MétaCan
Menu
← Back to cohort
Record W4386288569 · doi:10.26434/chemrxiv-2023-5w98m

Thermoelectric CO2RR electrolysis

2023· preprint· en· W4386288569 on OpenAlexafffund
Curtis P. Berlinguette, Abhishek Soni, Xin Lu, Chris Zhou, Sneha Singh

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of British ColumbiaCanadian Institute for Advanced Research
FundersCanada First Research Excellence FundCanada Research ChairsCanadian Institute for Advanced Research
KeywordsElectrolysisPotentiostatThermoelectric generatorThermoelectric effectProcess engineeringWaste heatMars Exploration ProgramGeothermal gradientElectricityEnvironmental scienceMaterials scienceWaste managementElectrochemistryChemical engineeringMechanical engineeringChemistryEngineeringElectrical engineeringThermodynamicsPhysicsElectrodeAstrobiologyHeat exchanger

Abstract

fetched live from OpenAlex

We report here a fully contained electrolyzer that drives the CO2 reduction reaction (CO2RR) with thermoelectric generators instead of a potentiostat. The thermoelectric generators generate the electricity required to drive electrolysis when the temperature difference between the two faces is at least 40 oC. We show that the temperature gradients that could exist at a geothermal plant are sufficient to drive the reduction of gaseous CO2 into CO in an electrochemical flow cell, creating an opportunity to use waste heat to help decarbonization and clean fuels production. We also show how the electrolyzer system may be relevant to the future colonization of Mars, where there are large temperature differentials and an atmosphere rich in CO2.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.022
GPT teacher head0.260
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueChemRxiv→Same topicCO2 Reduction Techniques and Catalysts→French-language works237,207→