pH-dependent mechanisms of sulfadiazine degradation by natural pyrite-driven heterogeneous Fenton-like reactions.

The development of a natural pyrite/peroxymonosulfate (PMS) system for the removal of antibiotic contamination from water represented an economic and green sustainable strategy. Yet, a noteworthy knowledge gap remained considering the underlying reaction mechanism of the system, particularly in relation to its pH sensitivity. Herein, this paper investigated the impacts of critical reaction parameters and initial pH levels on the degradation of sulfadiazine (SDZ, 3 mg/L) in the pyrite/PMS system, and elucidated the pH dependence of the reaction mechanism. Results showed that under optimal conditions, SDZ could be completely degraded within 5 min at a broad pH range of 3.0-9.0, with a pseudo-first-order reaction rate of >1.0 min-1. The low or high PMS doses could lower degradation rates of SDZ through the decreased levels of active species, while the amount of pyrite was positively correlated with the removal rate of SDZ. The diminutive concentrations of anions exerted minor impacts on the decomposition of SDZ within the pyrite PMS system. Mechanistic results demonstrated that the augmentation of pH levels facilitated the transition from the non-radical to the radical pathway within the natural pyrite/PMS system, while concurrently amplifying the role of •OH in the degradation process of SDZ. This could be attributed to the change in interface electrostatic repulsion induced by pH fluctuations, as well as the mutual transformation between active species. The stable presence of the relative content of Fe(II) in the used pyrite was ensured owing to the reduced sulfur species acting as electron donors, providing the pyrite/PMS system excellent reusability. This paper sheds light on the mechanism regulation of efficient removal of organic pollutants through pyrite PMS systems, contributing to practical application.

Deep learning-based radiomic nomograms for predicting Ki67 expression in prostate cancer.

BACKGROUND: To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa). MATERIALS: The data of 229 patients with PCa from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. Deep learning features were extracted and selected from each patient's prostate multiparametric MRI (diffusion-weighted imaging, T2-weighted imaging, and contrast-enhanced T1-weighted imaging sequences) data to establish a deep radiomic signature and construct models for the preoperative prediction of Ki67 expression. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a joint model. The predictive performance of multiple deep-learning models was then evaluated. RESULTS: Seven prediction models were constructed: one clinical model, three deep learning models (the DLRS-Resnet, DLRS-Inception, and DLRS-Densenet models), and three joint models (the Nomogram-Resnet, Nomogram-Inception, and Nomogram-Densenet models). The areas under the curve (AUCs) of the clinical model in the testing, internal validation, and external validation sets were 0.794, 0.711, and 0.75, respectively. The AUCs of the deep models and joint models ranged from 0.939 to 0.993. The DeLong test revealed that the predictive performance of the deep learning models and the joint models was superior to that of the clinical model (p < 0.01). The predictive performance of the DLRS-Resnet model was inferior to that of the Nomogram-Resnet model (p < 0.01), whereas the predictive performance of the remaining deep learning models and joint models did not differ significantly. CONCLUSION: The multiple easy-to-use deep learning-based models for predicting Ki67 expression in PCa developed in this study can help physicians obtain more detailed prognostic data before a patient undergoes surgery.

A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study.

BACKGROUND: To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer. METHODS: Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms. RESULTS: Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians. CONCLUSION: The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.

Factors affecting the durability of dimethyl dithiocarbamate-stabilized air pollution control (APC) residues derived from municipal solid waste incineration.

