Green Chemistry最新文献

Lithium-ion batteries (LIBs) are widely used in the mobile electronics, power, energy storage and other fields due to their excellent electrochemical performance, but their limited service life has resulted in a large number of spent LIBs being discarded. Due to the advantages of high recovery efficiency and mild reaction conditions, the combined pyro-hydrometallurgical process for recovering valuable metal elements from spent LIBs is emerging in line with the principles of green chemistry and has potential for large-scale industrial applications. Here we review current developments in the combined recovery process, aiming to figure out the challenges and future directions for the combined process. In detail, thermal pretreatment methods for collecting the cathode material from spent LIBs, the combined recovery process for treating the cathode material, and the subsequent separation and extraction process are summarized. Furthermore, the practical application of combined recycling schemes is demonstrated. Finally, the development and challenges of the combined process in recycling spent LIBs are revealed. Achieving pollution-free emissions and high-value utilization of spent LIB resources with low-cost treatment are future directions for the combined process.

Microbial cell separation and recycling have become the major major high-cost procedures in commercial fermentation biotechnology, especially for the difficult-to-cultivate strains. In this regard, high-speed centrifugation is a significant industrial operation for bacterial separation but at the cost of high-end equipment and energy consumption. Therefore, the present study proposes a novel resin particle assisted method to facilitate the centrifugal separation and recycling of microbial cells from fermentation broths, which significantly reduces the centrifuge force of three representative microbial cells, yeast, Gram-positive bacteria and Gram-negative bacteria, by 26% to 36%. In particular, the resin assisted centrifugation successfully achieves an efficient separation at 563g for the small size bacteria of Gluconobacter oxydans. By comparison with glass or steel particles, the mechanism of resin-assisting cell sedimentation was analyzed from the aspects of resin granularity, porosity, charged groups and the isoelectric point of bacterial cells. The interaction model was therefore hypothesized for the resin particle with microbial cells. In addition, efficient separation was easily realized for cell reuse and resin recovery by a simple operation of fresh fermentation medium injection. The resin assisting strategy provides a simple and green technological approach for the separation and recycling of small bacterial cells in the fermentation and biotechnological industry.

Third-generation biodiesel produced using carbon-neutral algal feedstock is a promising alternative to meet global energy demands. However, the economic viability of algae-derived biodiesel is severely impacted by poor lipid recovery and taxing downstream processes. In this regard, green Fenton chemistry was employed to disrupt algal cells in a bio-electro-Fenton-assisted photosynthetic microbial fuel cell (BEF-PMFC) by employing different Fenton catalysts for higher lipid recovery. The maximum lipid yield of 39.2% with 98% chlorophyll removal was achieved by homogeneous Fenton oxidation in a Ni–Pd/C catalysed BEF-PMFC after 6 h of reaction at a pH of 3.0, whereas a comparable lipid yield (37.5%) and chlorophyll removal (95%) were attained by a CoFe-AC-driven heterogeneous Fenton oxidation process. Experiments exhibited a maximum of 90% lipid extraction efficiency, which was 1.5-fold higher than that without cell-disruptive wet biomass. Finally, biodiesel synthesised from lipids obtained via BEF conformed to the ASTM D6751-12 standard. The PMFC equipped with the Ni–Pd/C coated cathode generated a maximum power density of 74.5 mW m⁻² and a chemical oxygen demand removal efficiency of 89.2%, which were ca. 2.8 times and 1.2 times higher compared to the control PMFC operated without any catalyst on the cathode. Thus, this investigation paves the way for using a green chemistry-based strategy to assist PMFCs in achieving higher recovery of bioelectricity and lipid recovery with minimal reliance on chemicals.

