Research [ASAP] Artificial Intelligence-Enhanced CO2 Capture Capacity Predictions of Functional Ionic Liquids Based on Group Contribution Descriptors LikeLiked Date: November 13, 2025 Less than 1 MinRead Views: 5 ACS Sustainable Chemistry & Engineering DOI: 10.1021/acssuschemeng.5c06870 Source: http://dx.doi.org/10.1021/acssuschemeng.5c06870 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 Relationship between R&D investment, financial development, energy use, and carbon dioxide emissions in USA: New insights from ARDL methodology Research December 24, 2025 Nam House / 90odesign Built Environment December 24, 2025 Development and systematic evaluation of aqueous triazole chloride-based deep eutectic solvents for efficient CO2 capture Research December 24, 2025 Accelerated biodegradation of polyurethanes through embedded cutinases Research December 24, 2025 Nebraska rancher urges expanded LRP to manage cattle market volatility Food & Agriculture December 24, 2025 Ranked: The 5 Largest Megacities in 2025, Based on Built-Up Area Finance & investments December 24, 2025 Céu Bar / Küster Brizola Arquitetos Built Environment December 24, 2025 Reducing the healthcare carbon footprint through fully self-service artificial intelligence-powered screening: A comparative simulation study utilizing data from Shanghai City and Lixin Town, China Research December 24, 2025 Visualizing the Relationship Between Country Size and GDP Finance & investments December 24, 2025 Podcast Studio Design Ideas for Creating a Comfortable and Professional Sound Food & Agriculture December 24, 2025 Load more
