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Record W2885534103 · doi:10.1021/acs.jpcc.8b04751

Adsorption and Decomposition of Formic Acid on Cobalt(0001)

2018· article· en· W2885534103 on OpenAlexafffund
Jeffrey J. Sims, Cherif Aghiles Ould Hamou, Romain Réocreux, Carine Michel, Javier B. Giorgi

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche ScientifiqueUniversity of Ottawa
KeywordsFormic acidFormateCobaltChemistryInorganic chemistryDehydrogenationAdsorptionCatalysisDecompositionSelectivityPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Formic acid can undergo dehydration or dehydrogenation with variable selectivity over a range of metal catalysts. The selectivity among these reactions depends on the reaction mechanism and reaction conditions pertinent on each surface. This work provides mechanistic insight into the decomposition of formic acid on cobalt at high- and low-temperature regimes. The adsorption and decomposition of formic acid on a Co(0001) single crystal was studied in ultrahigh vacuum by X-ray photoelectron spectroscopy and temperature-programmed desorption. Insight is provided using density functional theory calculations. In the low-temperature regime, formic acid adsorbs molecularly on the surface at 130 K. Partial decomposition produces CO at 140 K, and at 160 K, the decomposition of formic acid into formate, which is a thermodynamic sink, is dominant. Water can be formed at low temperature via bimolecular processes. At high temperature (>400 K), the similar barriers for decomposition of the formate species lead to the concomitant production of CO, CO 2, and H 2 . The correlation between experiment and theory provides a framework for the interpretation of surface species and reaction path operating in different regimes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.258
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicCarbon dioxide utilization in catalysisFrench-language works237,207