Green Chemical Engineering最新文献

{"title":"pH-tuneable simultaneous and selective dye wastewater remediation with digestate-derived biochar: adsorption behaviour, mechanistic insights and potential application","authors":"Ifunanya R. Akaniro ,&nbsp;Gaihong Wang ,&nbsp;Peixin Wang ,&nbsp;Ruilong Zhang ,&nbsp;Wenhua Xue ,&nbsp;Jian Ye ,&nbsp;Jonathan W.C. Wong ,&nbsp;Jun Zhao","doi":"10.1016/j.gce.2024.07.001","DOIUrl":"10.1016/j.gce.2024.07.001","url":null,"abstract":"<div><div>The use of biochar for organic pollutants adsorption has emerged as a key component in wastewater remediation research. In this study, biochar prepared from digestate was subjected to nitric acid functionalization to enhance its adsorption capacity for organic dye mixtures of methylene blue and methyl red in synthetic wastewater. Based on experimental evidence, modified biochar BC750_NM, with a micro-mesoporous structure and a specific surface area of ∼454.15 m²/g had the best adsorption performance at optimum conditions. This adsorbent exhibited both selective and simultaneous dye adsorption upon pH control, mainly attributable to a multi-interaction process in the medium. Notably, the adsorption of both methylene blue and methyl red approached 90% under acidic pH, while methylene blue was preferentially adsorbed over methyl red at alkaline pH to attain an excellent adsorption rate of 100% for methylene blue. Our approach not only yields a valuable resource for mitigating water pollution but also offers a sustainable solution for digestate management, showcasing the potential for innovative techniques to produce synergistic environmental solutions.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Pages 344-356"},"PeriodicalIF":9.1,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141704406","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":"Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models","authors":"Xiuxian Chen,&nbsp;Guzhong Chen,&nbsp;Kunchi Xie,&nbsp;Jie Cheng,&nbsp;Jiahui Chen,&nbsp;Zhen Song,&nbsp;Zhiwen Qi","doi":"10.1016/j.gce.2024.06.005","DOIUrl":"10.1016/j.gce.2024.06.005","url":null,"abstract":"<div><div>For discovering uncharted chemical space of ionic liquids (ILs) for CO₂ dissolution, a reliable generative framework combining re-balanced variational autoencoder (VAE), artificial neural network (ANN), and particle swarm optimization (PSO) is developed based on a comprehensive experimental solubility database from literature. The re-balanced VAE transforms the chemical space of ILs into continuous latent space, which is demonstrated by t-distributed stochastic neighbor embedding (t-SNE) visualization and sampled ions of the latent space. ANN is connected with the re-balanced VAE to predict the CO₂ solubility and the resultant VAE-ANN model achieves a low mean absolute error (MAE) of 0.022 on the test set. Lastly, the PSO algorithm is employed to search the latent space for optimal IL structures with the highest predicted solubility. A total of 5120 ILs are generated and optimized through 10 parallel runs of PSO. Their CO₂ solubilities are predicted and compared to those of the 3735 ILs combined with the already-known cations and anions in the CO₂ solubility database under 298.15 K and 100 kPa. The results demonstrate a notably larger distribution of higher CO₂ solubility in optimized ILs after PSO, which effectively points out the significance and directions for exploring the wide IL chemical space.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 3","pages":"Pages 335-343"},"PeriodicalIF":9.1,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116667","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":"A comprehensive study of affordable “water-in-salt” electrolytes and their properties","authors":"Aritsa Bunpheng ,&nbsp;Panwad Chavalekvirat ,&nbsp;Kanokporn Tangthana-umrung ,&nbsp;Varisara Deerattrakul ,&nbsp;Khanin Nueangnoraj ,&nbsp;Wisit Hirunpinyopas ,&nbsp;Pawin Iamprasertkun","doi":"10.1016/j.gce.2024.06.004","DOIUrl":"10.1016/j.gce.2024.06.004","url":null,"abstract":"<div><div>The search for alternative electrolytes has been extremely topical in recent years with the “water-in-salt” electrolyte, especially, lithium bis(trifluoromethanesulfonyl) imide (LiTFSI) coming to the fore in the context of high-voltage electrolytes. However, “water-in-LiTFSI” exhibits ultra-high cost and low ionic transport when compared with the aqueous lithium-halide, -nitrate as well as -sulphate salts (quoted as LiX). This work rediscovered the properties of a “water-in-salt” (LiX electrolytes) made from a variety of concentration from 1 m to saturated conditions. The changes of physical properties <em>e.g.</em>, viscosity, pH, conductivity, density, and temperature during mixing were then reported. The electrochemical properties of electrolyte were tested using carbon-based materials (YEC-8A) as a model system (three electrode configuration), and the finding was then expanded to a coin cell supercapacitor for benchmarking the performance per cost unit. It has been found that the use of highly concentrated LiX electrolytes does not always enhance the potential window. LiBr and LiI shown the redox properties while increasing the concentration can speed up the redox process (voltage remains unchanged). Using superconcentrated LiCl can slightly expand the potential window; however, corrosion is the main task to be addressed. Besides, voltage expansion of LiNO₃ is found to be approximately 2.2 V, which is comparable to LiTFSI. The breakdown cost of the electrolyte also shows that LiTFSI exhibits the lowest energy density per cost unit (dollars), while LiNO₃ provides the most feasible cost in term of power density. We then marked that the electrolytes such as LiBr and LiI can be used as redox additive electrolytes. This work also shows the fundamental insight into the physical and electrochemical properties of LiX for possible alternative use as a cheap “water-in-salt” electrolyte in energy storage apart from LiTFSI.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 1","pages":"Pages 126-135"},"PeriodicalIF":9.1,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704746","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}

