Journal of water process engineering最新文献

{"title":"Ultrasound-assisted nanofiltration for remazol brilliant blue reactive removal: elucidating the role of ph in efficacy and by-product formation","authors":"Ismail Abiodun Eweje ,&nbsp;Sam Yav Munongo ,&nbsp;Rana Kıdak ,&nbsp;Ime Akanyeti","doi":"10.1016/j.jwpe.2025.108754","DOIUrl":"10.1016/j.jwpe.2025.108754","url":null,"abstract":"<div><div>The effective treatment of dye-laden industrial effluents remains a critical challenge in environmental engineering. This study introduces a novel hybrid system integrating ultrasound (US) and nanofiltration (NF) technologies for the removal of Remazol Brilliant Blue Reactive (RBBR) dye from aqueous solutions. RBBR, a widely used textile dye, poses significant environmental and health risks due to its persistence and toxicity. For the first time, the influence of pH on the performance of a hybrid US-NF system for RBBR removal, including by-product formation and membrane fouling was systematically investigated. Initially, the US and NF processes were optimized independently by systematically varying key parameters such as US frequency, exposure time, membrane pressure, and solution pH. Ultrasonic treatment at 575 kHz resulted in 79 % reduction in absorbance and 58 % total organic carbon (TOC) removal at pH 3 after 90 min, indicating partial oxidation of the dye. NF alone consistently removed over 99 % of the dye across all tested pH values, though a flux decline of 16 % was observed at pH 3 due to fouling. The hybrid US-NF system maintained over 99 % dye rejection while completely eliminating flux decline at an optimal pH of 3, significantly outperforming the standalone NF process. Degradation by-products were confirmed via mass spectrometry and Fourier-transform infrared spectroscopy, highlighting the complementary role of NF in removing potential intermediates. These results demonstrate the potential of US–NF coupling as a robust and sustainable approach for advanced treatment of dye-contaminated wastewater, contributing to the development of cleaner industrial processes and improved water reuse practices.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108754"},"PeriodicalIF":6.7,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid machine learning model for disinfectant dosing in small-scale water treatment under data scarcity","authors":"Diego Takashi Sato ,&nbsp;Orlando M. Oliveira Belo ,&nbsp;Antonio P. Castro Junior ,&nbsp;Viviane M. Gomes Pacheco ,&nbsp;Cloves Gonçalves Rodrigues ,&nbsp;Antonio Paulo Coimbra ,&nbsp;Wesley Pacheco Calixto","doi":"10.1016/j.jwpe.2025.108736","DOIUrl":"10.1016/j.jwpe.2025.108736","url":null,"abstract":"<div><div>Disinfection by-products, including trihalomethanes and haloacetic acids, pose persistent risks to human health and aquatic ecosystems, particularly in small-scale water treatment plants characterized by limited automation and incomplete monitoring records. This study proposes a hybrid model that integrates extreme gradient enhancement with seasonal trend decomposition, allowing incomplete time series to be partitioned into trend and seasonal components, thereby improving prediction stability and improving interpretability of variable influence. The main contribution is a method that explicitly addresses seasonal variability and data scarcity while preserving predictive accuracy under infrastructure constraints, achieving <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>≥</mo><mn>0</mn><mo>.</mo><mn>90</mn></mrow></math></span> and RMSE values between 0.15 and 0.30. The model was validated in a real decentralized system, where it exhibited high performance even with data missing up to 30%, producing monthly reductions of approximately 450 g of trihalomethanes and 800 g of haloacetic acids, along with lower chlorine and fluoride consumption. By integrating technical, environmental, and economic dimensions, including measurable financial returns with a positive annual ROI and a short payback period, the approach provides a replicable solution for dosing control in data-limited contexts, aligned with the Sustainable Development Goal 6 of the United Nations and the advancement of responsible digital strategies in the water sector.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108736"},"PeriodicalIF":6.