Evidence map›Paper›PMID 41058976›Full record

ReviewThe journal of physical chemistry. C, Nanomaterials and interfaces2025

Computational Electrosynthesis: A Perspective on Mechanistic Questions, Methodological Approaches, and Elucidating the Role of the Electrical Double Layer.

John H Hymel, Chloe A Renfro, Shahriar N Khan, John P Pederson, Jesse G McDaniel

Abstract readReview
In one paragraph

Review in The journal of physical chemistry. C, Nanomaterials and interfaces, 2025. 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.

John H HymelSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States.ORCID https://orcid.org/0000-0002-4917-7865
Chloe A RenfroSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States.
Shahriar N KhanSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States.
John P PedersonSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States.ORCID https://orcid.org/0000-0001-6718-3823
Jesse G McDanielSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States.ORCID https://orcid.org/0000-0002-9211-1108

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this perspective, we discuss how modern computational chemistry techniques are utilized to elucidate electrosynthesis reaction mechanisms via the computation of kinetic rate parameters that depend on reaction conditions. The specific focus is on elementary reaction steps that occur within the electrical double layer (EDL) environment at the working electrode/electrolyte interface, for which the governing free energy profiles and rate constants can be explicitly computed from classical molecular dynamics and hybrid quantum mechanics/molecular mechanics (QM/MM) computer simulations with atomistic resolution of the EDL environment. We focus on direct electrolysis reactions at inert electrodes with prototypical "E-C-E" mechanisms, in which the reaction yield and selectivity may depend on whether the full process occurs heterogeneously within the EDL, or rather the steps following the initial electron transfer proceed homogeneously with the second electron transfer occurring via disproportionation (DISP pathway). For short-lived radical intermediates with lifetimes of ∼10

Identifiers

PMID41058976
PMCPMC12498507

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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.