Sodium dimethyl dithiocarbamate (SDD) is widely used for stabilizing heavy metals to minimize pollution from air pollution control (APC) residues derived from municipal solid waste incineration. However, the effect of environmental conditions on heavy metal leaching from SDD-stabilized APC residues remains unknown. Therefore, this study aimed to evaluate the durability of SDD-stabilized APC residues and determine the relationship between heavy metal leaching and environmental factors, including pH, temperature, and oxygen. The results revealed that accelerated SDD decomposition and the decline in durability of SDD-stabilized APC residues were caused by acidic and aerated conditions and temperatures above 40 °C. A decrease in pH from 12.25 to 4.69 increased the Cd and Pb concentrations in SDD-stabilized APC residue leachate from below detection (0.002 mg/L) to 1.32 mg/L and 0.04 mg/L to 3.79 mg/L, respectively. Heating at 100 °C for 2 d increased the Cd and Pb concentrations from below detection (0.002 mg/L and 0.01 mg/L) to 2.96 mg/L and 0.47 mg/L, respectively. Aeration for 5 d increased the Cd and Pb concentrations from below detection to 0.09 mg/L and 0.49 mg/L, respectively. The decline in durability was attributed to acid hydrolysis, thermal decomposition, and oxidative damage of SDD, resulting in breakage of the chelated sulfur-metal bond, which was confirmed by the decrease in the oxidizable fraction of heavy metals and the SDD content. This study improves the understanding of the factors contributing to the decline in durability of heavy metals in SDD-stabilized APC residues, which is important for ensuring the long-term stabilization and environmental safety of these residues.

Uncovering Dynamic Edge-Sites in Atomic Co-N-C Electrocatalyst for Selective Hydrogen Peroxide Production.

Understanding the nature of single-atom catalytic sites and identifying their spectroscopic fingerprints are essential prerequisites for the rational design of target catalysts. Here, we apply correlated in situ X-ray absorption and infrared spectroscopy to probe the edge-site-specific chemistry of Co-N-C electrocatalyst during the oxygen reduction reaction (ORR) operation. The unique edge-hosted architecture affords single-atom Co site remarkable structural flexibility with adapted dynamic oxo adsorption and valence state shuttling between Co(2-δ)+ and Co2+ , in contrast to the rigid in-plane embedded Co1 -Nx counterpart. Theoretical calculations demonstrate that the synergistic interplay of in situ reconstructed Co1 -N2 -oxo with peripheral oxygen groups gives a rise to the near-optimal adsorption of *OOH intermediate and substantially increases the activation barrier for its dissociation, accounting for a robust acidic ORR activity and 2e- selectivity for H2 O2 production.

Value of Apparent Diffusion Coefficient for Assessing Preoperative T Staging of Low Rectal Cancer and Whether This Is Correlated With Ki-67 Expression.

PURPOSE: To explore the value of the apparent diffusion coefficient (ADC) in assessing preoperative T staging of low rectal cancer and the correlation between ADC value and Ki-67 expression. METHODS: Data on 77 patients with a proven pathology of low rectal cancer were retrospectively analyzed. All patients underwent a magnetic resonance imaging scan 1 week prior to operation, and the mean ADC value was measured. All tumors were fully removed, and pathologic staging was determined. The Ki-67 expression was determined using immunohistochemical methods in all patients. The correlation between Ki-67 expression and ADC features was studied. RESULTS: A total of 77 patients with low rectal cancer were included in the study. The pathology type was adenocarcinoma. The numbers of patients with pathological stages T1, T2, T3, and T4 were 9, 23, 32, and 13, respectively. The ADC value of all tumors ranged from 0.60 to 1.20 mm2/s. The average Ki-67 proliferation index was 55.3% ± 20.2%. A significant difference was observed between the preoperative ADC value and pathological T staging of low rectal cancer (P < .01). The more advanced the T stage, the lower the detected ADC values were. A negative correlation was noted between the preoperative ADC value and Ki-67 proliferation index of rectal cancer (r = -0.71, P < .01). When the Ki-67 proliferation index increased, lower ADC values were detected. CONCLUSION: The ADC values can provide useful information on preoperative tumor staging and may facilitate evaluation of the biological behavior of low rectal cancer. The ADC values should be considered a sensitive image biomarker of rectal cancer.

Multi-agent cooperative target search.

