Evidence map›Paper›PMID 38013904›Full record

ArticleDigital discovery2023

Density functional theory and machine learning for electrochemical square-scheme prediction: an application to quinone-type molecules relevant to redox flow batteries.

Arsalan Hashemi, Reza Khakpour, Amir Mahdian, Michael Busch, Pekka Peljo, Kari Laasonen

Abstract read
In one paragraph

Article in Digital discovery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Arsalan HashemiDepartment of Chemistry and Material Science, School of Chemical Engineering, Aalto University 02150 Espoo Finland arsalan.hashemi@aalto.fi.ORCID https://orcid.org/0000-0003-2699-5731
Reza KhakpourDepartment of Chemistry and Material Science, School of Chemical Engineering, Aalto University 02150 Espoo Finland arsalan.hashemi@aalto.fi.
Amir MahdianDepartment of Chemistry and Material Science, School of Chemical Engineering, Aalto University 02150 Espoo Finland arsalan.hashemi@aalto.fi.
Michael BuschInstitute of Theoretical Chemistry, Ulm University Albert-Einstein Allee 11 89069 Ulm Germany.
Pekka PeljoResearch Group of Battery Materials and Technologies, Department of Mechanical and Materials Engineering, Faculty of Technology, University of Turku 20014 Turun Yliopisto Finland.ORCID https://orcid.org/0000-0002-1229-2261
Kari LaasonenDepartment of Chemistry and Material Science, School of Chemical Engineering, Aalto University 02150 Espoo Finland arsalan.hashemi@aalto.fi.ORCID https://orcid.org/0000-0002-4419-7824

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proton-electron transfer (PET) reactions are rather common in chemistry and crucial in energy storage applications. How electrons and protons are involved or which mechanism dominates is strongly molecule and pH dependent. Quantum chemical methods can be used to assess redox potential (

Identifiers

PMID38013904
PMCPMC10561546

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.