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Record W2967323505 · doi:10.1002/chem.201903064

Underevaluated Solvent Effects in Electrocatalytic CO<sub>2</sub> Reduction by Fe<sup>III</sup> Chloride Tetrakis(pentafluorophenyl)porphyrin

2019· article· en· W2967323505 on OpenAlexfundno aff
Bin Zhao, Haitao Lei, Ni Wang, Gelun Xu, Wei Zhang, Rui Cao

Bibliographic record

VenueChemistry - A European Journal · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesShaanxi Province Postdoctoral Science FoundationNational Natural Science Foundation of ChinaCanadian Institute for Theoretical Astrophysics
KeywordsPorphyrinBenzonitrileChemistryTetrahydrofuranPropylene carbonateDimethylformamideAcetonitrileSolventCatalysisInorganic chemistryChloridePhotochemistrySolvent effectsElectrocatalystOrganic chemistryPhysical chemistryElectrochemistryElectrode

Abstract

fetched live from OpenAlex

Abstract FeIII chloride tetrakis(pentafluorophenyl)porphyrin (1) is known to have poor electrocatalytic activity for the CO2‐to‐CO conversion in dimethylformamide. In this work, we re‐examined Fe porphyrin 1 as a CO2 reduction catalyst in various solvents. Our results show that 1 displays fairly high electrocatalytic CO2‐to‐CO activity in acetonitrile with a turnover frequency (TOF) up to 4.2×104 s−1. On the other hand, 1 shows a modest activity in propylene carbonate, and is inefficient to catalyze CO2 reduction in benzonitrile, dimethylformamide, and tetrahydrofuran. Several solvent effects were considered, but none of these effects alone can explain the observed large activity difference of 1 for CO2 reduction in these solvents. Based on the results, it is suggested that more care should be paid when comparing different CO2 reduction catalysts because solvent effects are significant and are underevaluated.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.008
GPT teacher head0.223
Teacher spread0.216 · 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

Citations39
Published2019
Admission routes1
Has abstractyes

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Same venueChemistry - A European JournalSame topicCO2 Reduction Techniques and CatalystsFrench-language works237,207