This paper addresses a vision-based cooperative search for multiple mobile ground targets by a group of unmanned aerial vehicles (UAVs) with limited sensing and communication capabilities. The airborne camera on each UAV has a limited field of view and its target discriminability varies as a function of altitude. First, by dividing the whole surveillance region into cells, a probability map can be formed for each UAV indicating the probability of target existence within each cell. Then, we propose a distributed probability map updating model which includes the fusion of measurement information, information sharing among neighboring agents, information decay and transmission due to environmental changes such as the target movement. Furthermore, we formulate the target search problem as a multi-agent cooperative coverage control problem by optimizing the collective coverage area and the detection performance. The proposed map updating model and the cooperative control scheme are distributed, i.e., assuming that each agent only communicates with its neighbors within its communication range. Finally, the effectiveness of the proposed algorithms is illustrated by simulation.

Leachate from municipal solid waste landfills: A neglected source of microplastics in the environment.

Over the past decade or so, microplastics (MPs) have received increasing attention due to their ubiquity and potential risk to the environment. Waste plastics usually end up in landfills. These plastics in landfills undergo physical compression, chemical oxidation, and biological decomposition, breaking down into MPs. As a result, landfill leachate stores large amounts of MPs, which can negatively impact the surrounding soil and water environment. However, not enough attention has been given to the occurrence and removal of MPs in landfill leachate. This lack of knowledge has led to landfills being an underestimated source of microplastics. In order to fill this knowledge gap, this paper collects relevant literature on MPs in landfill leachate from domestic and international sources, systematically summarizes their presence within Asia and Europe, assesses the impacts of landfill leachate on MPs in the adjacent environment, and particularly discusses the possible ecotoxicological effects of MPs in leachate. We found high levels of MPs in the soil and water around informal landfills, and the MPs themselves and the toxic substances they carry can have toxic effects on organisms. In addition, this paper summarizes the potential impact of MPs on the biochemical treatment stage of leachate, finds that the effects of MPs on the biochemical treatment stage and membrane filtration are more significant, and proposes some novel processes for MPs removal from leachate. This analysis contributes to the removal of MPs from leachate. This study is the first comprehensive review of the occurrence, environmental impact, and removal of MPs in leachate from landfills in Asia and Europe. It offers a comprehensive theoretical reference for the field, providing invaluable insights.

Drivers of change behind the spatial distribution and fate of typical trace organic pollutants in fresh waste leachate across China.

There have been growing concerns regarding the health and environmental impacts of trace organic pollutants (TOPs). However, fresh leachate from municipal solid waste (MSW) has been overlooked as a potential reservoir of TOPs. Therefore, we investigated 90 legacy and emerging TOPs in fresh leachate from 14 provinces and municipalities in China. Additionally, the fate and final discharge impacts of TOPs in 14 leachate treatment systems were analyzed. The results revealed that the detection rate of 90 TOPs was over 50 % in all samples. Notably, polychlorinated biphenyls, banned for 40 years, were frequently detected in fresh leachate. The concentration of pseudo-persistent TOPs (105-107 ng/L) is significantly higher than that of persistent TOPs (102-104 ng/L). Spatial distribution patterns of TOPs in fresh leachate suggest that economy, population, climate, and policies impact TOPs discharge from MSW. For example, economically developed and densely populated areas displayed higher TOPs concentrations, whereas warmer climates facilitate TOPs leaching from MSW. We confirmed that waste classification policies were a key driver of the decline in multiple TOPs in leachate. Mass balance analysis shows that the final effluent and sludge from current dominant leachate treatment systems contain refractory TOPs, especially perfluoroalkyl acids, which must be prioritized for control. This paper was the first comprehensive investigation of multiple TOPs in fresh leachate at a large geographic scale. The factors affecting the occurrence, spatial distribution, and fate of TOPs in fresh leachate were revealed. It provides a valuable reference for the establishment of policies for the management of TOPs in MSW and the associated leachate.

