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Record W3025250053 · doi:10.1149/ma2020-01391758mtgabs

Combined CO<sub>2</sub>-Electroreduction Towards Formate Production and HMF Oxidation

2020· article· en· W3025250053 on OpenAlexaff
Roger Lin, Jiaxun Guo, Ali Seifitokaldani

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsPolytechnique MontréalMcGill University
Fundersnot available
KeywordsFaraday efficiencyOverpotentialFormateOxygen evolutionAnodeFormic acidChemistryInorganic chemistryElectrolysisElectrochemical reduction of carbon dioxideMaterials scienceChemical engineeringCatalysisElectrochemistryElectrodeOrganic chemistryCarbon monoxide

Abstract

fetched live from OpenAlex

Recent advancement on carbon dioxide reduction reaction (CO2RR) has shown promising results on CO2 conversion to value-added carbon products as renewable fuels or valuable chemical feedstocks. Formic acid is one of the products that requires low applied voltage in CO2RR. However, it remains with a low full cell energy efficiency, mainly due to the large full cell applied potential. Traditionally, the anodic reaction is carried out with oxygen evolution reaction (OER), which requires a higher applied potential on the counter electrode. This work reports a viable alternative anodic reaction on electro-oxidative upgrading of biomass-derived 5-hydroxymethylfurfural (HMF) into valorized chemicals such as furandicarboxylic acid (FDCA) using Ni-based catalyst. Experimental results have demonstrated that in the combined electrolyzer, the Faradaic efficiency of formate can increase from 41% to 76%, while keeping the applied potential as low as 2 V for achieving a current density of 10 mA/cm2. In addition, at the anode, the Faradaic efficiency reached 60% towards FDCA using the same combined setup. It is postulated that due to the lower overpotential at anode, the cathodic potential becomes more negative for the same current density, and hence a higher Faradaic efficiency towards formate can be attained. Moreover, compared to conventional systems with OER, the combined system is able to produce a value-added product such as FDCA with a considerable FE, approaching a more economic application.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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