{"title":"Developing deep learning-based large-scale organic reaction classification model via sigma-profiles","authors":"Wenlong Wang ,&nbsp;Chenyang Xu ,&nbsp;Jian Du ,&nbsp;Lei Zhang","doi":"10.1016/j.gce.2024.06.003","DOIUrl":"10.1016/j.gce.2024.06.003","url":null,"abstract":"<div><div>Advanced technologies like deep learning have accelerated the discovery of novel chemical reactions, especially in the field of organic synthesis. With hundreds of thousands of reactions available for reference, one way to effectively leverage them is by classifying chemical reactions into different clusters based on their specific characteristics, which makes target-guided navigation in the vast chemical space possible. Although previous attempts that apply deep learning to reaction classification tasks have made substantial progress, developing a model with good interpretability as well as high accuracy for large-scale reaction classification tasks remains an open question. In this work, a deep learning-based model for a large-scale reaction classification task is first constructed by utilizing pre-trained BERT and autoencoder. Then, the model is trained under the open-source dataset USPTO_TPL which contains recorded reactions of up to 1000 different types. The multi-classification accuracy of the model on the testing dataset is 99.382%, showing its great potential for practical use. Besides, a reaction similarity map is presented to correlate the reactions in the USPTO_TPL dataset based on their sigma-profile-based statistical features. Finally, representative reactions from the testing dataset are provided to illustrate the model's effectiveness on the reaction classification task.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 181-192"},"PeriodicalIF":9.1,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141410117","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}

{"title":"Integration of physical information and reaction mechanism data for surrogate prediction model and multi-objective optimization of glycolic acid production","authors":"Zhibo Zhang ,&nbsp;Yaowei Wang ,&nbsp;Dongrui Zhang ,&nbsp;Deming Zhao ,&nbsp;Huibin Shi ,&nbsp;Hao Yan ,&nbsp;Xin Zhou ,&nbsp;Xiang Feng ,&nbsp;Chaohe Yang","doi":"10.1016/j.gce.2024.06.002","DOIUrl":"10.1016/j.gce.2024.06.002","url":null,"abstract":"<div><div>With the continuous development of the chemical industry, the concept of advocating green development has become increasingly popular. Glycolic acid (GA), serving as the monomer for biodegradable plastic polyglycolic acid, plays a crucial role in combating plastic pollution and fostering an eco-friendly society. The selective oxidation of ethylene glycol (EG) to produce GA represents a novel green production technology. Controlling reaction parameters to achieve multi-objective optimization of product distribution and direct CO₂ emissions is crucial for scaling up the process. With the advent of the big data era, the integration of the chemical industry with artificial intelligence to achieve engineering scale-up is an important trend. This study proposes a neural network model for production prediction and optimization. The model is trained using experimental data, reaction mechanism data, and physical information, enabling rapid prediction of GA production. After validating with 40% of experimental data and 16% of reaction mechanism data, the model's prediction error was within ±5%, and the linear correlation coefficient R² between the predicted values and actual values was 0.998. Furthermore, this study integrated a multi-objective optimization algorithm based on the model, enabling surrogate optimization of reaction parameters during production. After optimization, the direct CO₂ emissions were reduced by over 99% and overall greenhouse gas emissions were reduced by 4.6%. The research paradigm proposed in this research can offer guidance and technical support for the optimized operation of EG selective oxidation to GA.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 169-180"},"PeriodicalIF":9.1,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141391858","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}

