Evidence map›Paper›PMID 39830401›Full record

ArticleNational science review2025

Monitoring C-C coupling in catalytic reactions via machine-learned infrared spectroscopy.

Li Yang, Zhicheng Zhao, Tongtong Yang, Donglai Zhou, Xiaoyu Yue, Xiyu Li, Yan Huang, Xijun Wang, Ruyun Zheng, Thomas Heine and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in National science review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Li YangInstitutes of Physical Science and Information Technology, School of Chemistry & Chemical Engineering; Engineering Research Center of Autonomous Unmanned System Technology (Ministry of Education), Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of Artificial Intelligence, Anhui University, Hefei 230601, China.
Zhicheng ZhaoInstitutes of Physical Science and Information Technology, School of Chemistry & Chemical Engineering; Engineering Research Center of Autonomous Unmanned System Technology (Ministry of Education), Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of Artificial Intelligence, Anhui University, Hefei 230601, China.
Tongtong YangKey Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230088, China.
Donglai ZhouKey Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230088, China.
Xiaoyu YueKey Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230088, China.
Xiyu LiKey Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230088, China.
Yan HuangKey Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230088, China.
Xijun WangKey Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230088, China.
Ruyun ZhengInstitutes of Physical Science and Information Technology, School of Chemistry & Chemical Engineering; Engineering Research Center of Autonomous Unmanned System Technology (Ministry of Education), Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of Artificial Intelligence, Anhui University, Hefei 230601, China.
Thomas HeineHelmholtz-Zentrum Dresden-Rossendorf, Dresden 01328, Germany.
Changyin SunInstitutes of Physical Science and Information Technology, School of Chemistry & Chemical Engineering; Engineering Research Center of Autonomous Unmanned System Technology (Ministry of Education), Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of Artificial Intelligence, Anhui University, Hefei 230601, China.
Jun JiangKey Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230088, China.
Sheng YeInstitutes of Physical Science and Information Technology, School of Chemistry & Chemical Engineering; Engineering Research Center of Autonomous Unmanned System Technology (Ministry of Education), Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, School of Artificial Intelligence, Anhui University, Hefei 230601, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tracking atomic structural evolution along chemical transformation pathways is essential for optimizing chemical transitions and enhancing control. However, molecule-level knowledge of structural rearrangements during chemical processes remains a great challenge. Here, we couple infrared spectroscopy as a non-invasive method to probe molecular transformations, with a machine-learned protocol to immediately map the spectroscopic fingerprints to atomistic structures. From the theoretical perspective, we demonstrate it here with the example of C-C coupling in catalytic reactions, elucidating various structural conformations along dynamic trajectories. Within the transferable application to the specific CO-CO dimerization reaction, the structural and energetic variations of the critical chemical species could be identified via infrared spectroscopy. This approach extends the power of spectroscopy from fingerprinting chemical configurations to using them for assigning dynamic structural information.

Indexed as

catalytic reactionchemical evolutiondynamic monitoringmachine learningspectroscopic descriptor

Identifiers

PMID39830401
PMCPMC11737394

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.