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-power visible LED driven persulfate-assisted degradation of phenolics using narrow-bandgap lanthanum perovskites","authors":"Tau S. Ntelane ,&nbsp;Usisipho Feleni ,&nbsp;Nomcebo H. Mthombeni ,&nbsp;Rudzani Sigwadi ,&nbsp;Alex T. Kuvarega","doi":"10.1016/j.jwpe.2025.108773","DOIUrl":"10.1016/j.jwpe.2025.108773","url":null,"abstract":"<div><div>Persulfate (PS, S₂O₈²⁻) based heterogeneous photocatalytic activation has sparked great scientific interest because of its effectiveness and environmental friendliness. Nevertheless, it remains a challenge to find low-energy consuming, environmentally acceptable light sources for PS activation that balance energy consumption and photocatalytic efficiency while providing more design flexibility for different kinds of photocatalytic reactors. Herein, a series of lanthanum perovskites (LaMO₃; M = Ni, Co, Cu, Fe) were synthesized, characterized, and evaluated for efficient degradation of phenolic compounds via PS activation using low power-consuming visible light-emitting diodes (LEDs) as a source of light. The effects of key influencing reaction parameters on degradation efficiency were thoroughly evaluated. In comparison to other perovskite catalysts, LaNiO₃ exhibited 98.4 % bisphenol A degradation efficiency with 69.3 % TOC removal within 50 min, owing to accelerated Ni²⁺/Ni³⁺ redox cycling, decreased rate of e⁻/h⁺ recombination, effective charge separation, and electron mobility. Additionally, over 91.0 % degradation efficiencies were maintained over a broad pH range (3.0–11.0), and for other phenolic compounds (phenol (95.8 %), 4-chlorophenol (85.7 %), and 4-nonylphenol (100 %)). Notably, the LaNiO₃/PS/Vis-LED system displayed excellent stability and reusability after five reuse cycles. Further, humic acid (HA) and inorganic ions have minimal negative effects on degradation efficiency. Radical quenching tests indicate that SO₄^•-, ^•OH, h⁺ and O₂^•- were dominant reactive species in the LaNiO₃/PS/Vis-LED system. These findings shed light on the treatment of phenolic polluted wastewater through persulfate activation and provide new approach for application of low-power consuming visible-LEDs in water treatment.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108773"},"PeriodicalIF":6.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Green and efficient recovery of phosphorus as Vivianite via anaerobic fluidized bed reactor (AFBR) from aquaculture wastewater","authors":"Hongjin Ji ,&nbsp;Yitong Sun ,&nbsp;Danhui Liang ,&nbsp;Jifei Chang ,&nbsp;Xiaoming Yang ,&nbsp;Xin Wang ,&nbsp;Nan Li","doi":"10.1016/j.jwpe.2025.108718","DOIUrl":"10.1016/j.jwpe.2025.108718","url":null,"abstract":"<div><div>The expansion of aquaculture has led to high-density farming wastewater rich in Total Phosphorus (TP) and Chemical Oxygen Demand (COD), causing severe eutrophication. In response to the low phosphorus recovery efficiency observed in conventional fish aquaculture wastewater (AWW) systems, a novel phosphorus recovery process was developed by integrating an anaerobic fluidized bed reactor (AFBR) with the formation of vivianite (Fe₃(PO₄)₂·8H₂O) as the precipitate product. The long-term laboratory-scale trials demonstrated the performance of the AFBR in treating AWW. Optimization of the iron source to FeCl₃ enabled the AFBR to achieve a vivianite recovery efficiency of 77 %, along with the phosphorus removal of 98 % and the COD removal efficiency of 99 %. Parameter optimization experiments revealed that extending the hydraulic retention time (HRT) to 24 h and increasing the upward flow velocity to 12 m·h⁻¹ improved phosphorus recovery efficiency by 11 % and 5.8 %, respectively. Based on the Life Cycle Assessment (LCA) and cost analysis, the FeCl₃-enhanced AFBR process producing vivianite offers better environmental performance and a net profit of 0.54 USD per ton of AWW treated.This study presents an efficient, eco-friendly, and cost-effective method for phosphorus recovery from the AWW, offering new insights into the high-value conversion of aquaculture effluents.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108718"},"PeriodicalIF":6.