Green Chemical Engineering最新文献

OFC: Outside Front Cover OFC：封面外侧

IF 9.1

Green Chemical Engineering Pub Date : 2024-09-03 DOI: 10.1016/S2666-9528(24)00045-1

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Outside Back Cover 封底外侧

IF 9.1

Green Chemical Engineering Pub Date : 2024-09-03 DOI: 10.1016/S2666-9528(24)00053-0

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Outside Back Cover 封底外侧

IF 9.1

Green Chemical Engineering Pub Date : 2024-07-16 DOI: 10.1016/S2666-9528(24)00028-1

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OFC: Outside Front Cover OFC：封面外侧

IF 9.1

Green Chemical Engineering Pub Date : 2024-07-16 DOI: 10.1016/S2666-9528(24)00020-7

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Outside Back Cover 封底外侧

Green Chemical Engineering Pub Date : 2024-03-15 DOI: 10.1016/S2666-9528(24)00014-1

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OFC: Outside Front Cover OFC：封面外侧

Green Chemical Engineering Pub Date : 2024-03-15 DOI: 10.1016/S2666-9528(24)00006-2

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CdTe QDs@SiO2 composite material for efficient photocatalytic degradation of tetracycline composites 用于高效光催化降解四环素复合材料的 CdTe QDs@SiO2 复合材料

IF 9.1

Green Chemical Engineering Pub Date : 2024-02-02 DOI: 10.1016/j.gce.2024.01.004

{"title":"CdTe QDs@SiO2 composite material for efficient photocatalytic degradation of tetracycline composites","authors":"","doi":"10.1016/j.gce.2024.01.004","DOIUrl":"10.1016/j.gce.2024.01.004","url":null,"abstract":"<div>In the contemporary context, tetracycline is widely utilized as a prevalent antibiotic in various facets of life. However, the excessive use of antibiotics has caused visible environmental consequences. Henceforth, the scientific community has increasingly focused on developing catalysts that exhibit exceptional efficacy in the proficient degradation of tetracycline. In this study, a novel nanomaterial was developed to encapsulate CdTe quantum dots (QDs) with a SiO2 shell. The distinct synthesis approach generated a composite material that showed heterogeneity and considerably increased the contact area with contaminants. Consequently, the transfer of photoelectron to the SiO2 spheres was significantly improved, leading to a more efficient separation during the catalytic process. The study investigated how different factors, such as the loading of the catalyst, the initial concentration of tetracycline, pH levels, and the wight ratio of CdTe QDs (SiO2 + CdTe QDs) affected the effectiveness of photocatalytic tetracycline degradation. The findings indicated that the optimal degradation efficiency was observed at a catalyst concentration of 0.25 g/L and a solution pH of 9, leading to an impressive degradation rate of 96% within a mere 2 h timeframe.</div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"5 4","pages":"Pages 533-542"},"PeriodicalIF":9.1,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666952824000049/pdfft?md5=982092b460fe833e27ca719fa42b006e&pid=1-s2.0-S2666952824000049-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139821346","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}

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Machine learning aided investigation on the structure-performance correlation of MOF for membrane-based He/H2 separation 机器学习辅助研究用于膜式氢/氢分离的 MOF 的结构-性能相关性

IF 9.1

Green Chemical Engineering Pub Date : 2024-02-01 DOI: 10.1016/j.gce.2024.01.005

{"title":"Machine learning aided investigation on the structure-performance correlation of MOF for membrane-based He/H2 separation","authors":"","doi":"10.1016/j.gce.2024.01.005","DOIUrl":"10.1016/j.gce.2024.01.005","url":null,"abstract":"<div>The separation of He/H2 using membrane technology has gained significant interest in the field of He extraction from natural gas. One of the greatest challenges associated with this process is the extremely close kinetic diameters of the two gas molecules, resulting in low membrane selectivity. In this study, we investigated the structure-performance relationship of metal-organic framework (MOF) membranes for He/H2 separation through molecular simulations and machine learning approaches. By conducting molecular simulations, we identified the potential MOF membranes with high separation performance from the Computation-Ready Experimental (CoRE) MOF database, and the diffusion-dominated mechanism was further elucidated. Moreover, random forest (RF)-based machine learning models were established to identify the crucial factors influencing the He/H2 separation performance of MOF membranes. The pore limiting diameter (PLD) and void fraction (φ), are revealed as the most important physical features for determining the membrane selectivity and He permeability, respectively. Additionally, density functional theory (DFT) calculations were carried out to validate the molecular simulation results and suggested that the electronegative atoms on the pore surfaces can enhance the diffusion-based separation of He/H2, which is critical for improving the membrane selectivities of He/H2. This study offers useful insights for designing and developing novel MOF membranes for the separation of He/H2 at the molecular level.</div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"5 4","pages":"Pages 526-532"},"PeriodicalIF":9.1,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666952824000050/pdfft?md5=620637f23e34ac1e1573e48118c6ef78&pid=1-s2.0-S2666952824000050-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139892709","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}

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Outside Back Cover 封底外侧

Green Chemical Engineering Pub Date : 2024-01-22 DOI: 10.1016/S2666-9528(23)00068-7

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OFC: Outside Front Cover OFC：封面外侧

Green Chemical Engineering Pub Date : 2024-01-22 DOI: 10.1016/S2666-9528(23)00061-4

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