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Record W4237335625 · doi:10.26434/chemrxiv.12660722

Reducing CO2 to HCO2- at Mild Potentials: Lessons from Formate Dehydrogenase

2020· preprint· en· W4237335625 on OpenAlexfundno aff
Jenny Y. Yang, Tyler Kerr, Xinran S. Wang, Jeffrey M. Barlow

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsnot available
FundersOffice of ScienceCanadian Institute for Advanced ResearchBasic Energy SciencesAlfred P. Sloan FoundationU.S. Department of Energy
KeywordsHeterolysisHydrideChemistryFormateCatalysisFormate dehydrogenaseElectron transferProtonMetalRedoxBond cleavageElectrochemistryCrystallographyInorganic chemistryPhotochemistryStereochemistryPhysical chemistryElectrodeOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

The catalytic reduction of CO<sub>2</sub> to HCO<sub>2</sub><sup>-</sup> requires a formal transfer of a hydride (two electrons, one proton). Synthetic approaches for inorganic molecular catalysts have exclusively relied on classic metal hydrides, where the proton and electrons originate from the metal (via heterolytic cleavage of an M-H bond). An analysis of the scaling relationships that exist in classic metal hydrides reveal that hydride donors sufficiently hydridic to perform CO<sub>2</sub> reduction are only accessible at very reducing electrochemical potentials, which is consistent with known synthetic electrocatalysts. By comparison, the formate dehydrogenase enzymes operate at relatively mild potentials. In contrast to reported synthetic catalysts, none of the major mechanistic proposals for hydride transfer in formate dehydrogenase proceed through a classic metal hydride. Instead, they invoke formal hydride transfer from an orthogonal or bi-directional mechanism, where the proton and electron are not co-located. We discuss the thermodynamic advantages of this approach for favoring CO<sub>2</sub> reduction at mild potentials, along with guidelines for replicating this strategy in synthetic systems.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.045
GPT teacher head0.295
Teacher spread0.250 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2020
Admission routes1
Has abstractyes

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