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Record W4250802625 · doi:10.1149/ma2019-01/41/1978

(Keynote) Electroreduction of CO<sub>2</sub> on Pb and Bi Nanoneedles

2019· article· en· W4250802625 on OpenAlexaff
Daniel Guay, Mengyang Fan, Sébastien Garbarino, Ana C. Tavares, Sagar Prabhudev, Gianluigi A. Botton

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsMcMaster UniversityInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsFormic acidFormateSynthetic fuelEnvironmental scienceCatalysisSyngasWaste managementChemical industryEnvironmental chemistryMethanePulp and paper industryChemistryEnvironmental engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The rise in atmospheric CO2 concentration since the beginning of the Industrial Age has raised concerns on its potential effect on global temperatures. While several factors such as natural climate changes, variations in solar activity, and volcanic eruptions may also contribute to observed variations in CO2 concentrations, it has been well established that anthropogenic emissions contribute to the annual emission of over 30 billion tonnes of CO2, only about half of which is recycled through natural pathways. CO2 conversion into value-added products is an avenue explored to mitigate environmental issues. Conversion methods appear promising, as they offer the potential to produce liquid and gaseous fuels for use either in the aeronautics industry, or as a chemical storage method for the intermittent energy produced by windmills and photovoltaic panels. Fuel synthesis may be achieved via either the production of a syngas mixture (H2 and CO) through the water gas shift reaction and subsequent conversion into hydrocarbon fuel through processes such as Fisher-Tropsch, or by direct electrochemical reduction of CO2. While all methods may result in similar conversion yields, depending on the catalyst used, direct electrochemical conversion of CO2 into value-added products is a low-temperature process, with the further advantage of requiring relatively simple equipment. Formic acid, or formate salts, are used in a variety of chemical processes such as electrowinning, leather tanning, and aircraft de-icing. Alternatively, formic acid and formate salts may be considered a hydrogen storage medium. Direct formic acid, and more recently, direct formate fuel cells, have been investigated in the literature as they demonstrate significant benefits over methanol fuel cells, including higher open circuit voltage and lower crossover. Among earth-abundant CO2 electrocatalysts, Sn, Pb and Bi are known to be highly selective for formate production, with faradic efficiency (FE) > 90%. However, several challenges need to be addressed for these materials to become viable alternatives for industrial applications based on an ERC process to become economically viable. In particular, issues related with large overpotential and low current densities need to be addressed, along with the long term stability of electrodes. In this study, we compared the activity and stability for CO2 electroreduction of high surface area metallic Bi and Pb nanoneedles (see Figure 1). In both cases, the materials films were prepared through a direct electrodeposition (potentiostatic method). Both types of films were extensively characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HR-TEM) and x-ray diffraction (XRD), while electrochemical activities were characterized by cyclic voltammetry (CV), linear sweep voltammetry (LSV), and potentiostatic measurements. Figure 1

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.092
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0920.017

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 head0.230
Teacher spread0.221 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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