Evidence map›Paper›PMID 42140992›Full record

ArticleNature communications2026

Machine-learning guided engineering of Mo

Tao Huang, Bingzhen Wang, Lina Yang, Quan Niu, Bingsuo Zou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 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

5 authors.

Tao HuangGuangxi Key Lab of Processing for Non-ferrous Metals and Featured Materials, School of Resources, Environments and Materials, Guangxi University, Nanning, China.
Bingzhen WangSchool of Computer, Electronics and Information, Guangxi University, Nanning, China.
Lina YangSchool of Computer, Electronics and Information, Guangxi University, Nanning, China.
Quan NiuState Key Laboratory of Luminescent Materials and Devices, Institute of Polymer Optoelectronic Materials and Devices, School of Materials Science and Engineering, South China University of Technology, Guangzhou, China.ORCID http://orcid.org/0009-0007-3024-7222
Bingsuo ZouGuangxi Key Lab of Processing for Non-ferrous Metals and Featured Materials, School of Resources, Environments and Materials, Guangxi University, Nanning, China. zoubs@gxu.edu.cn.ORCID http://orcid.org/0000-0003-4561-4711

Funding

Natural Science Foundation of Guangxi Province (Guangxi Natural Science Foundation) 2025GXNSFDA02850007Science and Technology Department of Guangxi Zhuang Autonomous (Guangxi Science and Technology Department) AD2506907
6 · The paper itself

Abstract

Developing highly efficient lead-free near-infrared (NIR) phosphors with strong thermal stability is a key challenge in material design and optoelectronics applications. Here, a machine-learning (ML) guided co-doping strategy to construct a broadband NIR-emitting phosphor, Cs

Identifiers

PMID42140992
PMCPMC13377217

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.