Evidence map›Paper›PMID 38835935›Full record

ArticleThe journal of physical chemistry. C, Nanomaterials and interfaces2024

Improving Molecule-Metal Surface Reaction Networks Using the Meta-Generalized Gradient Approximation: CO

Yuxiang Cai, Roel Michiels, Federica De Luca, Erik Neyts, Xin Tu, Annemie Bogaerts, Nick Gerrits

Abstract read
In one paragraph

Article in The journal of physical chemistry. C, Nanomaterials and interfaces, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Yuxiang CaiResearch Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, Antwerp, Wilrijk BE-2610, Belgium.
Roel MichielsResearch Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, Antwerp, Wilrijk BE-2610, Belgium.
Federica De LucaResearch Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, Antwerp, Wilrijk BE-2610, Belgium.
Erik NeytsResearch Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, Antwerp, Wilrijk BE-2610, Belgium.ORCID https://orcid.org/0000-0002-3360-3196
Xin TuDepartment of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, U.K.ORCID https://orcid.org/0000-0002-6376-0897
Annemie BogaertsResearch Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, Antwerp, Wilrijk BE-2610, Belgium.ORCID https://orcid.org/0000-0001-9875-6460
Nick GerritsResearch Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, Antwerp, Wilrijk BE-2610, Belgium.ORCID https://orcid.org/0000-0001-5405-7860

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Density functional theory is widely used to gain insights into molecule-metal surface reaction networks, which is important for a better understanding of catalysis. However, it is well-known that generalized gradient approximation (GGA) density functionals (DFs), most often used for the study of reaction networks, struggle to correctly describe both gas-phase molecules and metal surfaces. Also, GGA DFs typically underestimate reaction barriers due to an underestimation of the self-interaction energy. Screened hybrid GGA DFs have been shown to reduce this problem but are currently intractable for wide usage. In this work, we use a more affordable meta-GGA (mGGA) DF in combination with a nonlocal correlation DF for the first time to study and gain new insights into a catalytically important surface reaction network, namely, CO

Identifiers

PMID38835935
PMCPMC11145648

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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.