Green Chemical Engineering最新文献

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IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

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

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IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

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

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IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

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

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IF 9.1 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

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

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Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

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

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Q1 ENGINEERING, CHEMICAL

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 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

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

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.

在当代，四环素作为一种普遍的抗生素被广泛应用于生活的各个方面。然而，抗生素的过度使用对环境造成了明显的影响。因此，科学界越来越重视开发能有效降解四环素的催化剂。本研究开发了一种新型纳米材料，用二氧化硅外壳封装碲化镉量子点（QDs）。独特的合成方法产生的复合材料具有异质性，大大增加了与污染物的接触面积。因此，光电子向二氧化硅球体的转移得到了显著改善，从而在催化过程中实现了更高效的分离。研究调查了催化剂的负载量、四环素的初始浓度、pH 值以及碲化镉 QDs（SiO2 + 碲化镉 QDs）的重量比等不同因素如何影响光催化降解四环素的效果。研究结果表明，催化剂浓度为 0.25 克/升、溶液 pH 值为 9 时，降解效率最佳，在短短 2 小时内降解率达到 96%。

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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 Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

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

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.

利用膜技术分离 He/H2 在从天然气中提取 He 领域获得了极大的关注。该工艺面临的最大挑战之一是两种气体分子的动力学直径非常接近，导致膜的选择性较低。在本研究中，我们通过分子模拟和机器学习方法研究了用于 He/H2 分离的金属有机框架（MOF）膜的结构性能关系。通过分子模拟，我们从计算准备实验（CoRE）MOF 数据库中识别出了具有高分离性能的潜在 MOF 膜，并进一步阐明了以扩散为主导的机理。此外，还建立了基于随机森林（RF）的机器学习模型，以确定影响MOF膜He/H2分离性能的关键因素。结果表明，孔极限直径（PLD）和空隙率（φ）分别是决定膜选择性和氦渗透性的最重要物理特征。此外，为验证分子模拟结果，还进行了密度泛函理论（DFT）计算，结果表明孔表面的电负性原子可增强 He/H2 的扩散分离，这对提高膜的 He/H2 选择性至关重要。这项研究为在分子水平上设计和开发用于分离 He/H2 的新型 MOF 膜提供了有益的启示。

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Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

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

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Q1 ENGINEERING, CHEMICAL

Green Chemical Engineering

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

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