ACS ES&T engineering最新文献

{"title":"Confined Manganese-Based Catalysts for Gaseous Pollutant Removal: A Critical Review","authors":"Jialin Li, Zhijian Xiao, Jingling Yang, Mingshan Zhu","doi":"10.1021/acsestengg.4c00355","DOIUrl":"https://doi.org/10.1021/acsestengg.4c00355","url":null,"abstract":"Manganese-based materials are widely applied as catalysts for catalytic removal of gaseous pollutants due to their low cost and excellent redox performance. However, existing challenges such as unsatisfactory activity and stability hinder the application of manganese-based catalysts. Confining manganese-based materials into specific regions has the potential to influence intermolecular arrangement and mass diffusion and lower the activation energy barrier, thereby enhancing reaction stability and the catalyst’s activity. In this critical review, we briefly summarize the common reported nanoconfined manganese-based analogues for gaseous pollutant elimination including nitrogen oxides (NO_<i>x</i>) and volatile organic compounds (VOCs). Details of the commonly reported hosts for confined catalysts and their impact on catalytic behavior and an in-depth discussion of the related mechanism are included. Through this critical review, we aim to raise the research attention on using confined catalysis as a fundamental guide or even tool to improve the catalytic performance of gaseous pollutant elimination.","PeriodicalId":7008,"journal":{"name":"ACS ES&T engineering","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confined Manganese-Based Catalysts for Gaseous Pollutant Removal: A Critical Review","authors":"Jialin Li,&nbsp;Zhijian Xiao,&nbsp;Jingling Yang* and Mingshan Zhu*,&nbsp;","doi":"10.1021/acsestengg.4c0035510.1021/acsestengg.4c00355","DOIUrl":"https://doi.org/10.1021/acsestengg.4c00355https://doi.org/10.1021/acsestengg.4c00355","url":null,"abstract":"<p >Manganese-based materials are widely applied as catalysts for catalytic removal of gaseous pollutants due to their low cost and excellent redox performance. However, existing challenges such as unsatisfactory activity and stability hinder the application of manganese-based catalysts. Confining manganese-based materials into specific regions has the potential to influence intermolecular arrangement and mass diffusion and lower the activation energy barrier, thereby enhancing reaction stability and the catalyst’s activity. In this critical review, we briefly summarize the common reported nanoconfined manganese-based analogues for gaseous pollutant elimination including nitrogen oxides (NO_<i>x</i>) and volatile organic compounds (VOCs). Details of the commonly reported hosts for confined catalysts and their impact on catalytic behavior and an in-depth discussion of the related mechanism are included. Through this critical review, we aim to raise the research attention on using confined catalysis as a fundamental guide or even tool to improve the catalytic performance of gaseous pollutant elimination.</p>","PeriodicalId":7008,"journal":{"name":"ACS ES&T engineering","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral-Based Machine Learning Enables Rapid and Large-Scale Adsorption Capacity Prediction of Heavy Metals in Soil","authors":"Chongchong Qi, Tao Hu, Mengting Wu, Yong Sik Ok, Han Wang, Liyuan Chai, Zhang Lin","doi":"10.1021/acsestengg.4c00325","DOIUrl":"https://doi.org/10.1021/acsestengg.4c00325","url":null,"abstract":"Accurate and large-scale estimation of the soil adsorption capacity of heavy metals (HMs) is vital to tackle soil HM contamination. Here, a novel framework has been developed to evaluate the adsorption capacity of HMs in soil using visible and near-infrared spectroscopy. Soil attributes were accurately estimated without any spectral preprocessing using a combined autoencoder (AE) and deep neural network (DNN) approach. Soil HM adsorption capability was then evaluated based on spectral-derived soil attributes, using 2,416 data points on Cd(II), Pb(II), and Cr(VI). The proposed AE-DNN models offer accurate estimations of soil attributes with an average <i>R</i>² of 0.811 on the independent testing sets. The trained AE-DNN models can reveal patterns typically used by experts to identify bond assignments and promote data-driven knowledge discovery. By comparison with adsorption capacity maps based on actual and estimated soil attributes, we show that the spectral-based soil adsorption capacity evaluation is statistically reliable. Our adsorption capacity maps for the EU and USA identify known soil contamination sites and undocumented areas of high contamination risk. Our framework enables rapid and large-scale prediction of the adsorption capacity of HMs in soil and showcases important guidance for further soil contamination testing, soil management, and industrial planning.","PeriodicalId":7008,"journal":{"name":"ACS