Research [ASAP] A Hybrid Encoding-Enhanced Machine Learning and Multioptimization Framework for the Optimal Catalyst Design of the Methane-to-Ethylene Process LikeLiked Date: July 11, 2025 Less than 1 MinRead Views: 14 Industrial & Engineering Chemistry Research DOI: 10.1021/acs.iecr.5c00563 Source: http://dx.doi.org/10.1021/acs.iecr.5c00563 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 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 10 Best Automotive Paint Technologies That Are Changing Car Design in Dubai and Beyond in 2026 Built Environment February 25, 2026 [ASAP] Hollow Mesoporous Manganese Dioxide Nanoprobes for Hepatocellular Carcinoma Diagnosis and Therapy Research February 25, 2026 A tough Supreme Court hearing brings little clarity on Line 5 pipeline’s fate Energy 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 Load more