Rapid prediction of environmental chemistry properties is critical for the green and sustainable development of the chemical industry and drug discovery. Machine learning methods can be applied to learn the relations between chemical structures and their environmental impact. Graph machine learning, by learning the representations directly from molecular graphs, may have better predictive power than conventional feature-based models. In this work, we leveraged graph neural networks to predict the environmental chemistry properties of molecules. To systematically evaluate the model performance, we selected a representative list of datasets, ranging from solubility to reactivity, and compared them directly to commonly used methods. We found that the graph model achieved near state-of-the-art accuracy for all tasks and, for several, improved the accuracy by a large margin over conventional models that rely on human-designed chemical features. This demonstrates that graph machine learning can be a powerful tool to perform representation learning for environmental chemistry. Further, we compared the data efficiency of conventional feature-based models and graph neural networks, providing guidance for model selection dependent on the size of datasets and feature requirements.

The chemical conversion of carbon dioxide (CO₂) into high-value chemicals or fuels is exceedingly attractive due to its green and sustainable features. However, practical technologies on scale utilization of CO₂ are few, and nearly no new industrial processes on the topic have emerged over the years. The current bottlenecks, e.g., low efficiency and atom economy, seriously restrict the process development. In recent studies, the catalytic activation of CO₂ and/or substrate has been revealed to play a significant role in the promotion of CO₂ functionalization to valuable chemicals, including the representative reactions of epoxides/propargyl alcohols/propargylamines with CO₂, multicomponent cascade reactions, N-formylation of amines with CO₂ and hydrosilanes, and unactivated C–H bond carboxylation. Herein, recent significant advances (2017–2022) on the effective chemical fixation of CO₂ through molecular activation or synergistic activation strategies in homogeneous systems are presented. The superiority of molecular activation in thermochemical catalysis is shown in a wide range of CO₂ transformations. Through CO₂/substrate activation and catalysis with well-developed metal or organocatalysts, valuable chemicals are successfully attained with great efficiency. The new progress will provide significant guidance to promote the effective and sustainable utilization of CO₂.

Conventional dihydroxylation of alkenes is one of the most powerful synthetic tools for delivering two hydroxyl groups at vicinal positions. The direct formation of 1,3-diols remains a formidable challenge, yet dihydroxyl groups are broadly present in bioactive compounds, and are currently only available synthetically via multiple steps. The oxidative ring-opening of arylcyclopropanes has been demonstrated to access various 1,3-difunctionalized chemicals, but no 1,3-diols have been directly synthesized owing to their inherently high proclivity to become further oxidized. Herein, we report a facile and efficient strategy to 1,3-diols involving controlled electrochemical C–C bond cleavage of arylcyclopropanes with H₂O as the ultimately green hydroxyl source. Moreover, this protocol stands out with its high atom economy, broad substrate scope and excellent functional group tolerance, and hence is amenable to the synthesis of complex natural products and drug derivatives.

Prussian blue analogues (PBAs) have been considered as promising host frameworks for charge carriers because of their well-defined diffusion channel along the 〈100〉 direction. Among PBA families, Berlin green (BG) would be an ideal cathode platform because the empty carrier ion sites and two redox couples (Fe^3+/2+–CN–Fe^3+/2+) in the BG framework can deliver high specific capacity during battery operation. Nonetheless, in most solution-based precipitation processes, BG crystals are synthesized in irregular shapes rather than in well-defined cube shapes, thus limiting their capacities at high rate operations. In this work, given the aforementioned challenges, a simple two-step precipitation process to synthesize cubic BG without using any chelating agents and toxic acids was reported. Notably, an intermediate phase was identified as an important stage in converting irregularly shaped BG to cubic BG by releasing crystal water molecules from the framework. Utilizing well-aligned 〈100〉 channels in the cubic framework, cubic BG exhibits excellent electrochemical properties as a cathode for lithium-ion batteries, delivering a specific capacity of 107.2 mA h g⁻¹ at a high current density of 500 mA g⁻¹. A combined study of in situ X-ray diffraction and X-ray absorption fine structure analyses would provide a comprehensive structure–property relationship of BG cathodes.

