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Record W4405503531 · doi:10.1016/j.nanoms.2024.11.007

Recent progress in bismuth-based materials for electrochemical CO2 reduction to formate/formic acid

2024· article· en· W4405503531 on OpenAlexaff
Xinrui Linghu, Jun Chen, Liangliang Jiang, Tianshuai Wang

Bibliographic record

VenueNano Materials Science · 2024
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBismuthFormic acidFormateElectrochemistryReduction (mathematics)Materials scienceInorganic chemistryChemistryMetallurgyPhysical chemistryOrganic chemistryCatalysisElectrodeMathematics

Abstract

fetched live from OpenAlex

The electrochemical reduction of carbon dioxide (CO 2 ) to formate/formic acid represents a significant pathway for sustainable fuel production, addressing both environmental sustainability and the growing demand for renewable energy sources. Recently, bismuth-based (Bi-based) catalysts have attracted significant attention for this field due to their high selectivity, cost-effectiveness, and environmental friendliness. However, a critical challenge remains: developing catalysts that can achieve industrial-scale current density, high Faradaic efficiency, and robust stability simultaneously. Various emerging strategies have been explored to overcome this challenge. This review provides a comprehensive overview of recent advancements in this area. We begin with a discussion of the reaction mechanisms and theoretical optimization techniques for CO 2 reduction using Bi-based electrocatalysts. We then highlight recent optimization strategies for designing high-performance Bi-based catalysts, including approaches such as morphology control, crystal plane effects, doping engineering, interface engineering, and single-atom alloy engineering. Finally, we discuss future research directions for designing Bi-based catalysts capable of operating under industrial conditions for the electroreduction of CO 2 to formate/formic acid.

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 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)0.001
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 score0.030
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.290
Teacher spread0.277 · 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 teacher head, 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

Citations11
Published2024
Admission routes1
Has abstractyes

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