Bridging Fidelity and Synthesis Gaps in Metal-Organic Framework Environmental Sensing via Artificial Intelligence

Bridging Fidelity and Synthesis Gaps in Metal-Organic Framework Environmental Sensing via Artificial Intelligence | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Energy & Environmental Materials This is a preprint and has not been peer reviewed. Data may be preliminary. 13 May 2026 V1 Latest version Share on Bridging Fidelity and Synthesis Gaps in Metal-Organic Framework Environmental Sensing via Artificial Intelligence Authors : Yizhe Yang [email protected] , Xin Ren 0000-0002-2407-7905 [email protected] , Xiaoyue Duan [email protected] , Xiaonan Yang [email protected] , and Xuesong Zhao [email protected] Authors Info & Affiliations https://doi.org/10.22541/authorea.15003283/v1 Under Review Energy & Environmental Materials Peer review timeline 32 views 23 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The deployment of Metal-Organic Framework (MOF) sensors in real-world environmental monitoring is currently restricted by the "fidelity gap" caused by humidity instability and signal drift, as well as the "synthesis gap" between theoretical topology and experimental realizability. This review elucidates the paradigm shift from trial-and-error experimentation to data-driven precision sensing, establishing a closed-loop framework that integrates inverse material design, multi-scale simulation, and intelligent signal decoupling. Analysis reveals that Deep Learning architectures, unlike traditional linear regression, successfully decouple analyte responses from environmental noise by extracting high-dimensional latent features from physicochemical hysteresis loops and competitive adsorption kinetics. Furthermore, interpretable AI models (e.g., SHAP) demonstrate that local binding interactions (governed by metal nodes), rather than textural properties like surface area, serve as the dominant determinant for trace-level sensitivity, thereby correcting long-standing misconceptions in high-throughput screening. Additionally, integrating synthesis parameters into generative models is identified as the key academic law to ensure the crystallizability of computationally designed structures. Ultimately, a roadmap towards autonomous sensing ecosystems is proposed, where neuromorphic hardware and self-driving laboratories co-evolve to achieve real-time, ultra-trace pollutant monitoring through the seamless fusion of digital intelligence and material responsiveness. Information & Authors Information Version history V1 Version 1 13 May 2026 Peer review timeline Under Review Energy & Environmental Materials 12 May 2026 Review Complete 12 May 2026 Submission Checks Completed Collection Energy & Environmental Materials Keywords MOFs materials science environmental materials solar cells carbon materials nanotechnology photovoltaics materials science Metal-Organic Frameworks Deep Learning Environmental Sensing Inverse Design Intelligent Perception energy materials semiconductors solar cells light emitting materials materials science MOFs materials science environmental materials solar cells carbon materials nanotechnology photovoltaics materials science Authors Affiliations Yizhe Yang [email protected] Jilin Normal University, Siping, China View all articles by this author Xin Ren 0000-0002-2407-7905 [email protected] View all articles by this author Xiaoyue Duan [email protected] Jilin Normal University, Siping, China View all articles by this author Xiaonan Yang [email protected] Jilin Normal University, Siping, China View all articles by this author Xuesong Zhao [email protected] Jilin Normal University, Siping, China View all articles by this author Metrics & Citations Metrics Article Usage 32 views 23 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yizhe Yang, Xin Ren, Xiaoyue Duan, et al. Bridging Fidelity and Synthesis Gaps in Metal-Organic Framework Environmental Sensing via Artificial Intelligence. Authorea . 13 May 2026. DOI: https://doi.org/10.22541/authorea.15003283/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/authorea.15003283/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe572198fd452ad',t:'MTc3OTIxODI4Nw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();