Evidence map›Paper›PMID 42662641›Full record

ReviewACS omega2026

Latest Progress in Pd-Based Metallenes: From Formation Mechanism, Structural Design, Optimization Strategies to Electrocatalytic Applications.

Hongyan Zhang, Kaili Wang

Abstract readReview
In one paragraph

Review in ACS omega, 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

2 authors.

Hongyan ZhangSchool of Chemistry & Chemical Engineering and Environmental Engineering, Weifang University, Weifang 261061, China.
Kaili WangSchool of Chemistry & Chemical Engineering and Environmental Engineering, Weifang University, Weifang 261061, China.ORCID https://orcid.org/0000-0001-8192-2186

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, two-dimensional (2D) metallene nanomaterials have attracted widespread attention in the field of electrocatalysis, owing to their unique physical and chemical properties. Especially, palladium (Pd)-based metallenes, with their excellent conductivity, high specific surface area, unsaturated atom coordination environment, and tunable electronic structure, have emerged as promising electrocatalysts for green energy technologies such as fuel cells and hydrogen production via water electrolysis. In the review, it systematically elaborates on the recent research progress of Pd-based metallene electrocatalysts. At first, the formation mechanism of their 2D ultrathin sheet structure has been thoroughly analyzed from the perspectives of thermodynamics and kinetics. Second, the key synthesis methods, for example, wet-chemical, templating, and electrochemical synthesis, as well as the modification strategies developed for further optimizing performance, are summarized. Subsequently, based on their element composition and structural characteristics, they are classified into several categories: single-atom metallenes, intermetallic metallenes, high-entropy metallenes, and multimetallic metallenes; meanwhile, their structural advantages are discussed in detail. Finally, the review focuses on the application performance and catalytic mechanisms of Pd-based metallenes in fuel cell reactions such as the oxygen reduction reaction, formic acid oxidation reaction, ethanol oxidation reaction, hydrogen evolution reaction, and oxygen evolution reaction. This review offers a new perspective on the challenges and future development directions of 2D Pd-based metallenes for clean energy electrocatalysis.

Identifiers

PMID42662641
PMCPMC13520049

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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.