Overcoming metals redox rate limitations in spinel oxide-driven Fenton-like reactions via synergistic heteroatom doping and carbon anchoring for efficient micropollutant removal.

The transition metals redox rate limitations of spinel oxides during Fenton-like reactions hinder its efficient and sustainable treatment of actual wastewater. Herein, we propose to optimize the electronic structure of Co-Mn spinel oxide (CM) via sulfur doping and carbon matrix anchoring synergistically, enhancing the radicals-nonradicals Fenton-like processes for efficient water decontamination. Activating peroxymonosulfate (PMS) with optimised spinel oxide (CMSAC) achieved near-complete removal of ofloxacin (10 mg/L) within 6 min, showing 8.4 times higher efficiency than CM group. Significantly higher yields of SO4·- and high-valent metal species in CMSAC/PMS system provided exceptional resistance to co-existing anions, enabling efficient removal of various emerging contaminants in high salinity leachate. Specifically, sulfur coordination and carbon anchoring-induced oxygen vacancy synergistically improved the electronic structure and electron transfer efficiency of CMSAC, thus forming highly reactive Co sites and significantly reducing the energy barrier for Co(IV)=O generation. The reductive sulfur species facilitated the conversion of Co(III) to Co(II), thereby maintaining the stability of the catalytic activity of CMSAC. This work developed a synergistic optimization strategy to overcome the metals redox rate limitations of spinel oxides in Fenton-like reactions, providing deep mechanistic insights for designing Fenton-like catalysts suitable for practical applications.

Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer.

PURPOSE: To explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer. METHODS: The data of 491 patients with rectal cancer from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. They were categorized into high- and low-expression group based on postoperative pathological Ki-67 expression. Each patient's mp-MRI data were analyzed to extract and select the most relevant features of deep learning, and a deep learning model was constructed. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a nomogram for the prediction of Ki-67 expression. The performance characteristics of the DL-model, clinical model, and nomogram were assessed using ROCs, calibration curve, decision curve, and clinical impact curve analysis. RESULTS: The strongest deep learning features were extracted and screened from mp-MRI data. Two independent predictive factors, namely Magnetic Resonance Imaging T (mrT) staging and differentiation degree, were identified through clinical feature selection. Three models were constructed: a deep learning (DL)-model, a clinical model, and a nomogram. The AUCs of clinical model in the training, internal validation, and external validation set were 0.69, 0.78, and 0.67, respectively. The AUCs of the deep model and nomogram ranged from 0.88 to 0.98. The prediction performance of the deep learning model and nomogram was significantly better than the clinical model (P < 0.001). CONCLUSION: The nomogram based on deep learning can help clinicians accurately and conveniently predict the expression status of Ki-67 in rectal cancer.

Biodegradable and multifunctional black mulch film decorated with darkened lignin induced by iron ions for "green" agriculture.

High-value utilization of bleached lignin has been widely used in different fields, whereas the investigation on darkened lignin in composite materials was often ignored. In this work, a sort of eco-friendly and structurally robust sodium carboxymethyl cellulose (CMC)/polyvinyl alcohol (PVA)/sodium lignosulfonate (SLS) black composite mulch film was elaborately designed. The chelation and redox reaction effect between Fe ions and SLS lead to the formation of a more quinones structure on lignin, darkening both lignin and the mulch films. The chelation effect between Fe ions and biopolymer formed three-dimensional structures, which can be used as sacrifice bonds to dissipate energy and improve the mechanical properties of the composite films. In particular, the maximum elongation at break and toughness increased from 48.4 % and 1141 kJ/m3 for the CMC/PVA film to 210.9 % and 1426 kJ/m3 for the optimized CMC/PVA/SLS/Fe black mulch film, respectively. In addition, the optimized black mulch film also possesses good soil water retention, thermal preservation effect, controlled urea release, and well biodegradability. This work offered a novel strategy for designing eco-friendly black mulch with reinforced mechanical strength, slow-release urea, soil moisture retention, and heat preservation performances.