ES&T engineering","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Cost and Point-of-Use Sensing Device Based on 2D Diffusion-Controlled Colorimetric Sensing Array for Continuous and Multiplexed Air Pollution Monitoring","authors":"Jingjing Yu, Wei Ding, Francis Tsow, Chinmay Chandrakant Dixit, Vishal Varun Tipparaju, Erica Forzani, Xiaojun Xian","doi":"10.1021/acsestengg.4c00416","DOIUrl":"https://doi.org/10.1021/acsestengg.4c00416","url":null,"abstract":"The development of a low-cost and point-of-use sensing device capable of continuously and simultaneously detecting various air pollutants is essential for enhancing air pollution monitoring in communities and workplaces. This technology would enable real-time monitoring, facilitating prompt interventions to mitigate the impact of air pollution on human health and the environment. Colorimetric gas sensors are often used to detect different air pollutants owing to their high selectivity and sensitivity, low cost, and compact size. Nevertheless, continuous and multiplexed detection of gas with a colorimetric sensor often requires lifetime-extension methods and an expensive, space-demanding, and power-hungry optical imaging system as the signal transducer. To address these challenges, we introduce a fully integrated portable gas sensing device that uses cheap, miniaturized, and power-efficient electronic and sensing components. This device enables continuous and multiplexed monitoring of air pollutants through a two-dimensional (2D) diffusion-controlled colorimetric sensor array. By selecting sensing probes with specific colorimetric chemical reactions for different gas analytes, the 2D sensor array ensures specificity across a range of analytes. The test results demonstrate that our 2D diffusion-based colorimetric sensing device can be used for sensitive, selective, and multiplexed monitoring of different air pollutants (ammonia, carbon monoxide, carbon dioxide, and nitrogen dioxide) in a continuous manner. This device has proven effective in monitoring pollutants released from car exhaust. Our innovative chemical sensing approach has facilitated the creation of a compact, cost-effective, and easily deployable sensing device. This device offers continuous and multiplexed sensing capabilities, making it ideal for air pollution monitoring in communities and workplaces to provide evidence-based environmental data for effective air pollution management strategies.","PeriodicalId":7008,"journal":{"name":"ACS ES&T engineering","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pipe Failure Prediction in the Water Distribution System Using a Deep Graph Convolutional Network and Temporal Failure Series","authors":"Yanran Xu, Zhen He","doi":"10.1021/acsestengg.4c00234","DOIUrl":"https://doi.org/10.1021/acsestengg.4c00234","url":null,"abstract":"Ensuring the safety and reliability of the water distribution system (WDS) manifests significant importance for residential, commercial, and industrial needs and may benefit from the structure deterioration models for early warning of water pipe breaks. However, challenges exist in model calibration with limited monitoring data, unseen underground conditions, or high computing requirements. Herein, a novel deep learning-based DeeperGCN framework was proposed to predict pipe failure by cooperating with graph convolutional network (GCN) models for graph processing. The DeeperGCN model achieved much deeper architectures and was designed to utilize spatial and temporal data simultaneously. Two graph representation methods and three GCN models were compared, showing the best predictions with the “Pipe_as_Edge” method and the DeeperGEN model. To identify the priority of pipe maintenance directly, the prediction targets were assigned as a binary classification question to determine break or not over 1-, 3-, and 5-year periods, with prediction accuracies of 96.91, 96.73, and 97.23%, respectively. The issue of data imbalance was observed and addressed through varied evaluation metrics, resulting in the weighted F1 scores &gt;0.96. The DeeperGCN framework demonstrated potential applications in visualizing pipe failure prediction with high accuracies of 97.09, 96.31, and 97.81% across three periods in 2015, for example.","PeriodicalId":7008,"journal":{"name":"ACS ES&T engineering","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pipe Failure Prediction in the Water Distribution System Using a Deep Graph Convolutional Network and Temporal Failure Series","authors":"Yanran Xu,&nbsp; and ,&nbsp;Zhen He*,&nbsp;","doi":"10.1021/acsestengg.4c0023410.1021/acsestengg.4c00234","DOIUrl":"https://doi.org/10.1021/acsestengg.4c00234https://doi.org/10.1021/acsestengg.4c00234","url":null,"abstract":"<p >Ensuring the safety and reliability of the water distribution system (WDS) manifests significant importance