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Progress and Perspectives of Pulse Electrolysis for Stable Electrochemical Carbon Dioxide Reduction

2023· article· en· W4386533309 on OpenAlexafffund
Cornelius A. Obasanjo, Guorui Gao, Behnam Nourmohammadi Khiarak, Trong Huy Pham, Jackson Crane, Cao‐Thang Dinh

Bibliographic record

VenueEnergy & Fuels · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsQueen's University
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaQueen's University
KeywordsElectrolysisCathodeDegradation (telecommunications)ElectrochemistryMaterials scienceProcess engineeringElectrochemical reduction of carbon dioxideDeposition (geology)NanotechnologyEnvironmental scienceElectrodeChemistryComputer scienceCatalysisCarbon monoxideEngineering

Abstract

fetched live from OpenAlex

Electrochemical carbon dioxide (CO 2 ) reduction (ECR) to fuels and chemicals is a promising approach to address anthropogenic CO 2 emissions. Over the past few years, ECR technology has advanced significantly, leading to the demonstration at both relatively large scale and with high efficiency. Specifically, both product selectivity and energy efficiency at high current densities are approaching the target for practical application. However, stability, a critical performance metric for ECR economics, is still far from the performance required for widespread application. In ECR, the cathode is most prone to degradation due to catalyst reconstruction, electrode flooding, salt formation, and impurity deposition. Pulse electrolysis has emerged as a promising approach to mitigate these degradation pathways and improve the stability of the ECR systems. In this review, we first discuss key ECR cathode degradation mechanisms. Next, we highlight the progress toward designing stable ECR systems using pulse electrolysis. We also assess the prospects and challenges of applying pulse electrolysis toward sustainable and industrial ECR applications.

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)0.000
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 score0.033
Threshold uncertainty score0.594

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 science0.0000.000
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 head0.010
GPT teacher head0.243
Teacher spread0.234 · 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

Citations37
Published2023
Admission routes2
Has abstractyes

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