Research [ASAP] Machine Learning-Driven Optimization of Spent Lithium Iron Phosphate Regeneration LikeLiked Date: February 18, 2025 Less than 1 MinRead Views: 62 ACS Sustainable Chemistry & Engineering DOI: 10.1021/acssuschemeng.4c10415 Source: http://dx.doi.org/10.1021/acssuschemeng.4c10415 Tags:Materials Industry FacebookTwitterLinkedinWhatsAppTelegramEmail ALT-Lab-Ad-1ALT-Lab-Ad-2ALT-Lab-Ad-3ALT-Lab-Ad-4ALT-Lab-Ad-5ALT-Lab-Ad-6ALT-Lab-Ad-7ALT-Lab-Ad-8ALT-Lab-Ad-9ALT-Lab-Ad-10ALT-Lab-Ad-11ALT-Lab-Ad-12ALT-Lab-Ad-13 Recent Articles Hydrodynamic intensification of red soil microbial fuel cells: enhanced Acid Red 73 degradation and bioelectricity generation under free-fall influent Research February 26, 2026 Recovering acidic solid residues from wet chemical phosphorus recovery of sewage sludge ash in the production of cementitious composites Waste Management February 25, 2026 Solvent selection enables sustainable and affordable lignin biocomposite for cement-free construction Research February 25, 2026 Research February 25, 2026 Current-carrying-coil magnetic field influences methane production in anaerobic digestion of water hyacinth by modulating reactive oxygen species levels Research February 25, 2026 Breaking new ground in sustainability (SD): a novel integrated framework for eco/green-innovation (EI) and green supply chain management (GSCM) implementation in emerging economies –... Research February 25, 2026 [ASAP] Hollow Mesoporous Manganese Dioxide Nanoprobes for Hepatocellular Carcinoma Diagnosis and Therapy Research February 25, 2026 Prioritizing protection Food & Agriculture February 25, 2026 Activated carbon–embedded free-standing glucomannan membrane for efficient dye removal Research February 25, 2026 Assessing environmental and policy dimensions of LEED adoption in ready-made garments of Bangladesh: Drivers, barriers, and strategies for sustainable industrial green transformation Waste Management February 24, 2026 Load more
