Machine Learning-Assisted Screening of Non-Adjacent Dual-Atom Catalysts on N-Doped Graphene: Insights Into the Electron "Storage-Feedback" Mechanism for N

Abstract read

In one paragraph

Article 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

3 authors.

Cheng HeState Key Laboratory For Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi'an Jiaotong University, Xi'an, China.ORCID https://orcid.org/0000-0002-6612-5346

Zhi-Hao JiangState Key Laboratory For Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi'an Jiaotong University, Xi'an, China.

Wen-Xue ZhangSchool of Materials Science and Engineering, Chang'an University, Xi'an, China.ORCID https://orcid.org/0000-0002-9327-5761

Funding

Fundamental Research Funds for the Central Universities, CHD 300102315401Innovative Talents Promotion Program of Shaanxi Province 2024RS-CXTD-56Middle-aged and Young Scientific and Technological Innovation Leading Talents Program of Xi'an City 25ZQRC00011National Natural Science Foundation of China 52571016Natural Science Foundation of Shaanxi Province, China 2025JC-YBMS-376Natural Science Foundation of Shaanxi Province, China 2025JC-YBMS-496

6 · The paper itself

Abstract

Conventional dual-atom catalysts (DACs) for the nitrogen reduction reaction (NRR) primarily rely on adjacent metal sites to achieve synergistic effects. However, this configuration often suffers from inherent limitations such as steric hindrance, restricted active site density, and difficulty in finely tuning electronic interactions between metal centers, which collectively hinder catalytic performance under practical conditions. To address these challenges, we propose a novel class of bimetallic electrocatalysts featuring non-adjacent dual-atom sites anchored on N-doped graphene (denoted as TM

Indexed as

ammonia synthesisdensity functional theory calculationsmachine learningnitrogen reduction reactionnon‐adjacent dual‐atom catalysts

Identifiers

PMID42460576

DOI10.1002/smll.74649

PMCPMC13580305

What OpenQuestion holds

Textmetadata

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.