Evidence map›Paper›PMID 41053516›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Knowledge Graph for Methane Selective Conversion: Revisiting and Predicting Product Selectivity and Methane Conversion.

Boyu Xu, Gaoyang Li, Bohan Wang, Jiawei Bian, Hui Pan, Yulin Min, Guodong Qi, Jun Xu, Feng Deng, Feng Ju and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

12 authors.

Boyu XuDepartment of Information and Computing Sciences, Utrecht University, Princetonplein 5, Utrecht, 3584 CC, The Netherlands.
Gaoyang LiShanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai, 200090, China.
Bohan WangSchool of Materials Science and Engineering and East China University of Science and Technology - Utrecht University Joint Research Center for Sustainable and Circular Chemistry and Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, China.
Jiawei BianSchool of Chemical Engineering and East China University of Science and Technology - Utrecht University Joint Research Center for Sustainable and Circular Chemistry and Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, China.
Hui PanShanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai, 200090, China.ORCID https://orcid.org/0000-0002-5643-6546
Yulin MinShanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai, 200090, China.
Guodong QiNational Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, China.ORCID https://orcid.org/0000-0002-4762-7463
Jun XuNational Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, China.
Feng DengNational Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, China.
Feng JuSchool of Chemical Engineering and East China University of Science and Technology - Utrecht University Joint Research Center for Sustainable and Circular Chemistry and Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, China.
Hao LingSchool of Chemical Engineering and East China University of Science and Technology - Utrecht University Joint Research Center for Sustainable and Circular Chemistry and Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, China.
Zhendong WangState Key Laboratory of Green Chemical Engineering and Industrial Catalysis, Sinopec Shanghai Research Institute of Petrochemical Technology Co., Ltd., Shanghai, 201208, China.

Funding

National Key R&D Program of China 2022YFA1504500National Key R&D Program of China 2023YFA1509102National Natural Science Foundation of China 22272185National Natural Science Foundation of China 22378131National Natural Science Foundation of China 22478239Science and Technology Commission of Shanghai Municipality 19DZ2271100Shanghai Pujiang Programme 23PJD021Sinopec Foundation 224250Strategic Priority Research Program of the Chinese Academy of Sciences XDB0540000
6 · The paper itself

Abstract

Selective conversion of methane to carbon-based compounds is promising but currently limited by issues related to scale. Unraveling the interconnected relationships between products selectivity and methane conversion plays a pivotal role in understanding of this complex process. In this work, a knowledge graph (KG) is constructed for methane conversion with the help of a robust large language model basing on the literature reported methane conversion over different catalysts under various conditions. This KG, structured around 11 entity types and 32 relationship types, allows to effectively analyze advancements in methane conversion - including identifying optimal catalytic processes, evaluating reaction conditions, and tracking development trends. A deep neural network analysis of the KG highlighted catalysts with metal active sites and multifunctional supports as particularly effective for methanol production under conditions suitable for industrial-scale applications. These findings provide valuable insights for targeted catalyst development and industrial applications.

Indexed as

deep neural networksknowledge graphlarge language modelsmethane selective conversionpredicting catalytic performance

Identifiers

PMID41053516
PMCPMC12752571

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.