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Record W3127364710 · doi:10.1039/d0sc05402j

Investigating the innate selectivity issues of methane to methanol: consideration of an aqueous environment

2021· article· en· W3127364710 on OpenAlexfundno aff
Rhys J. Bunting, Peter S. Rice, Jillian M. Thompson, P. Hu

Bibliographic record

VenueChemical Science · 2021
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilQueen's UniversityQueen's University Belfast
KeywordsSelectivityMethaneMethanolAqueous solutionChemistryAqueous two-phase systemOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

molecular dynamics calculations. The water solvent is modelled explicity, with each water molecule given the same level of theory as the metal surface and surface species. Free energy profiles for these steps are generated by umbrella sampling. It is found that an aqueous environment has a considerable effect on the kinetics of the elementary steps yet has little effect on the methane/methanol selectivity-conversion limit. Despite this, we find that the aqueous phase promotes the C-OH pathway for methanol formation, which could enhance the selectivity for methanol formation over that of other oxygenates.

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 score0.071
Threshold uncertainty score0.183

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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

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