Evidence map›Paper›PMID 42502189›Full record

ReviewSmall (Weinheim an der Bergstrasse, Germany)2026

CO Oxidation Catalysis Decoded: An Electronic Structure Descriptor Perspective.

Zhan Cao, Xurui Li, Zixiang Xing, Qingtao Wang, Feng Feng, Chunshan Lu, Rubo Fang, Qunfeng Zhang, Xiaonian Li

Abstract readReview
In one paragraph

Review in Small (Weinheim an der Bergstrasse, Germany), 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

9 authors.

Zhan CaoState Key Laboratory of Green Chemical Synthesis and Conversion, Industrial Catalysis Institute, Zhejiang, University of Technology, Hangzhou, Zhejiang, China.
Xurui LiState Key Laboratory of Green Chemical Synthesis and Conversion, Industrial Catalysis Institute, Zhejiang, University of Technology, Hangzhou, Zhejiang, China.
Zixiang XingState Key Laboratory of Green Chemical Synthesis and Conversion, Industrial Catalysis Institute, Zhejiang, University of Technology, Hangzhou, Zhejiang, China.
Qingtao WangState Key Laboratory of Green Chemical Synthesis and Conversion, Industrial Catalysis Institute, Zhejiang, University of Technology, Hangzhou, Zhejiang, China.
Feng FengState Key Laboratory of Green Chemical Synthesis and Conversion, Industrial Catalysis Institute, Zhejiang, University of Technology, Hangzhou, Zhejiang, China.
Chunshan LuState Key Laboratory of Green Chemical Synthesis and Conversion, Industrial Catalysis Institute, Zhejiang, University of Technology, Hangzhou, Zhejiang, China.ORCID https://orcid.org/0000-0002-2485-3070
Rubo FangState Key Laboratory of Green Chemical Synthesis and Conversion, Industrial Catalysis Institute, Zhejiang, University of Technology, Hangzhou, Zhejiang, China.
Qunfeng ZhangState Key Laboratory of Green Chemical Synthesis and Conversion, Industrial Catalysis Institute, Zhejiang, University of Technology, Hangzhou, Zhejiang, China.
Xiaonian LiState Key Laboratory of Green Chemical Synthesis and Conversion, Industrial Catalysis Institute, Zhejiang, University of Technology, Hangzhou, Zhejiang, China.

Funding

National Key Research and Development Program of China 2021YFA1501801Natural Science Foundation of China 22078292 and 22008212Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and Informatization U20A20119Zhejiang Province Applied Basic Research Program 2026XMGD015
6 · The paper itself

Abstract

Carbon monoxide (CO) catalytic oxidation, as one of the most extensively studied model reactions, serves as a critical platform for understanding how electronic structures determine catalytic performance. Although descriptors such as CO adsorption energy are widely used to correlate catalytic activity, they are often closely tied to specific surface structures or material types, making it difficult to establish a universal framework. In contrast, electronic structure descriptors, derived directly from the intrinsic electronic properties of atoms, offer the potential for constructing a unified understanding. This review systematically categorizes four key electronic structure descriptor systems: energy-level structure, d-band structure, charge and valence state distribution, and spin structure - progressively revealing their inherent hierarchical relationships across energy-level distribution, orbital hybridization, charge population, and electron spin, as well as their decisive roles in determining the reaction pathways of CO oxidation. In addition, this paper expands the scope of descriptors and explores the construction pathways for dynamic electronic structure descriptors as well as multiscale descriptors. By employing physics-based descriptors and interpretable machine learning models, it is possible to overcome the limitations of traditional performance prediction and lay the foundation for establishing causal models that encompass dynamic evolution and cross-scale coupling processes.

Indexed as

carbon monoxide oxidationd‐band structureelectronic‐structure descriptorsmachine learning assistancespin polarizationstructure‐activity relationship

Identifiers

PMID42502189
PMCPMC13549076

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.