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Record W2507573444 · doi:10.1021/acscatal.5b00185

Direct Hydrogenation of Carbon Dioxide by an Artificial Reductase Obtained by Substituting Rhodium for Zinc in the Carbonic Anhydrase Catalytic Center. A Mechanistic Study

2015· article· en· W2507573444 on OpenAlexafffund
Paolo Piazzetta, Tiziana Marino, Nino Russo, Dennis R. Salahub

Bibliographic record

VenueACS Catalysis · 2015
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsUniversity of Calgary
FundersSeventh Framework ProgrammeNatural Sciences and Engineering Research Council of CanadaEuropean Social FundRegione CalabriaEuropean Commission
KeywordsChemistryCarbonic anhydraseRhodiumCatalysisFormic acidZincMetathesisTurnover numberCombinatorial chemistryCatalytic cycleOrganic chemistryEnzyme

Abstract

fetched live from OpenAlex

Recently, a new artificial carbonic anhydrase enzyme in which the native zinc cation has been replaced with a Rh(I) has been proposed as a new reductase that is able to efficiently catalyze the hydrogenation of olefins. In this paper, we propose the possible use of this modified enzyme in the direct hydrogenation of carbon dioxide. In our theoretical investigation, we have considered different reaction mechanisms such as reductive elimination and σ-bond metathesis. In addition, the release of the formic acid and the restoring of the catalytic cycle have also been studied. Results show that the σ-bond metathesis potential energy surface lies below the reactant species. The rate-determining step is the release of the product with an energy barrier of 12.8 kcal mol –1 . On the basis of our results, we conclude that this artificial enzyme can efficiently catalyze the conversion of CO 2 to HCOOH by a direct hydrogenation reaction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.024
GPT teacher head0.272
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations29
Published2015
Admission routes2
Has abstractyes

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