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Record W3111290452 · doi:10.1021/acs.iecr.0c04711

From CO<sub>2</sub> to Formic Acid Fuel Cells

2020· article· en· W3111290452 on OpenAlexafffund
Zhenni Ma, Ulrich Legrand, Ergys Pahija, Jason R. Tavares, Daria C. Boffito

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsPolytechnique Montréal
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsFormic acidProton exchange membrane fuel cellFuel cellsEnergy storageChemistryProcess engineeringEnvironmental scienceChemical engineeringPower (physics)EngineeringOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Formic acid is a liquid, safe, and energy-dense carrier for fuel cells. Above all, it can be sustainably produced from the electroreduction of CO 2 . The formic acid market is currently saturated, and it requires alternative applications to justify additional production capacity. Fuel cell technologies offer a chance to expand it, while creating an opportunity for sustainability in the energy sector. Formic acid-based fuel cells represent a promising energy supply system in terms of high theoretical open-circuit voltage (1.48 V). Compared to common fuel cells running on H 2 (e.g., proton-exchange membrane fuel cells), formic acid has a lower storage cost and is safer. This review focuses on the sustainable production of formic acid from CO 2 and on the detailed analysis of commercial examples of formic acid-based fuel cells, in particular direct formic acid fuel cell stacks. Designs described in the literature are mostly at the laboratory scale, still, with 301 W as the maximum power output achieved. These case studies are fundamental for the scale-up; however, additional efforts are required to solve crossover and increase performance.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.076
GPT teacher head0.308
Teacher spread0.232 · 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

Citations138
Published2020
Admission routes2
Has abstractyes

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