysts. It discusses recent advances in biorefinery and pollutant degradation in air, soil, and water, providing deeper and more comprehensive information of the catalysts, such as physicochemical properties and surface chemistry. The catalytic performance and deactivation mechanisms under different catalytic systems were critically reviewed, providing new insights into developing efficient and practical biochar-based catalysts for large-scale use in various applications. Machine learning (ML)-based predictions and inverse design have addressed the innovation of biochar-based catalysts with high-performance applications, as ML efficiently predicts the properties and performance of biochar, interprets the underlying mechanisms and complicated relationships, and guides biochar synthesis. Finally, environmental benefit and economic feasibility assessments are proposed for science-based guidelines for industries and policymakers. With concerted effort, upgrading biomass waste into high-performance catalysts for biorefinery and environmental protection could reduce environmental pollution, increase energy safety, and achieve sustainable biomass management, all of which are beneficial for attaining several of the United Nations Sustainable Development Goals (UN SDGs) and Environmental, Social and Governance (ESG).

Effects of pyrolysis conditions and heteroatom modification on the polycyclic aromatic hydrocarbons profile of biochar prepared from sorghum distillery residues

The formation of 2- to 6-ring polycyclic aromatic hydrocarbons (PAHs) in sorghum distillery residue-derived biochar (SDRBC) was evaluated under different thermochemical pyrolysis conditions of carbonization atmosphere (N2 or CO2), temperature (300-900 °C) and doping with nonmetallic elements, i.e., N, B, O, P, N + B, and N + S. The results indicated that without surface modification, PAHs formation was 944 ± 74 ng g-1, the highest level, and 181 ± 16 ng g-1, the lowest level, at 300 °C in N2 and CO2 atmosphere, respectively. Boron doping of SDRBC significantly reduced the PAHs content (by 97%) under N2 at 300 °C. Results demonstrated that boron modified SDRBC exhibited the highest degree of PAH reduction. Combined pyrolysis temperature and atmosphere in addition to heteroatom doping is a robust and viable strategy for efficient suppression of PAHs formation and high-value utilization of pyrolysis products of low carbon footprint.

Molecular probing of dissolved organic matter and its transformation in a woolen textile wastewater treatment station

Woolen textile industry produces enormous wastewater (WTIW) with high pollution loads, and needs to be treated by wastewater treatment stations (WWTS) before centralized treatment. However, WTIW effluent still contains many biorefractory and toxic substances; thus, comprehensive understandings of dissolved organic matter (DOM) of WTIW and its transformation are essential. In this study, total quantity indices, size exclusion chromatography, spectral methods, and Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) were used for comprehensively characterizing DOM and its transformation during full-scale treatments, including influent, regulation pool (RP), flotation pool (FP), up-flow anaerobic sludge bed (UA), anaerobic/oxic (AO) and effluent. DOM in influent featured a large molecular weight (5-17 kDa), toxicity (0.201 HgCl₂ mg/L), and a protein content of 338 mg C/L. FP largely removed 5-17 kDa DOM with the formation of 0.45-5 kDa DOM. UA and AO removed 698 and 2042 chemicals, respectively, which were primarily saturated components (H/C > 1.5); however, both UA and AO contributed to the formation of 741 and 1378 stable chemicals, respectively. Good correlations were found among water quality indices and spectral/molecular indices. Our study reveals the molecular composition and transformation of WTIW DOM during treatments and encourages the optimization of the employed processes in WWTS.

Adsorbed copper on urea modified activated biochar catalyzed H2O2 for oxidative degradation of sulfadiazine：Degradation mechanism and toxicity assessment

The combined pollution of heavy metals and organic compounds usually occurs simultaneously and induces high toxicity. The technology of simultaneous removal of combined pollution is lacking and the removal mechanism is not clear. Sulfadiazine (SD), a widely used antibiotic, was used as a model contaminant. Urea modified sludge-based biochar (USBC) was prepared and used to catalyze H₂O₂ to remove the combined pollution of Cu²⁺ and sulfadiazine (SD) without causing secondary pollution. After 2 h, the removal rates of SD and Cu²⁺ were 100 and 64.8%, respectively. Cu²⁺ adsorbed on the surface of USBC accelerated the activation of H₂O₂ by the USBC catalyzed by CO bond to produce hydroxyl radical (^•OH) and single oxygen (¹O₂) to degrade SD. Twenty-three intermediate products were detected, most of which were completely decomposed into CO₂ and H₂O. The toxicity was significantly reduced in the combined polluted system. This study highlights the potential of the low-cost technology based on sludge reuse and its inherent significance in reducing the toxic risk of combined pollution in the environment.