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Record W2926690991 · doi:10.1021/acsenergylett.9b00191

Strain Engineering Electrocatalysts for Selective CO<sub>2</sub> Reduction

2019· article· en· W2926690991 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Energy Letters · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of British Columbia
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsStrain (injury)SelectivityElectrochemistryRedoxPerspective (graphical)Pulmonary surfactantMaterials scienceCatalysisChemistryNanotechnologyElectrodeComputer sciencePhysical chemistryMetallurgyOrganic chemistryBiologyBiochemistry

Abstract

fetched live from OpenAlex

Lattice strain can enhance the activity and selectivity of electrochemical reactions by breaking the linear scaling relationship. Notwithstanding, the explicit use of strain to affect the CO2 reduction reaction (CO2RR) is rarely reported. In this Perspective, we highlight the opportunity to use strain to affect the activity and selectivity of CO2RR electrocatalysts. We summarize the existing challenges in isolating the influence of strain from convoluting factors (e.g., size, shape, electronic, and surfactant effects) that result from typical methods of inducing strain. We also propose ways to isolate strain effects using the application of mechanical strain to thin-film CO2RR catalysts. We designed this Perspective to motivate the use of joint empirical and computational studies to investigate CO2RR strain–activity–selectivity relationships.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.025
Threshold uncertainty score1.000

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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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