npj Clean Water最新文献_第10页

Scalable hydrocell technology based on recycled polymers for atmospheric water harvesting 基于可回收聚合物的可扩展水电池技术用于大气水收集

IF 11.4 1区工程技术

npj Clean Water Pub Date : 2025-12-10 DOI: 10.1038/s41545-025-00534-7

V. Campos, C. A. Tanaka, D. G. Marques, N. P. A. Granado

{"title":"Scalable hydrocell technology based on recycled polymers for atmospheric water harvesting","authors":"V. Campos, C. A. Tanaka, D. G. Marques, N. P. A. Granado","doi":"10.1038/s41545-025-00534-7","DOIUrl":"https://doi.org/10.1038/s41545-025-00534-7","url":null,"abstract":"Freshwater scarcity demands materials and systems that enable efficient, low-cost, and environmentally responsible water generation. Here we report a hydrocell technology based on a recycled-polymer superabsorbent, poly(acrylamide-co-potassium acrylate) (PANSAP), synthesized through solvent-free alkaline hydrolysis of post-consumer polyacrylonitrile textiles. The process achieves an Environmental Factor (E-factor) of 0.056 when ammonia released during hydrolysis is recovered as ammonium phosphate, comparable to benchmark polymer-recycling efficiencies. The resulting cross-linked polymer exhibits a swelling capacity exceeding 200 g H₂O g⁻¹ and a stable atmospheric uptake of 0.43 ± 0.09 g g⁻¹ over 79 days at 69–90% relative humidity. Integrated into modular hydrocell plates, PANSAP delivers hybrid solar–electric desorption yields of 4–6 L day⁻¹ from 25 units at 0.90–1.25 kWh L⁻¹, approaching the thermodynamic minimum for water evaporation. Long-term cycling tests indicate negligible nitrogen loss (0.09 mg L⁻¹ NH₃ yr⁻¹), corresponding to an estimated service life beyond 2,500 cycles. Derived from recycled textiles, partially biodegradable, and agronomically beneficial through potassium release, PANSAP establishes a scalable, circular-materials framework for atmospheric water harvesting. The results position polymer-based hydrocells as a durable and sustainable platform for decentralized freshwater generation in arid and semi-arid regions.","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"6 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145711576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An agile benchmarking framework for wastewater resource recovery technologies 废水资源回收技术的敏捷基准框架

IF 11.4 1区工程技术

npj Clean Water Pub Date : 2025-12-06 DOI: 10.1038/s41545-025-00537-4

Xinyi Zhang, Saumitra Rai, Zixuan Wang, Yalin Li, Jeremy S. Guest

引用次数: 0

Dual-defect LaFeO3 photo-Fenton removes β-E2 from dairy wastewater 双缺陷LaFeO3光fenton去除乳废水中的β-E2

IF 11.4 1区工程技术

npj Clean Water Pub Date : 2025-12-05 DOI: 10.1038/s41545-025-00535-6

Shiyu Lv, Tian Yuan, Xueyan Zhang, Pu Yang, Kerong Fu, Xia Yao, Weilin Fu, Qiuxian Zhang, Yanli Luo, Feng Wang

引用次数: 0

Augmented machine learning with limited data for hydrogen yield prediction in wastewater dark fermentation 基于有限数据的增强机器学习用于废水暗发酵产氢量预测

IF 11.4 1区工程技术

npj Clean Water Pub Date : 2025-11-28 DOI: 10.1038/s41545-025-00529-4

Chong Liu, Fayong Li, Pengyan Zhang, Paramasivan Balasubramanian

{"title":"Augmented machine learning with limited data for hydrogen yield prediction in wastewater dark fermentation","authors":"Chong Liu, Fayong Li, Pengyan Zhang, Paramasivan Balasubramanian","doi":"10.1038/s41545-025-00529-4","DOIUrl":"https://doi.org/10.1038/s41545-025-00529-4","url":null,"abstract":"Current machine learning (ML) efforts for predicting hydrogen yield in dark fermentation are constrained by limited sample sizes and distributional skewness, yielding unstable models. These data characteristics fundamentally restrict generalization and hinder the optimization of process conditions. In this study, a generative adversarial network (GAN)-inspired strategy was developed to augment an initial dataset of 210 dark fermentation samples to 1050 synthetic instances, significantly enhancing data distribution normality and coverage. Across nine ML algorithms, the Histogram-based Gradient Boosting (HGB) model performed best on the test dataset ( R 2 ≈ 0.95; RMSE < 0.06; MAE < 0.05). SHAP and accumulated local effects (ALE) analyses indicated that butyrate, biomass, and Ni positively influenced hydrogen yield, whereas elevated COD, ethanol, and longer hydraulic retention time (HRT) reduced it. Two-dimensional ALE plots further identified the optimal operating conditions for dark fermentation (Fe/Ni ratio ≈ 1:3; HRT of 4–5 h; pH ≈ 4.9; and COD < 25 g L − 1 ). A Python-based graphical user interface (GUI) integrating the HGB model was developed for practical hydrogen yield prediction and process diagnostics. This study demonstrates that combining GAN-inspired data with gradient boosting models can enhance both prediction accuracy and process control in biohydrogen production from wastewater.","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"67 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart control of oxychlorine species using reinforcement learning in saline electrochemical oxidation 基于强化学习的盐水电化学氧化中氧氯种类的智能控制