{"title":"Progress in the design and performance evaluation of catalysts for low-temperature direct ammonia fuel cells","authors":"Zhongbin Gong ,&nbsp;Hao Wang ,&nbsp;Chenhao Li ,&nbsp;Qinqin Sang ,&nbsp;Ying Xie ,&nbsp;Xiaosa Zhang ,&nbsp;Yanrong Liu","doi":"10.1016/j.gce.2024.06.001","DOIUrl":"10.1016/j.gce.2024.06.001","url":null,"abstract":"<div><div>Ammonia, a hydrogen-rich and carbon-free energy carrier, possesses advantages such as high energy density and convenient liquefaction storage and serves as an optimal medium for hydrogen storage. Low-temperature direct ammonia fuel cells (DAFCs) represent a highly promising pathway for the efficient utilization of ammonia energy. However, the sluggish kinetics of the low-temperature ammonia oxidation reaction (AOR), requires high loading of platinum-group metals (PGMs) catalysts, and their poisoning significantly hampers the performance of DAFCs, thereby limiting their large-scale commercial application. Therefore, it is crucial to design efficient, cost-effective, and stable catalysts. In this work, a detailed review of recent research efforts aimed at elucidating the mechanism underlying the AOR is presented. Building on this knowledge base, progress in the design and synthesis of both PGM and PGM-free catalysts for the AOR is discussed, as well as membrane electrode assembly (MEA) preparation processes for DAFCs. Furthermore, the results of the performance evaluation of AOR catalysts in single-cell tests are summarized. Finally, based on our findings from this research area thus far, potential design strategies for AOR catalysts that can promote the rapid development of low temperatures DAFCs are proposed.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 1","pages":"Pages 54-67"},"PeriodicalIF":9.1,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141403872","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}

{"title":"Switching from deep eutectic solvents to deep eutectic systems for natural product extraction","authors":"Zhaoyang Wang,&nbsp;Simin Wang,&nbsp;Yuan Zhang,&nbsp;Wentao Bi","doi":"10.1016/j.gce.2024.05.002","DOIUrl":"10.1016/j.gce.2024.05.002","url":null,"abstract":"<div><div>This article presents a comprehensive overview of recent advancements in natural product extraction, focusing on the evolution from deep eutectic solvents (DESs) to deep eutectic systems (DESys) for extraction. DESs, known for their environmentally friendly properties, have become crucial in extracting various natural products from plants, including micromolecules, lignin, and polysaccharides. Research into the extraction mechanism reveals that target compounds typically form hydrogen bonds with DESs, effectively becoming part of the solvent system. This insight has led to the development of the DESys extraction method, where hydrogen bond acceptors (HBAs) and hydrogen bond donors (HBDs) are directly mixed with the sample to form a DESys containing the target compounds. The shift from DES-based extraction to DESys-based extraction introduces innovative approaches where target compounds are integral to the solvent system, allowing for one-step dissolution and extraction. This methodology eliminates the need for pre-prepared DESs, simplifying processes and enhancing extraction efficiency. Additionally, strategies for DESs recycling and reuse contribute to sustainability efforts, offering cost-effective and environmentally friendly extraction solutions. The expanding applications of DES-based and DESys-based natural product extraction in cosmetics, food, industry, and environmental fields highlight their promising development potential. By delineating the transition from DES-based to DESys-based extraction of natural products, this review offers valuable insights for advancing the practice of green chemical engineering.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 1","pages":"Pages 36-53"},"PeriodicalIF":9.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141278624","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}

{"title":"A size shrinkable dendrimer-lipid hybrid nanoassembly for reversing tumor drug resistance","authors":"Xuanrong Sun ,&nbsp;Tenghan Zhang ,&nbsp;Zhao Lou ,&nbsp;Yujie Zhou ,&nbsp;Yuteng Chu ,&nbsp;Dongfang Zhou ,&nbsp;Juhong Zhu ,&nbsp;Yue Cai ,&nbsp;Jie Shen","doi":"10.1016/j.gce.2024.05.001","DOIUrl":"10.1016/j.gce.2024.05.001","url":null,"abstract":"<div><div>Drug resistance is a major obstacle in tumor therapy. One effective approach to overcoming this issue is by improving the penetration of drugs into the lesions. Here, we report size shrinkable dendrimer-lipid hybrid nanoassemblies (PATU-lipid-PEG/DOX). The PATU-lipid-PEG/DOX have initial sizes of ∼92 nm, which are ideal for blood circulation and tumor vascular penetration. Once PATU-lipid-PEG/DOX at tumor sites, they will disassemble and release small dendrimers (∼3 nm) to realize deep tumor penetration. As a result, Doxorubicin (DOX) can be delivered intracellularly, thereby reversing tumor multidrug resistance. The efficacy of PATU-lipid-PEG/DOX was validated in drug-resistant tumor mice. This study provides a versatile drug delivery platform to address the challenges of tumor drug resistance.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 1","pages":"Pages 116-125"},"PeriodicalIF":9.1,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141132284","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}

