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Record W3002122215 · doi:10.1002/slct.201904580

Enhanced Electrochemical Reduction of CO <sub>2</sub> to CO upon Immobilization onto Carbon Nanotubes Using an Iron‐Porphyrin Dimer

2020· article· en· W3002122215 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

VenueChemistrySelect · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsPorphyrinCarbon nanotubeElectrochemistryAqueous solutionTetraphenylporphyrinCatalysisCarbon monoxideDimerMaterials scienceElectrocatalystElectrochemical reduction of carbon dioxideInorganic chemistryChemical engineeringChemistryPhotochemistryElectrodeNanotechnologyOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Electrochemical reduction of carbon dioxide (CO 2 ) is a viable solution for conversion of atmospheric CO 2 to value‐added materials such as carbon monoxide (CO). In this project, a new urea iron‐tetraphenylporphyrin‐dimer (Fe‐TPP‐Dimer) was synthesized and applied for electrocatalytic CO 2 reduction under both homogeneous and heterogeneous conditions to selectively reduce CO 2 to CO. Immobilization of the catalyst onto carbon nanotubes (CNTs) in aqueous solution resulted in remarkable enhancement of its electrocatalytic abilities, with exceptional turnover frequencies (10 s −1 ), high faradic efficiency (FE) of ∼90%, and a current density of 16 mA/cm 2 at −0.88 V vs. RHE. This project exhibits the importance of molecular design in accessing heterogeneous applications with CNTs.

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 categoriesMeta-epidemiology (narrow)
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 score0.003
Threshold uncertainty score1.000

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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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