IF 11.4 1区工程技术

npj Clean Water Pub Date : 2025-11-28 DOI: 10.1038/s41545-025-00530-x

Yong-Uk Shin, Dongwoo Kim, Sung Il Yu, Hyokwan Bae, Am Jang

引用次数: 0

Microplastic removal across ten drinking water treatment facilities and distribution systems 在十个饮用水处理设施和分配系统中去除微塑料

IF 11.4 1区工程技术

npj Clean Water Pub Date : 2025-11-23 DOI: 10.1038/s41545-025-00531-w

Charles Balkenbusch, Judith Glienke, Yuhao Wu, Keenan Munno, Michael Jung, Husein Almuhtaram, Robert C. Andrews

引用次数: 0

Phosphorus adsorption in paddy water by immobilized Ce-MOFs: performance, mechanism analysis, and dynamic adsorption 固定化ce - mof对水稻水中磷的吸附性能、机理分析及动态吸附

IF 11.4 1区工程技术

npj Clean Water Pub Date : 2025-11-18 DOI: 10.1038/s41545-025-00524-9

Xianglan Jiao, Tao Xia, Lingzhi Zhang, Lei Shi, Zhimin Ao, Xuede Li, Jie Li

引用次数: 0

Scalable brine treatment using 3D-printed multichannel thermodiffusion 采用3d打印多通道热扩散的可扩展盐水处理

IF 11.4 1区工程技术

npj Clean Water Pub Date : 2025-11-18 DOI: 10.1038/s41545-025-00526-7

Milad Mohsenzadeh, Shuqi Xu, Osman Shamet, Juan F. Torres

引用次数: 0

Techno-economic analysis of multichannel thermodiffusion for desalination and brine concentration 多通道热扩散海水淡化浓缩技术经济分析

IF 11.4 1区工程技术

npj Clean Water Pub Date : 2025-11-18 DOI: 10.1038/s41545-025-00528-5

Christopher Jackson, Shuqi Xu, Juan F. Torres

引用次数: 0

Resolving inherent constraints in eutrophication monitoring of small lakes using multi-source satellites and machine learning 利用多源卫星和机器学习解决小湖泊富营养化监测的固有约束

IF 11.4 1区工程技术

npj Clean Water Pub Date : 2025-11-18 DOI: 10.1038/s41545-025-00525-8

Wei Si, Zhixiong Chen, Chi Yung Jim, Mou Leong Tan, Dong Liu, Yue Yao, Lifei Wei, Shangshang Xu, Fei Zhang

{"title":"Resolving inherent constraints in eutrophication monitoring of small lakes using multi-source satellites and machine learning","authors":"Wei Si, Zhixiong Chen, Chi Yung Jim, Mou Leong Tan, Dong Liu, Yue Yao, Lifei Wei, Shangshang Xu, Fei Zhang","doi":"10.1038/s41545-025-00525-8","DOIUrl":"https://doi.org/10.1038/s41545-025-00525-8","url":null,"abstract":"Remote sensing monitoring of small-lake eutrophication faces challenges such as sparse data, insufficient synergy of multi-source data, and limited model generalization performance. Hence, this study developed a scenario-aware modeling framework for the trophic level index (TLI) by integrating multi-source imagery data from Sentinel-2, GF-1, HJ-2, and PlanetScope, using Dongqian Lake in Zhejiang Province, China as the case study. The cross-sensor prediction accuracy was evaluated using algorithms such as CatBoost Regression (CBR), XGBoost Regression (XGBR), TabPFN Regression (TPFNR), and Linear Regression (LR). Meanwhile, the influence of input features was quantified by SHapley Additive exPlanations (SHAP). The main results found that : (1) Overall annual mean values of total nitrogen/total phosphorus ratio (TN/TP) and TLI were 22.13 and 37.36 ± 4.99, respectively, indicating a mesotrophic and phosphorus-limited state in Dongqian Lake. (2) TLI exhibited the strongest correlation with water color and algal spectral indices, including Normalized Difference Water Index (NDWI), Normalized Green–Red Difference Index (NGRDI), and Blue–Green Ratio (BGR). (3) CBR demonstrated the strongest cross-sensor generalization capability across different imagery, with only minor variations in prediction accuracy (ΔR ≈ 0.07–0.15). Feature attribution analysis identified NDWI, NGRDI, and BGR as primary contributing features for the CBR model. (4) Integrating high-frequency multi-source remote sensing imagery with 27 field surveys achieved seamless monitoring of the TLI. The spatial distribution of TLI showed distinct seasonal variations, with higher values observed in nearshore areas and lower values in the lake center. TLI values were relatively low in spring, but surged sharply and remained elevated in summer. This study provided a reference basis for detailed remote sensing monitoring and management of eutrophication in small lakes.","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"32 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145536154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0