for residential, commercial, and industrial needs and may benefit from the structure deterioration models for early warning of water pipe breaks. However, challenges exist in model calibration with limited monitoring data, unseen underground conditions, or high computing requirements. Herein, a novel deep learning-based DeeperGCN framework was proposed to predict pipe failure by cooperating with graph convolutional network (GCN) models for graph processing. The DeeperGCN model achieved much deeper architectures and was designed to utilize spatial and temporal data simultaneously. Two graph representation methods and three GCN models were compared, showing the best predictions with the “Pipe_as_Edge” method and the DeeperGEN model. To identify the priority of pipe maintenance directly, the prediction targets were assigned as a binary classification question to determine break or not over 1-, 3-, and 5-year periods, with prediction accuracies of 96.91, 96.73, and 97.23%, respectively. The issue of data imbalance was observed and addressed through varied evaluation metrics, resulting in the weighted F1 scores &gt;0.96. The DeeperGCN framework demonstrated potential applications in visualizing pipe failure prediction with high accuracies of 97.09, 96.31, and 97.81% across three periods in 2015, for example.</p>","PeriodicalId":7008,"journal":{"name":"ACS ES&T engineering","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acid Dosing Increases Recoverable Phosphorus during Different Occupancy Conditions in Full-Scale Urine Diversion System","authors":"Lucas Crane*,&nbsp;Daniella Saetta and Treavor H. Boyer*,&nbsp;","doi":"10.1021/acsestengg.4c0016410.1021/acsestengg.4c00164","DOIUrl":"https://doi.org/10.1021/acsestengg.4c00164https://doi.org/10.1021/acsestengg.4c00164","url":null,"abstract":"<p >Phosphorus (P) is a finite resource used in fertilizers. Urine contains high concentrations of P that can be recovered using urine diversion (UD), but current UD systems become clogged by P precipitation in piping, inhibiting their operations and reducing recoverable P. Dosing systems with acetic acid can prevent precipitation, resulting in more available nutrients for recovery. This study monitored a full-scale multistory UD system and implemented acid dosing to prevent clogging and produce urine suitable for P recovery. Both baseline (i.e., no acid) and acid dosing conditions were tested during normal and below-normal occupancies. In baseline systems, urine collected during below-normal occupancy had higher pH and greater nutrient losses compared to during normal occupancy. However, during both occupancies, baseline systems had clogs that decoupled occupancy patterns from urine collection in the tank and lowered the mass of recoverable P. During both occupancies, acid dosing dissolved pre-existing precipitate, resulting in ∼10× greater recoverable P than in baseline systems, and partially stabilized urine (&gt;10%) and lowered pH (&lt;9) until ∼7 days of operation, suggesting that urine can be immediately treated once the storage tank is full. The results demonstrate that acid dosing can prevent operation challenges in UD, improving UD’s technology readiness.</p>","PeriodicalId":7008,"journal":{"name":"ACS ES&T engineering","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acid Dosing Increases Recoverable Phosphorus during Different Occupancy Conditions in Full-Scale Urine Diversion System","authors":"Lucas Crane, Daniella Saetta, Treavor H. Boyer","doi":"10.1021/acsestengg.4c00164","DOIUrl":"https://doi.org/10.1021/acsestengg.4c00164","url":null,"abstract":"Phosphorus (P) is a finite resource used in fertilizers. Urine contains high concentrations of P that can be recovered using urine diversion (UD), but current UD systems become clogged by P precipitation in piping, inhibiting their operations and reducing recoverable P. Dosing systems with acetic acid can prevent precipitation, resulting in more available nutrients for recovery. This study monitored a full-scale multistory UD system and implemented acid dosing to prevent clogging and produce urine suitable for P recovery. Both baseline (i.e., no acid) and acid dosing conditions were tested during normal and below-normal occupancies. In baseline systems, urine collected during below-normal occupancy had higher pH and greater nutrient losses compared to during normal occupancy. However, during both occupancies, baseline systems had clogs that decoupled occupancy patterns from urine collection in the tank and lowered the mass of recoverable P. During both occupancies, acid dosing dissolved pre-existing precipitate, resulting in ∼10× greater recoverable P than in baseline systems, and partially stabilized urine (&gt;10%) and lowered pH (&lt;9) until ∼7 days of operation, suggesting that urine can be immediately treated once the