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Record W7117570821 · doi:10.1021/acsenergylett.5c03743

Beyond Copper: Expanding the Horizon of CO <sub>2</sub> Electrocatalysis for Multicarbon Product Formation

2025· article· en· W7117570821 on OpenAlexaff
Kainat Talat, Umair Muhammad, S. M. Murtaza Sherazi, P. Silambarasan, Hyoyoung Lee

Bibliographic record

VenueACS Energy Letters · 2025
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsEnvironment and Climate Change Canada
FundersMinistry of Science and ICT, South KoreaKorea Basic Science InstituteNational Research Foundation of KoreaMinistry of EducationSungkyunkwan University
KeywordsOverpotentialElectrocatalystKey (lock)Product (mathematics)CatalysisFocus (optics)

Abstract

fetched live from OpenAlex

Copper-based CO 2 electroreduction holds great promise for producing valuable C 2+ products, but issues with selectivity, stability, high overpotentials, and limited extension beyond C 4 products persist. Non-Cu catalysts, despite some performance gaps compared to copper, offer alternative pathways with encouraging advancements in overpotential, stability, and selectivity, particularly addressing copper’s limitations in producing higher-order products. This Focus Review delivers a critical framework of non-Cu catalysts in comparison with Cu catalysts, classifying them by key metrics, current density, selectivity, stability, and overpotential by directly linking each metric to the specific design strategies. It dives into how, why, and which strategies applied to non-Cu catalysts enhance specific key metrics in comparison with Cu. Moreover, it highlights how the non-Cu catalysts open the door to producing even beyond C 4+ products, a barrier that copper has not crossed. Throughout a critical, side-by-side comparison with copper, we reveal the unique promise of non-Cu catalysts and outline visionary future paths to drive innovation in this evolving field.

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 score0.097
Threshold uncertainty score0.567

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 head0.007
GPT teacher head0.235
Teacher spread0.229 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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