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed feature engineering with fuzzy symbolic regression for predicting settling velocity in water treatment","authors":"Adriano Bressane ,&nbsp;Daniel H.R. Toda ,&nbsp;Rogerio G. Negri ,&nbsp;Jorge K.S. Formiga ,&nbsp;Abayomi O. Bankole ,&nbsp;Afolashade R. Bankole ,&nbsp;Soroosh Sharifi ,&nbsp;Rodrigo Moruzzi","doi":"10.1016/j.jwpe.2025.108749","DOIUrl":"10.1016/j.jwpe.2025.108749","url":null,"abstract":"<div><div>Predicting the settling velocity of fractal aggregates remains a challenge in water treatment, as classical models like Stokes' Law oversimplify the influence of non-sphericity, porosity, and complex morphology. Empirical and fractal-based models lack generalizability, while most machine learning models operate as black boxes, providing limited physical insight. This study proposes a Physics-Informed Machine Learning Fuzzy Symbolic Regression (PIML-SR) framework enhanced with fuzzy preprocessing to derive interpretable and physically consistent equations for settling velocity prediction. A dataset of <em>Al</em>-kaolinite flocs was obtained using high-speed imaging in a sedimentation column. Morphological parameters and physics-based descriptors, such as drag force and Reynolds number, were incorporated through fuzzy preprocessing, which converts normalized features into smooth membership functions to handle regime transitions and measurement uncertainty, combined with fuzzy symbolic regression. The PIML-SR model demonstrated excellent accuracy (R² &gt; 0.99, MAE ≈ 0.015 μm/s) and robustness to up to 10 % Gaussian noise. In contrast, a baseline symbolic model (R² ≈ 0.56, MAE ≈ 556.6 μm/s) and a purely data-driven artificial neural network (R² ≈ 0.63, MAE ≈ 518.3 μm/s), both trained solely on morphological features, along with a Physics-Informed Neural Network (R² ≈ −1.93, MAE ≈ 1794.9 μm/s), all exhibited limited or poor accuracy, underscoring the critical importance of integrating physical knowledge, as achieved by the proposed fuzzy symbolic regression approach, for attaining high-fidelity, generalizable, and interpretable predictions. This represents the first application of a fuzzy-enhanced PIML-SR framework for sedimentation, providing an interpretable, physically grounded, and noise-resilient approach for optimizing sedimentation processes in water treatment.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108749"},"PeriodicalIF":6.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active site on boron‑nitrogen co-doping lignin-based carbon nanotube-coated nZVI for enhanced hexavalent chromium removal by adsorption-redox behavior from groundwater","authors":"Canghai Guan ,&nbsp;Long Guo ,&nbsp;Hongyu Ren ,&nbsp;Kunteng Jia ,&nbsp;Yongchang Sun","doi":"10.1016/j.jwpe.2025.108769","DOIUrl":"10.1016/j.jwpe.2025.108769","url":null,"abstract":"<div><div>The utilization of high-efficiency and environmentally friendly materials for pollutant removal has remained a research hotspot in groundwater remediation studies. Herein, a boron (B) and nitrogen (N) co-doped lignin-based carbon nanotube-coated nano zero-valent iron (nZVI) composite (Fe⁰@LC-NB) was synthesized via a straightforward one-step pyrolysis protocol for Cr(VI) remediation from groundwater. This synthetic approach simultaneously achieves the carbonization of lignin, growth of carbon nanotubes (CNTs), formation of nZVI, and co-doping of boron and nitrogen, resulting in a tubular structure that effectively prevents the aggregation of nZVI and enhances its reactivity. The abundant active sites of B–N–C, B<img>C, and pyridinic N generated by B/N co-doping are absent in singly-doped materials, significantly boosting the Cr(VI) adsorption capacity. Simultaneously, the electron donor-acceptor system formed by B, N, and Fe⁰ markedly increases the interfacial electron transfer rate, thereby achieving exceptional redox activity of the material. The results showed that the maximal adsorption capacity of Fe⁰@LC-NB reached 232.94 mg/g, which was 1.5 times that of un-modified lignin-based carbon nanotube-coated nZVI (Fe⁰@LC). The mechanism analysis indicates that electrostatic adsorption, redox reactions, and complexation processes were the major reasons for Cr(VI) removal. Moreover, unlike conventional nZVI materials that undergo rapid passivation in application, the protective carbon shell enables the composite to maintain outstanding performance even in complex aquatic environments, offering a cost-effective and efficient method for groundwater remediation.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108769"},"PeriodicalIF":6.