Aryl ethers greatly influence lignin depolymerization and the oxygen content in lignin products. Cleaving aryl ethers normally requires harsh conditions such as high-pressure hydrogen gas and elevated temperature. Herein, we developed a synergistic method that combines photocatalytic hydrogen transfer with acid catalysis for H₂-free hydrogenolysis of diphenyl ether and aromatic oxygenates at room temperature. The electron-enriched Pt/TiO₂ surface stored abundant hydrogen species under light irradiation and efficiently catalyzed hydrogen transfer from isopropanol to aryl ethers. The acid mediated the hydrogenation sequence into: hydrogenolysis of aryl C–O bonds > saturation of aryl rings ≫ hydrogenolysis of aliphatic C–O bonds. DFT calculations suggested the aryl ether bond adsorbed on the Pt surface was weakened through protonation. This method delivered 98% yield of aliphatic monomers (73% cyclohexane and 25% cyclohexanol) from cleavage of diphenyl ether, and converted aromatic mixtures into cycloalkanes (57%) and aliphatic alcohols (9%) under mild conditions.

Building a stronger bioeconomy requires production capabilities that are largely generated through microbial genetic engineering. Plant feedstocks can additionally be genetically engineered to generate desirable feedstock traits and provide precursors for direct microbial conversion into desired products. The oleaginous yeast Rhodosporidium toruloides is a promising organism for this type of conversion as it can grow on a wide range of deconstructed biomass and consume a variety of carbon sources. Here, we leveraged R. toruloides native p-coumaric acid consumption pathway to accumulate protocatechuate (PCA) from 4-hydroxybenzoate (4HBA) released from a sorghum feedstock line genetically engineered to overproduce 4HBA. We did so by generating and evaluating an R. toruloides strain that accumulates PCA, RSΔ12623. We then show that at two scales a cholinium lysinate pretreatment with enzymatic saccharification successfully extracts 95% of the 4HBA from the engineered sorghum biomass while producing deconstructed lignin that can be more efficiently depolymerized in a subsequent thermochemical reaction. We also demonstrate that strain RSΔ12623 can convert more than 95% of 4HBA to PCA while consuming >95% of the glucose and >80% of the xylose present in sorghum hydrolysates. Finally, to evaluate the scalability of such fermentations, we conducted the conversion of 4HBA to PCA in a 2 L bioreactor under controlled conditions. This work demonstrates the potential of purposefully producing aromatic precursors in planta that can be liberated during biomass deconstruction for direct microbial conversion to desirable bioproducts.

Indirect CO₂ hydrogenation to methanol and ethylene glycol is a green, efficient, and economical technique for converting CO₂ into high-value chemicals to address the intractable environmental crisis caused by CO₂ emissions. However, traditional methods for screening and optimizing catalysts in this process mainly depend on experience and repeated ‘trial-and-error’ experiments, which are resource-, time- and cost-consuming tasks. Therefore, this study developed a machine learning framework for predicting the conversion ratio of ethylene carbonate and the yield of methanol and ethylene glycol from the indirect CO₂ hydrogenation technology to accelerate the catalyst screening and optimization processes. The initial dataset was visualized by conducting principal component analysis and improved to ensure sufficient information variables for the machine learning model to distinguish between catalyst types. After comparing the optimized results of three algorithms, the neural network with two hidden layers is the core of the machine learning model of the indirect CO₂ hydrogenation process. It was then further optimized by a feature engineering method coupled with feature importance analysis and the Pearson correlation matrix. It indicates that the optimized neural network model has higher performance, especially in predicting ethylene carbonate conversion and product yields. Compared with other input features, the space velocity and hydrogen/ester ratio are the two most important factors affecting the conversion ratio of ethylene carbonate and the yield of methanol and ethylene glycol. Based on the results of the feature importance analysis, a multi-objective optimization model with a genetic algorithm was employed to screen the most suitable catalyst. Compared with other catalysts, more efforts should be devoted to the optimized xMoO_x–Cu/SiO₂ catalyst for the industrialization of indirect CO₂ hydrogenation technology after experimental verification.