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Record W3042815916 · doi:10.1038/s41467-020-17403-1

Electrochemical CO2 reduction to high-concentration pure formic acid solutions in an all-solid-state reactor

2020· article· en· W3042815916 on OpenAlexafffund
Lei Fan, Chuan Xia, Peng Zhu, Yingying Lü, Haotian Wang

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsCanadian Institute for Advanced Research
FundersZhejiang UniversityChina Scholarship CouncilRice UniversityCanadian Institute for Advanced ResearchWelch Foundation
KeywordsFormic acidFormateFaraday efficiencyElectrolyteElectrochemistryInorganic chemistryAnodeMaterials scienceInert gasChemistryCatalysisElectrodeChromatographyOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Electrochemical CO 2 reduction reaction (CO 2 RR) to liquid fuels is currently challenged by low product concentrations, as well as their mixture with traditional liquid electrolytes, such as KHCO 3 solution. Here we report an all-solid-state electrochemical CO 2 RR system for continuous generation of high-purity and high-concentration formic acid vapors and solutions. The cathode and anode were separated by a porous solid electrolyte (PSE) layer, where electrochemically generated formate and proton were recombined to form molecular formic acid. The generated formic acid can be efficiently removed in the form of vapors via inert gas stream flowing through the PSE layer. Coupling with a high activity (formate partial current densities ~450 mA cm −2 ), selectivity (maximal Faradaic efficiency ~97%), and stability (100 hours) grain boundary-enriched bismuth catalyst, we demonstrated ultra-high concentrations of pure formic acid solutions (up to nearly 100 wt.%) condensed from generated vapors via flexible tuning of the carrier gas stream.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.025
GPT teacher head0.306
Teacher spread0.281 · 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

Citations560
Published2020
Admission routes2
Has abstractyes

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