MRI-based radiomics for preoperative prediction of recurrence and metastasis in rectal cancer.

OBJECTIVES: To explore the value of multi-parametric MRI (mp-MRI) radiomic model for preoperative prediction of recurrence and/or metastasis (RM) as well as survival benefits in patients with rectal cancer. METHODS: A retrospective analysis of 234 patients from two centers with histologically confirmed rectal adenocarcinoma was conducted. All patients were divided into three groups: training, internal validation (in-vad) and external validation (ex-vad) sets. In the training set, radiomic features were extracted from T2WI, DWI, and contrast enhancement T1WI (CE-T1) sequence. Radiomic signature (RS) score was then calculated for feature screening to construct a rad-score model. Subsequently, preoperative clinical features with statistical significance were selected to construct a clinical model. Independent predictors from clinical and RS related to RM were selected to build the combined model and nomogram. RESULTS: After feature extraction, 26 features were selected to construct the rad-score model. RS (OR = 0.007, p < 0.01), MR-detected T stage (mrT) (OR = 2.92, p = 0.03) and MR-detected circumferential resection margin (mrCRM) (OR = 4.70, p = 0.01) were identified as independent predictors of RM. Then, clinical model and combined model were constructed. ROC curve showed that the AUC, accuracy, sensitivity and specificity of the combined model were higher than that of the other two models in three sets. Kaplan-Meier curves showed that poorer disease-free survival (DFS) time was observed for patients in pT3-4 stages with low RS score (p < 0.001), similar results were also found in pCRM-positive patients (p < 0.05). CONCLUSION: The mp-MRI radiomics model can be served as a noninvasive and accurate predictors of RM in rectal cancer that may support clinical decision-making.

Combined pretreatment of malic acid and kraft pulping for the production of fermentable sugars and highly active lignin.

The separation and utilization of cellulose, hemicellulose, and lignin in lignocellulosic biorefineries present significant challenges. This study proposes a pretreatment method for biomass refining by combining acid and kraft pulping. Firstly, the biomass was pretreated by malic acid, resulting in the isolation of xylo-oligosaccharides (XOS) with a yield of 86.26 % with optimized conditions of 180 °C, 1 wt% concentration, 40 min. Secondly, a mixture of 12.98 wt% NaOH and 1.043 wt% Na2S is employed to achieve lignin removal efficiency up to 63.42 %. Physical refinement techniques are then applied to enhance the enzyme digestion efficiency of cellulose, resulting in an increase from 55.03 % to 91.4 % for efficient cellulose conversion. The reacted samples exhibit a lignin composition rich in ß-O-4 ether bonds, facilitating their high-value utilization. The results indicated that the combined pretreatment approach demonstrates high efficiency in separating cellulose, hemicellulose, and lignin while obtaining XOS, highly active lignin, and enzyme-digested substrates.

Optimizing adsorption performance of sludge-derived biochar via inherent moisture-regulated physicochemical properties.

Understanding the impact of abundant inherent moisture in sewage sludge on the physicochemical properties and adsorption applications of sludge-derived biochar (SDB) contributed significantly to promoting economical sludge reuse. The moisture (0-80%) contributed to the development of micropore and mesopore in SDB at 400 °C, resulting in a maximum increase in specific surface area (SSA) and total pore volume (TPV) of SDB by 38.47% (84.811-117.437 m2/g) and 92.60% (0.0905-0.1743 m3/g), respectively. At 600/800 °C, moisture only facilitated mesopore formation, while was exacerbated with increasing moisture content. Despite reduction in SSA during this stage, TPV increased by a maximum of 20.47% (0.1700-0.2048 m3/g). The presence of moisture during pyrolysis led to an increase in the formation of 3-5 thickened benzene rings and defective structures in SDB, along with more C=O, O-C=O/-OH, pyrrole N, pyridine N, and thiophene. As a result, moisture (40%/80%) increased the maximum adsorption capacity (76.2694-88.0448/90.1190 mg/g) of SDB (600 °C) for tetracycline, mainly due to enhanced pore filling effect and hydrogen bonding induced by improved physicochemical properties. This study offered a novel approach for optimizing the performance of SDB adsorption applications by manipulating the sludge moisture, which is critical for practical sludge management.

