Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1038/s42004-025-01819-1 http://scihub22266oqcxt.onion/10.1038/s42004-025-01819-1 41361584!?!41361584 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=41361584&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Commun+Chem 2025 ; ? (?): ? Nephropedia Template TP {{tp|p=41361584|t=2025. Design of active sites for amine-functionalized direct air capture materials using integrated high-throughput calculations and machine learning |pdf=|usr=}}{{41361584}} gab.com Text Design of active sites for amine-functionalized direct air capture materials using integrated high-throughput calculations and machine learning JO: Commun Chem http://www.kidney.de/pget.php?&tw=1&pmid=41361584 #FOAvip Direct air capture (DAC) of carbon dioxide is a critical technology for mitigating climate change, but current materials face limitations in efficiency and scalability. We discover novel active sites for DAC materials using a combined machine learning (ML) and high-throughput atomistic modeling approach. Our ML model accurately predicts high-quality, density functional theory-computed CO(2) binding enthalpies for a wide range of nitrogen-bearing moieties. Leveraging this model, we rapidly screen over 1.6 million binding sites from a comprehensive database of theoretically feasible molecules to identify binding sites with superior CO(2) binding properties. Additionally, we assess the feasibility of experimentally synthesizing these structures using established ML metrics, discovering nearly 2,500 novel binding sites. This set of binding sites may be used for targeted design of functionalized amine sorbents for multi-objective optimization strategies. Altogether, our high-fidelity database and ML framework represent a significant advancement in the rational development of scalable, cost-effective carbon dioxide capture technologies, offering a promising pathway to meet key targets in the global initiative to combat climate change. Twit Text FOAVip Design of active sites for amine-functionalized direct air capture materials using integrated high-throughput calculations and machine learning ????Commun Chem http://www.kidney.de/pget.php?&tw=1&pmid=41361584 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41361584 #FOAvip English Wikipedia <ref>{{Cite pmid|41361584}}</ref> Design of active sites for amine-functionalized direct air capture materials using integrated high-throughput calculations and machine learning #MMPMID41361584 Davis MC; Kort-Kamp WJM; Matanovic I; Zelenay P; Holby EF Commun Chem 2025[Dec]; ? (?): ? PMID41361584show ga Direct air capture (DAC) of carbon dioxide is a critical technology for mitigating climate change, but current materials face limitations in efficiency and scalability. We discover novel active sites for DAC materials using a combined machine learning (ML) and high-throughput atomistic modeling approach. Our ML model accurately predicts high-quality, density functional theory-computed CO(2) binding enthalpies for a wide range of nitrogen-bearing moieties. Leveraging this model, we rapidly screen over 1.6 million binding sites from a comprehensive database of theoretically feasible molecules to identify binding sites with superior CO(2) binding properties. Additionally, we assess the feasibility of experimentally synthesizing these structures using established ML metrics, discovering nearly 2,500 novel binding sites. This set of binding sites may be used for targeted design of functionalized amine sorbents for multi-objective optimization strategies. Altogether, our high-fidelity database and ML framework represent a significant advancement in the rational development of scalable, cost-effective carbon dioxide capture technologies, offering a promising pathway to meet key targets in the global initiative to combat climate change. ?Design of active sites for amine-functionalized direct air capture mat(epmc).pdf DeepDyve Pubget Overpricing