storage tank is full. The results demonstrate that acid dosing can prevent operation challenges in UD, improving UD’s technology readiness.","PeriodicalId":7008,"journal":{"name":"ACS ES&T engineering","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-valorization of Food Waste and CO2 to Produce Volatile Fatty Acids Using Liter-Scale Tubular Microbial Electrosynthesis Cells","authors":"Yanhong Bian, Aaron M. Leininger, Weilan Zhang, Yanna Liang, Zhiyong Jason Ren","doi":"10.1021/acsestengg.4c00218","DOIUrl":"https://doi.org/10.1021/acsestengg.4c00218","url":null,"abstract":"This study presents the simultaneous conversion of food waste and CO₂ into volatile fatty acids (VFAs) using a 6 L tubular microbial electrosynthesis cell (MES). The MES reactor uses a bioanode to convert food waste into current and CO₂, while on the cathode, H₂ is produced and subsequently consumed by cathode microbes for the conversion of CO₂ to VFAs. The study reveals that system performance is impacted by organic loading, applied voltage, and flow rate, and optimal operational conditions achieve a VFA titer of 1763 mg/L with the Coulombic efficiency (CE) exceeding 90% at the anode, highlighting efficient electron recovery from food waste. Resistance analysis indicates that the cathode contributed most to system resistance, while microbial community analysis shows a synergy between fermentative and electroactive bacteria in the anode and dominant acetogens in the cathode, facilitating efficient electron recovery and VFA synthesis, respectively. The research underscores the tubular MES’s potential for sustainable food waste treatment and CO₂ valorization into valuable VFAs, contributing to waste management and greenhouse gas mitigation strategies.","PeriodicalId":7008,"journal":{"name":"ACS ES&T engineering","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Removal and Oxidation of Low Concentration tert-Butanol from Potable Water using Nonthermal Plasma Coupled with Metal Oxide Adsorption","authors":"Cristina E. Stere*,&nbsp;Maicon Delarmelina,&nbsp;Mbongiseni W. Dlamini,&nbsp;Sarayute Chansai,&nbsp;Philip R. Davies,&nbsp;Graham J. Hutchings,&nbsp;C. Richard A. Catlow* and Christopher Hardacre*,&nbsp;","doi":"10.1021/acsestengg.4c0016610.1021/acsestengg.4c00166","DOIUrl":"https://doi.org/10.1021/acsestengg.4c00166https://doi.org/10.1021/acsestengg.4c00166","url":null,"abstract":"<p >Taste and odor are crucial factors in evaluating the quality of drinking water for consumers. Geosmin is an example of a pollutant commonly found in potable water responsible for earthy and musty taste, and odor even at low concentrations. We have investigated the use of a hybrid two-step adsorption-mineralization process for low-level volatile organic compounds removal from potable water using dielectric barrier discharge over common metal oxides (MO). The system proposed is a proof of principle with <i>tert</i>-butanol (TBA) used as a model compound for geosmin removal/degradation during wastewater treatment when combined with an appropriate metal oxide adsorbent. Initial assessments of the adsorption properties of titania by density functional theory (DFT) calculations and experimental tests indicated that the adsorption of geosmin and TBA with water present results in only weak interactions between the sorbate and the metal oxide. In contrast, the DFT results show that alumina could be a suitable adsorbent for these tertiary alcohols and were reinforced by experimental studies. We find that while there is a competitive effect between the water and TBA adsorption from gaseous/liquid feed, the VOC can be removed, and the alumina will be regenerated by the reactive oxygen species (ROS) produced by a dielectric barrier discharge (DBD). The use of alumina in conjunction with NTP leads to efficient degradation of the adsorbate and the formation of oxygenated intermediates (formates, carbonates, and carboxylate-type species), which could then be mineralized for the regeneration of the adsorbent. A reaction mechanism has been proposed based on the <i>in-situ</i> infrared measurements and DFT calculations, while the removal of TBA with conventional heating is indicative of a gradual desorption process as a function of temperature rather than the destruction of the adsorbate. Furthermore, steady performance was observed after several adsorption–regeneration cycles, indicating no alteration of the adsorption properties of alumina during the NTP treatment and demonstrating the potential of the approach to be applied in the treatment of high throughput of water, without the challenges faced by the biocatalysts or formation of toxic byproducts.</p>","PeriodicalId":7008,"journal":{"name":"ACS ES&T engineering","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestengg.4c00166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}