Evidence map›Paper›PMID 42265377›Full record

ArticleCommunications engineering2026

A machine learning perspective on three decades of methanol synthesis: research framework and experimental operation insights.

Mo Yan, Chengxi Yao, Shihao Wu, Muhammad Asif, Nuning Anugrah Putri Namari, Joohwan Ha, Ziyang Wang, Jaewon Lee, Dayoung Yu, Seokchan Lee and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications engineering, 2026. 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
–field-weighted citation impact
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

14 authors.

Mo Yan *Institute for Carbon-Neutral Energy Research (I²CNER), Kyushu University, Fukuoka, Japan.ORCID http://orcid.org/0009-0008-4465-7342
Chengxi Yao *School of Mechanical Engineering, Sungkyunkwan University, Suwon, Gyeong Gi-Do, South Korea.
Shihao WuInstitute for Advanced Ceramics, School of Materials Science and Engineering, Harbin Institute of Technology, Harbin, China.
Muhammad AsifDepartment of Environmental Sciences, COMSATS University, Islamabad Abbottabad Campus, Islamabad, Pakistan.
Nuning Anugrah Putri NamariInstitute for Carbon-Neutral Energy Research (I²CNER), Kyushu University, Fukuoka, Japan.
Joohwan HaDepartment of Semiconductor Convergence Engineering, Sungkyunkwan University, Suwon, Gyeong Gi-Do, South Korea.
Ziyang WangSchool of Mechanical Engineering, Sungkyunkwan University, Suwon, Gyeong Gi-Do, South Korea.
Jaewon LeeSchool of Mechanical Engineering, Sungkyunkwan University, Suwon, Gyeong Gi-Do, South Korea.
Dayoung YuSKKU Advanced Institute of Nanotechnology, Sungkyunkwan University, Suwon, Gyeong Gi-Do, South Korea.
Seokchan LeeSKKU Advanced Institute of Nanotechnology, Sungkyunkwan University, Suwon, Gyeong Gi-Do, South Korea.
Seungjae LeeSchool of Mechanical Engineering, Sungkyunkwan University, Suwon, Gyeong Gi-Do, South Korea.
Hao YuSchool of Engineering, The University of Edinburgh, Edinburgh, UK.
Xiaoming DuanInstitute for Advanced Ceramics, School of Materials Science and Engineering, Harbin Institute of Technology, Harbin, China. duanxiaoming@hit.edu.cn.ORCID http://orcid.org/0000-0002-8723-8286
Taesung KimSchool of Mechanical Engineering, Sungkyunkwan University, Suwon, Gyeong Gi-Do, South Korea. tkim@skku.edu.ORCID http://orcid.org/0000-0001-6280-7668

Funding

National Research Foundation of Korea (NRF) 2022R1A3B1078163
6 · The paper itself

Abstract

Heterogeneous catalysis optimization relies on the coordinated selection of catalyst composition, synthesis procedure, and reaction conditions, yet the empirical links between these variables and catalytic performance remain scattered across individual studies. Here, we show that an interpretable machine learning (ML) framework can decode this complexity by integrating disparate literature data. We curate, to our knowledge, the first unified dataset from 200 papers spanning three decades of CO

Identifiers

PMID42265377
PMCPMC13594224

What OpenQuestion holds

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Registered trials

None linked

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.