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Record W4200415821 · doi:10.1016/j.xcrp.2021.100682

Strategies for heterogeneous small-molecule electrosynthesis

2021· article· en· W4200415821 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

VenueCell Reports Physical Science · 2021
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrosynthesisScope (computer science)NanotechnologyRenewable energyBiochemical engineeringComputer scienceEngineeringChemistryMaterials scienceElectrical engineering

Abstract

fetched live from OpenAlex

With an increasing global emphasis on renewable energy, electrosynthetic technologies stand to play a substantial role in generating the fuels and chemicals that power today’s society. While directions such as water electrolysis and CO2 directions have been heavily researched in the last decade, the scope of electrosynthesis can be greatly expanded to cover the full range of chemical targets that serve as building blocks for materials, pharmaceuticals, fertilizers, and more. To this end, the main challenges lie in the discovery of novel reaction routes and innovative catalytic systems that circumvent conventional limitations of electrocatalysis. Against this backdrop, this perspective will focus on the use of emerging methodologies to pioneer new electrosynthetic reaction systems. In this work, strategies of environmental control, phase change materials, reactant-selective membranes, and mediated approaches are discussed, before touching on the innovative spectroscopic approaches used to probe these systems and wrapping up with a forward-thinking outlook.

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 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 score0.053
Threshold uncertainty score0.424

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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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