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Record W2968917598 · doi:10.1038/s41467-019-11542-w

CO2 electrochemical catalytic reduction with a highly active cobalt phthalocyanine

2019· article· en· W2968917598 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of British Columbia
FundersSorbonne UniversitéInstitut Universitaire de FranceChina Scholarship CouncilAgence Nationale de la Recherche
KeywordsSelectivityPhthalocyanineCatalysisCobaltNanomaterial-based catalystElectrochemistryMaterials scienceInorganic chemistryNoble metalChemical engineeringChemistryElectrodeNanotechnologyOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Molecular catalysts that combine high product selectivity and high current density for CO 2 electrochemical reduction to CO or other chemical feedstocks are urgently needed. While earth-abundant metal-based molecular electrocatalysts with high selectivity for CO 2 to CO conversion are known, they are characterized by current densities that are significantly lower than those obtained with solid-state metal materials. Here, we report that a cobalt phthalocyanine bearing a trimethyl ammonium group appended to the phthalocyanine macrocycle is capable of reducing CO 2 to CO in water with high activity over a broad pH range from 4 to 14. In a flow cell configuration operating in basic conditions, CO production occurs with excellent selectivity (ca. 95%), and good stability with a maximum partial current density of 165 mA cm −2 (at −0.92 V vs. RHE), matching the most active noble metal-based nanocatalysts. These results represent state-of-the-art performance for electrolytic carbon dioxide reduction by a molecular catalyst.

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.

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 score0.463
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.264
Teacher spread0.256 · 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