Target-prepared sludge biochar-derived synergistic Mn and N/O induces high-performance periodate activation for reactive iodine radicals generation towards ofloxacin degradation.

Converting waste activated sludge into catalysts for the removal of antibiotics in water fulfils the dual purpose of waste-to-resource and hazardous pollution elimination. In this study, sludge-derived biochar (SDB) for efficient periodate (PI) activation was first prepared via one-step pyrolysis of potassium permanganate-polyhexamethylenebiguanide conditioned sludge without additional modification. The SDB (750 °C)-PI system degraded 100% ofloxacin (OFL, 41.5 µM) within 6 min and was almost undisturbed by inorganic ions or humic acids. The experimental results confirmed that the predominant role of reactive iodine species (RIS) and the auxiliary involvement of singlet oxygen (1O2) jointly contributed to the OFL degradation. Theoretical calculations further indicated that the synergy between Mn and N/O induced local charge redistribution and improved electron transfer capability of SDB, leading to the formation of electron-rich Mn sites and enhanced Mn(II)↔Mn(III)↔Mn(IV) redox to promote PI activation. More importantly, the enhanced adsorption and charge transfer of PI on the Mn site of the Mn-N/O-C structures induced the I-O bond stretching and the rapid generation of RIS. This study offered a cost-effective strategy for developing SDB-based catalysts, further advancing the comprehension of sludge management and the intricate mechanisms underlying RIS formation in PI-advanced oxidation processes.

Unveiling the occurrence, distribution, removal, and environmental impacts of 65 emerging contaminants in neglected fresh leachate from municipal solid waste incineration plants.

Emerging contaminants (ECs) are commonly found in environmental media. Yet leachate from municipal solid waste incineration plants (MSWIPs), which can serve as a reservoir for various contaminants, including ECs, has received little investigation. To address this gap, 65 ECs were analyzed in the fresh leachate and biological effluent from three major MSWIPs in Shanghai. Results indicated that over half (56%) of the 65 ECs were detected in fresh leachate. Different ECs would be removed to varying degrees after biological treatment, including polycyclic aromatic hydrocarbons (PAHs) (65%), polybrominated diphenyl ethers (PBDEs) (51%), phthalate esters (PAEs) (36%), and organophosphorus pesticides (OPPs) (34%). Notably, for tetrabromobisphenol A (TBBPA), a PBDE substitute, only 2% was removed after biological treatment, while polychlorinated biphenyls (PCBs) were effectively removed at 83%. Water solubility and the octanol-water partition coefficient are key factors influencing the distribution and removal of ECs in leachate. the effluent will still contain refractory ECs even after the biological treatment. These residual ECs discharged to sewers can impact wastewater treatment plants or contaminate surface water and groundwater. These findings provide insights into the leachate contamination by ECs, their environmental fate, factors affecting their behavior, and potential environmental impacts.

A review of pristine and modified biochar immobilizing typical heavy metals in soil: Applications and challenges.

