Evidence map›Paper›PMID 39071063›Full record

ArticleACS central science2024

Nonidealities in CO

Joy S Zeng, Vineet Padia, Grace Y Chen, Joseph H Maalouf, Aditya M Limaye, Alexander H Liu, Michael A Yusov, Ian W Hunter, Karthish Manthiram

Abstract read
In one paragraph

Article in ACS central science, 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. 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

9 authors.

Joy S ZengDepartment of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.ORCID https://orcid.org/0000-0002-3443-3504
Vineet PadiaDepartment of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
Grace Y ChenDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.ORCID https://orcid.org/0000-0002-6899-5943
Joseph H MaaloufDepartment of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.ORCID https://orcid.org/0000-0003-0017-0387
Aditya M LimayeDepartment of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.ORCID https://orcid.org/0000-0003-0639-4154
Alexander H LiuDepartment of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
Michael A YusovDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.
Ian W HunterDepartment of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
Karthish ManthiramDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.ORCID https://orcid.org/0000-0001-9260-3391

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In electrocatalysis, mechanistic analysis of reaction rate data often relies on the linearization of relatively simple rate equations; this is the basis for typical Tafel and reactant order dependence analyses. However, for more complex reaction phenomena, such as surface coverage effects or mixed control, these common linearization strategies will yield incomplete or uninterpretable results. Cohesive kinetic analysis, which is often used in thermocatalysis and involves quantitative model fitting for data collected over a wide range of reaction conditions, requires more data but also provides a more robust strategy for interrogating reaction mechanisms. In this work, we report a robotic system that improves the experimental workflow for collecting electrochemical rate data by automating sequential testing of up to 10 electrochemical cells, where each cell can have a different electrode, electrolyte, gas-phase reactant composition, and applied voltage. We used this system to investigate the mechanism of carbon dioxide electroreduction to carbon monoxide at several immobilized metal tetrapyrroles. Specifically, at cobalt phthalocyanine (CoPc), cobalt tetraphenylporphyrin (CoTPP), and iron phthalocyanine (FePc), we see signatures of complex reaction mechanisms, where observed bicarbonate and CO

Identifiers

PMID39071063
PMCPMC11273456

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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.