Evidence map›Paper›PMID 41332317›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

A Machine Learning-Driven Pore-Scale Network Model Coupling Reaction Kinetics and Interparticle Transport for Catalytic Process Design.

Ming-Liang Qu, Zhao-Bin Ding, Dingyue Zhang, Sajjad Foroughi, Huanhao Chen, Zi-Tao Yu, Jiyizhe Zhang, Lifeng Xiao, Martin J Blunt, Xiaolei Fan and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Ming-Liang QuState Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China.ORCID https://orcid.org/0000-0003-2071-8899
Zhao-Bin DingSchool of Chemical Engineering and Technology, Sun Yat-sen University, Zhuhai, 519028, China.
Dingyue ZhangState Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, Nanjing, 210009, China.
Sajjad ForoughiDepartment of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK.
Huanhao ChenState Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, Nanjing, 210009, China.
Zi-Tao YuState Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China.
Jiyizhe ZhangDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB3 0AS, UK.
Lifeng XiaoGreen Synenergy Co., No. 1299 Kehai Avenue, Shaoxing, 312000, China.
Martin J BluntDepartment of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK.
Xiaolei FanWenzhou Key Laboratory of Novel Optoelectronic and Nano Materials, Institute of Wenzhou, Zhejiang University, Wenzhou, 325006, China.ORCID https://orcid.org/0000-0002-9039-6736
Qingyang LinState Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China.

Funding

Institute of Wenzhou, Zhejiang University XMGL-KJZX-202204National Natural Science Foundation of China 52206119National Natural Science Foundation of China W2431016Wenzhou Municipal Science and Technology Bureau G20240011Wenzhou Municipal Science and Technology Bureau ZG2024051Zhejiang University Global Partnership Fund and the Wenzhou Municipal Science and Technology Bureau G20240011 ZG2024051
6 · The paper itself

Abstract

A pore-scale dual-network model is presented with kinetics (DNMK), enhanced by machine learning (ML), for efficient multiscale modeling of reaction-transport coupled catalytic processes in porous systems. In such systems, apparent catalytic performance arises from the intricate interplay between intrinsic microkinetics and inter-particle transport phenomena. By explicitly resolving these coupled effects, DNMK provides mechanistic insight into how spatial particle arrangements and transport limitations govern overall reactor performance. A key innovation of this work is the integration of ML-based surrogates to accelerate the microkinetic module, effectively bridging the large spatial and temporal scale mismatches between transport and catalytic reactions. This hybrid approach achieves up to a 750-fold computational speed-up while preserving full physical and chemical fidelity. The framework is demonstrated for sorption-enhanced CO

Indexed as

Catalytic processesCO2 hydrogenationKineticsMachine learningPore‐scale modelingPorous media

Identifiers

PMID41332317
PMCPMC12884819

What OpenQuestion holds

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Registered trials

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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.