In recent years, the application of biochar in the remediation of heavy metals (HMs) contaminated soil has received tremendous attention globally. We reviewed the latest research on the immobilization of soil HMs by biochar almost in the last 5 years (until 2021). The methods, effects and mechanisms of biochar and modified biochar on the immobilization of typical HMs in soil have been systematically summarized. In general, the HMs contaminating the soil can be categorized into two groups, the oxy-anionic HMs (As and Cr) and the cationic HMs (Pb, Cd, etc.). Reduction and precipitation of oxy-anionic HMs by biochar/modified biochar are the dominant mechanism for reducing HMs toxicity. Pristine biochar can effectively immobilize cationic HMs. The commonly applied modification method is to add substances that can precipitate HMs to the biochar. In addition, we assessed the risks of biochar applications. For instance, biochar may cause the leaching of certain HMs; biochar aging; co-transportation of biochar nanoparticles with HMs. Future work should focus on the artificial/intelligent design of biochar to make it suitable for remediation of multiple HMs contaminated soil.

A sustainable reuse strategy of converting waste activated sludge into biochar for contaminants removal from water: Modifications, applications and perspectives.

Conversion of sewage sludge to biochar for contaminants removal from water achieves the dual purpose of solid waste reuse and pollution elimination, in line with the concept of circular economy and carbon neutrality. However, the current understanding of sludge-derived biochar (SDB) for wastewater treatment is still limited, with a lack of summary regarding the effect of modification on the mechanism of SDB adsorption/catalytic removal aqueous contaminants. To advance knowledge in this aspect, this paper systematically reviews the recent studies on the use of (modified) SDB as adsorbents and in persulfate-based advanced oxidation processes (PS-AOPs) as catalysts for the contaminants removal from water over the past five years. Unmodified SDB not only exhibits stronger cation exchange and surface precipitation for heavy metals due to its nitrogen/mineral-rich properties, but also can provide abundant catalytic active sites for PS. An emphatic summary of how certain adsorption removal mechanisms of SDB or its catalytic performance in PS-AOPs can be enhanced by targeted regulation/modification such as increasing the specific surface area, functional groups, graphitization degree, N-doping or transition metal loading is presented. The interference of inorganic ions/natural organic matter is one of the unavoidable challenges that SDB is used for adsorption/catalytic removal of contaminants in real wastewater. Finally, this paper presents the future perspectives of SDB in the field of wastewater treatment. This review can contribute forefront knowledge and new ideas for advancing sludge treatment toward sustainable green circular economy.

Performance and mechanism of bamboo residues pyrolysis: Gas emissions, by-products, and reaction kinetics.

The performances and reaction kinetics of the bamboo shoot leaves (BSL) pyrolysis were characterized integrating thermogravimetry, Fourier transform infrared spectroscopy, and pyrolysis-gas chromatography/mass spectrometry analyses. The high volatiles and low ash, N, and S contents of BSL rendered its pyrolysis suitable for bio-oil generation. The main mass loss of BSL pyrolysis occurred in the devolatilization stage between 200 and 550 °C. The peak temperatures of pseudo-hemicellulose, cellulose and lignin pyrolysis in BSL were 248.04, 322.65 and 383.51 °C, respectively, while their average activation energies estimated by Starink method were 144.29, 175.79 and 243.02 kJ/mol, respectively. The one-dimensional diffusion mechanism (f (α) = 1/(2α)) best elucidated the hemicellulose reaction. The cellulose (f (α) = 0.74 (1 - α)[-ln (1 - α)]-13/37) and lignin (f (α) = 0.35 (1 - α)[-ln (1 - α)]-13/7) reactions were best described by the nucleation mechanisms. The estimated kinetic triplets accurately predicted the pyrolysis process. 619.3 °C and 5 °C/min were determined as the optimal pyrolytic temperature and heating rate. The C-containing gases were dominant among the non-condensable gases evolved from the pyrolysis. The NOx precursors (NH3 and HCN) were found more important than NO emission in pollution control. 2,3-dihydrobenzofuran, (1-methylcyclopropyl) methanol, heptanal, acetic acid, and furfurals were the main pyrolytic by-products. BSL-derived biochar is a relatively pure carbon-rich material with extremely low N and S content. The BSL pyrolysis yielded a promising performance, as well as value-added by-products to be utilized in the fields of bioenergy, fragrance, and pharmaceuticals.