Evidence map›Paper›PMID 41044945›Full record

ArticleFaraday discussions2026

Electrochemical synthesis of high entropy nanoparticles and the exploration of the Pd-Ag-Au composition space for the oxygen reduction reaction.

Menglong Liu, Divyansh Gautam, Christian M Clausen, Ahmad Tirmidzi, Gustav K H Wiberg, Jan Rossmeisl, Matthias Arenz

Abstract read
In one paragraph

Article in Faraday discussions, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

7 authors.

Menglong LiuDepartment of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Bern, 3012, Switzerland. matthias.arenz@unibe.ch.ORCID http://orcid.org/0000-0003-2826-6698
Divyansh GautamDepartment of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Bern, 3012, Switzerland. matthias.arenz@unibe.ch.ORCID http://orcid.org/0000-0001-7670-562X
Christian M ClausenCenter for High Entropy Alloy Catalysis (CHEAC), Department of Chemistry, University of Copenhagen, København Ø, 2100, Denmark.ORCID http://orcid.org/0000-0003-1207-5264
Ahmad TirmidziDepartment of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Bern, 3012, Switzerland. matthias.arenz@unibe.ch.ORCID http://orcid.org/0009-0007-7342-8154
Gustav K H WibergDepartment of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Bern, 3012, Switzerland. matthias.arenz@unibe.ch.ORCID http://orcid.org/0000-0003-1884-604X
Jan RossmeislCenter for High Entropy Alloy Catalysis (CHEAC), Department of Chemistry, University of Copenhagen, København Ø, 2100, Denmark.ORCID http://orcid.org/0000-0001-7749-6567
Matthias ArenzDepartment of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Bern, 3012, Switzerland. matthias.arenz@unibe.ch.ORCID http://orcid.org/0000-0001-9765-4315

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multi-metallic alloys such as high entropy alloys (HEAs) span an extensive compositional space, potentially offering materials with enhanced activity and stability for various catalytic reactions. However, experimentally identifying the optimal composition within this vast compositional space poses significant challenges. In this study, we present a medium-throughput approach to screen the composition-activity correlation of electrodeposited multi-metallic and HEA nanoparticles. We apply the approach for exploring the Pd-Ag-Au composition subspace for the alkaline Oxygen Reduction Reaction (ORR). The Pd-Ag-Au alloy nanoparticles were synthesized electrochemically, characterized and evaluated for the ORR using a rotating disk electrode (RDE) setup. From 107 individual measurements, a composition-activity correlation model was constructed using Gaussian Process Regression (GPR), pinpointing the optimal composition around Pd

Identifiers

PMID41044945
PMCPMC12495384

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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.