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An attention-based parallel model with sliding window decomposition algorithm for water quality prediction","authors":"Yahong Yang ,&nbsp;Pengtang Zhang ,&nbsp;Yali Wang","doi":"10.1016/j.jwpe.2025.108751","DOIUrl":"10.1016/j.jwpe.2025.108751","url":null,"abstract":"<div><div>Accurate forecasting of effluent water quality is essential for enhancing the safety and economic efficiency of wastewater treatment plants (WWTPs) due to the volatility and time-varying nature of effluent water quality. Representative neural networks, such as Long Short-Term Memory (LSTM), have been extensively employed in time-series prediction. However, as the volume of water quality data increases, these models become unstable, making accurate prediction challenging. This study proposes a hybrid prediction method, DVIBM, based on optimized decomposition for forecasting effluent water quality. DVIBM integrates the Dung Beetle Optimization (DBO) algorithm, Variational Mode Decomposition (VMD), Informer, Bidirectional Long Short-Term Memory (BiLSTM) network, and the multi-scale attention mechanism (MUSE). The DBO algorithm is employed to optimize the hyperparameters <span><math><mi>α</mi></math></span> and <span><math><mi>k</mi></math></span> in VMD, within a sliding window framework, to determine the decomposition bandwidth and the number of modes. The original water quality time-series is decomposed into multiple sub-series, with future data excluded during the process to effectively extract features while preventing data leakage. DVIBM couples Informer and BiLSTM via the MUSE attention mechanism, adaptively fusing multi-scale long- and short-term features, thereby reducing error accumulation and propagation in cascaded or single-architecture. Across varying sliding-window parameter combinations and time steps, as well as in ablation comparisons, DVIBM achieves MAE/MSE/R² of 0.104/0.017/0.975 for effluent TN and 0.071/0.008/0.969 for TP, significantly outperforming the benchmark models. Global and local interpretability analyses of effluent TN and TP are conducted using the SHAP (Shapley Additive Explanations) algorithm, providing theoretical support for the interpretability of wastewater treatment systems.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108751"},"PeriodicalIF":6.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time effluent water quality prediction model based on BiLSTM and KAN for wastewater treatment plants","authors":"Siyu Liu ,&nbsp;Zhaocai Wang","doi":"10.1016/j.jwpe.2025.108750","DOIUrl":"10.1016/j.jwpe.2025.108750","url":null,"abstract":"<div><div>Predicting effluent water quality in wastewater treatment plants (WWTPs) is essential for operation optimization, resource efficiency, and regulatory compliance. However, traditional methods struggle with complex temporal dynamics and nonlinear interactions, and current research lacks unified approaches for feature interaction, noise robustness, and multiscale modeling. In this study, we introduce a hybrid model combining bidirectional long short-term memory (BiLSTM) and Kolmogorov-Arnold networks (KAN), alongside a feature-selection mechanism that fuses Spearman, Kendall, and maximal information coefficient (MIC) metrics to identify key water-quality drivers. The feature-selection strategy integrates three methods to capture both monotonic and non-monotonic associations, reducing noise by focusing on impactful predictors. The model synergistically combines BiLSTM's bidirectional temporal feature extraction (capturing past-future context of time-series data) with KAN's strong nonlinear approximation power (modeling complex interactions via spline-based univariate function combinations, based on the Kolmogorov-Arnold theorem), optimizing spatiotemporal feature integration through a dynamic weighted gating mechanism. Experimental results show that, compared with benchmark models such as long short-term memory (LSTM), the model reduces the root mean square error (RMSE) in predicting effluent chemical oxygen demand (COD) by 7.67 % to 45.17 % and improves the coefficient of determination (R²) by 0.96 % to 14.76 %, demonstrating superior forecasting performance. Temporal differential analysis uncovers water quality fluctuations within a day, while multiscale forecasting achieves R² &gt; 0.92, validating the model's ability to capture dynamic changes and perform nonlinear mapping. This study further applies SHapley Additive Explanation (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for interpretability: SHAP identifies key drivers while LIME clarifies how these variables influence specific predictions, aiding operational adjustments. Noise-injection tests confirm robustness, ensuring reliability under sensor drift. This framework offers a comprehensive, interpretable, and resilient solution for real-time WWTP control (e.g., dynamic carbon source dosing) and advances smart water management.