{"title":"Machine learning-assisted prediction and optimization of solid oxide electrolysis cell for green hydrogen production","authors":"Qingchun Yang ,&nbsp;Lei Zhao ,&nbsp;Jingxuan Xiao ,&nbsp;Rongdong Wen ,&nbsp;Fu Zhang ,&nbsp;Dawei Zhang","doi":"10.1016/j.gce.2024.04.004","DOIUrl":"10.1016/j.gce.2024.04.004","url":null,"abstract":"<div><div>The solid oxide electrolysis cell (SOEC) holds great promise to efficiently convert renewable energy into hydrogen. However, traditional modeling methods are limited to a specific or reported SOEC system. Therefore, four machine learning models are developed to predict the performance of SOEC processes of various types, operating parameters, and feed conditions. The impact of these features on the SOEC's outputs is explained by the Shapley additive explanations and partial dependency plot analyses. The preferred model is integrated with a genetic algorithm to determine the optimal values of each input feature. Results show the improved extreme gradient enhanced regression (XGBoost) algorithm is the core of the machine learning model of the process since it has the highest R² (&gt; 0.95) in the three outputs. The electrolytic cell descriptors have a greater impact on the system performance, contributing up to 54.5%. The effective area, voltage, and temperature are the three most influential factors in the SOEC system, contributing 21.6%, 16.6%, and 13.0% to its performance. High temperature, high pressure, and low effective area are the most favorable conditions for H₂ production rate. After conducting multi-objective optimization, the optimal current intensity and hydrogen production rate were determined to be 1.61 A/cm² and 1.174 L/(h·cm²).</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 2","pages":"Pages 154-168"},"PeriodicalIF":9.1,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141036786","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}

{"title":"Tailored SrFeO3-δ for chemical looping dry reforming of methane","authors":"Ao Zhu,&nbsp;Dongfang Li,&nbsp;Tao Zhu,&nbsp;Xing Zhu","doi":"10.1016/j.gce.2024.04.003","DOIUrl":"10.1016/j.gce.2024.04.003","url":null,"abstract":"<div><div>Chemical looping dry reforming of methane (CL-DRM) is a highly efficient process that converts two major greenhouse gases (CH₄ and CO₂) into syngas ready for the feedstock of liquid fuel production. One of the major obstacles facing this technology now is creating oxygen carriers that are stable and reactive. We fabricated high-performance Sr_0.98Fe_0.7Co_0.3O_3-δ perovskite-structured oxygen carrier by combining A-site defects and B-site doping of SrFeO_3-δ. During isothermal CL-DRM tests at 850 °C, Sr_0.98Fe_0.7Co_0.3O_3-δ achieved 87% CH₄ conversion and 94% CO selectivity in the CH₄ partial oxidation reaction, followed by a syngas yield of 8.5 mmol/g, and CO yield of 4.2 mmol/g in CO₂ decomposition. A-site defect engineering of the perovskite creates abundant oxygen vacancies and enhances oxygen storage capacity (OSC). Co-doping of the B-site of Sr_0.98FeO_3-δ increases oxygen mobility and CH₄/CO₂ activation, resulting in high activity in the CL-DRM process. This methodology resulted in high ionic mobility and facilitated the rapid diffusion of oxygen in the bulk phase, thereby increasing the redox properties of SrFeO_3-δ. The oxygen carrier exhibits excellent structural stability and regeneration ability in successive redox cycles. This strategy offers a simple but very effective pathway to tailor OSC, oxygen mobility, and oxygen vacancies of perovskite-structured materials for chemical looping or redox-involved processes.</div></div>","PeriodicalId":66474,"journal":{"name":"Green Chemical Engineering","volume":"6 1","pages":"Pages 102-115"},"PeriodicalIF":9.1,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140786370","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}