2026-10-11T10:16:59Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33745625 2026-09-14T15:05:36Z category_146 category_873 category_734 category_931 category_64 portal_63 item_type_3 month_year_09_2026

Knowledge-Guided Autonomous Discovery of Microenvironment-Tuned Metal–Organic Framework Photocatalysts Yiming Zhao (348349) Tao Song (130854) Linjiang Chen (1370121) Yan Huang (46805) Kang Sun (630771) Wentao Han (12411844) Mingyang Shen (9131639) Chenwei Mao (24889634) Peng Lan (5699819) Meng Zhou (105903) Weiwei Shang (10664974) Jun Jiang (149215) Hai-Long Jiang (691928) Space Science Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer time experimental feedback quinolinecarboxylic acid ), optimally illuminated reactor fused three domains faster charge separation catalyst reached 12 across six closed accelerate targeted discovery 48 mmol g 33 mmol g guided autonomous discovery coupled electron transfer catalysis yet difficult x </ b refine microenvironment designs 30 </ b guided knowledge fusion 2 </ sub guided large proton transfer mimetic catalysis delivered 2 ∼ 36 system mined sphere microenvironments search converged results establish reduced proton reduced impedance rank order promote proton principles calculations practical microenvironment postsynthetic modification porous solids placing basic photoluminescence quenching parent material mof photocatalysts loop iterations library ’ generating 31 functional materials fold improvement enhanced photocurrent engineering strategy couples literature bond networks automated platform Designing second-sphere microenvironments that promote proton-coupled electron transfer is central to catalysis yet difficult to achieve in porous solids, such as metal–organic frameworks (MOFs). Here, we report an end-to-end workflow that couples literature-guided large-language-model (LLM) reasoning with real-time experimental feedback to propose, test, and refine microenvironment designs in MOF photocatalysts. The system mined and fused three domains (namely, photocatalytic H₂ production, hydrogenases and enzyme-mimetic catalysis) and deduced the hypothesis that placing basic, hydrogen-bonding groups near catalytic centers would facilitate water activation and proton transfer. The hypothesis was instantiated by postsynthetic modification of UiO-67, generating 31 <b>Pt@UiO-67-X</b> variants and evaluating them across six closed-loop iterations on an automated platform. The search converged on <b>Pt@UiO-67-30</b> (8-quinolinecarboxylic acid), which delivered 2.33 mmol g^–1 h^–1, a ∼36-fold improvement over the parent material; in a larger, optimally illuminated reactor the same catalyst reached 12.48 mmol g^–1 h^–1 while preserving the library’s rank order. Photoluminescence quenching, enhanced photocurrent, and reduced impedance are consistent with faster charge separation, and first-principles calculations are consistent with reduced proton-transfer barriers via N···H hydrogen-bond networks. These results establish a practical microenvironment-engineering strategy in MOFs and show how LLM-guided knowledge fusion with experiment-in-the-loop reasoning can systematize and accelerate targeted discovery of functional materials. 2026-09-14T00:00:00Z Dataset Dataset 10.1021/jacs.6c13252.s003 https://figshare.com/articles/dataset/Knowledge-Guided_Autonomous_Discovery_of_Microenvironment-Tuned_Metal_Organic_Framework_Photocatalysts/33745625 CC BY-NC 4.0