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108750"},"PeriodicalIF":6.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced electrochemical detection of the antibiotic levofloxacin using temperature optimized Er2MoO6 nanomaterials for environmental monitoring","authors":"Kumar Gokulkumar ,&nbsp;Sri Balaji Natarajan ,&nbsp;Shen-Ming Chen ,&nbsp;Sakthivel Kogularasu ,&nbsp;Shih-Hsuan Chen ,&nbsp;Kun-Mu Lee","doi":"10.1016/j.jwpe.2025.108757","DOIUrl":"10.1016/j.jwpe.2025.108757","url":null,"abstract":"<div><div>Levofloxacin (LFX) is widely used in healthcare and aquaculture due to its structural stability and physicochemical properties, which allow its residues to persist in the environment and pose significant risks to human health. However, conventional sensors often lack the efficiency and sensitivity required for detecting complex molecular compounds. In this work, a temperature-optimized erbium molybdate (Er₂MoO₆)-based electrochemical sensor was developed for the highly sensitive and selective detection of LFX. The Er₂MoO₆ nanoparticles, synthesized via a hydrothermal process at two different temperatures (160 °C and 200 °C), exhibited excellent stability and reusability for sustained sensing. Notably, the sample (Er₂MoO₆-T2) synthesized at 200 °C demonstrated significant improvements in crystallinity, morphology, and surface properties. The Er₂MoO₆-T2 modified glassy carbon electrode achieved an ultra-low detection limit of 0.00146 μM, a wide linear range (0.0025–2125.5 μM), and outstanding selectivity, reproducibility, and long-term stability. Furthermore, the temperature-optimized Er₂MoO₆ nanoparticles enabled high recovery rates of 98.6 % in complex matrices such as human urine, blood, lake water, and pond water, confirming the sensor's reliability for real-world applications. This study highlights the potential of Er₂MoO₆-based electrodes as high-performance electrochemical platforms for antibiotic monitoring.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108757"},"PeriodicalIF":6.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Highly efficient (Au)(III) recovery from acid wastewater by thiosemicarbazide modified Zr-MOF","authors":"Guowen He ,&nbsp;Dan Wu ,&nbsp;Dechong Ma ,&nbsp;Jiaqi Bu ,&nbsp;Zecheng Cheng","doi":"10.1016/j.jwpe.2025.108743","DOIUrl":"10.1016/j.jwpe.2025.108743","url":null,"abstract":"<div><div>The design and development of new precious metal recovery adsorbents have significant socio-economic benefits; this study successfully synthesized a high-performance gold ion adsorbent, UiO-66-TMB, by modifying UiO-66-NH₂ with thiosemicarbazide, a common industrial chemical rich in amino and thiol groups. The obtained material exhibits excellent stability and adsorption performance, with the maximum adsorption capacity of 729.15 mg·g⁻¹ at pH = 2. The adsorbent has good recyclability and can maintain superior selectivity in environments containing multiple impurity ions. In addition, even under strong acidic conditions with low initial gold ion concentration, a saturated adsorption capacity of 705.6 mg·g⁻¹ can be maintained. Mechanism studies have shown that there is a synergistic effect between electrostatic attraction, coordination binding, and redox reactions between Au and functional groups containing N and S elements on the surface of materials. The functionalization strategy proposed in this work opens new research directions for developing efficient and stable precious metal adsorption materials, offering a potential solution for sustainable resource recovery from industrial wastewater and electronic waste.</div></div>","PeriodicalId":17528,"journal":{"name":"Journal of water process engineering","volume":"78 ","pages":"Article 108743"},"PeriodicalIF":6.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}