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Record W4416542089 · doi:10.1007/s10800-025-02345-7

Electrochemical regeneration of caustic absorbent for the capture of CO2

2025· article· en· W4416542089 on OpenAlexaff
Colin Oloman

Bibliographic record

VenueJournal of Applied Electrochemistry · 2025
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElectrochemistryElectrolyteAlkali metalGas diffusion electrodeElectrochemical cellElectrodeCarbon fibersMembrane reactor

Abstract

fetched live from OpenAlex

Abstract Electrochemistry has a potential role in the removal of CO 2 from the atmosphere in the process of “electrochemically mediated carbon capture”. The future viability of that technology rests in part on the invention and development of specialized electrochemical reactors. This paper reports tests undertaken with a novel bench-scale electrochemical reactor intended for the potential regeneration of caustic absorbent in the direct air capture of CO 2 . The reactor design was based on capillary effects in micro-porous separators, with a single electrolyte stream and no ion-exchange membranes or gas diffusion electrodes. In batch-recycle mode the reactor converted 1 to 4 molar solutions of M 2 CO 3 (M ≡ Na or K) to MOH + M 2 CO 3 plus separate gas co-products H 2 and (O 2 + CO 2 ). Integral current efficiency for OH − and CO 2 fell from ca. 90% to 10% as the product [OH − ]/[CO 3 = ] ratio climbed from 0 to 3. The current density, full cell voltage, pressure, temperature and electrochemical specific energy ranged respectively from 1–6 kA m −2 , 3–10 V, 105–120 kPa(a), 25–70 ℃ and 4–200 kWh kg CO 2 −1 . Operating time was limited to about 20 h as reactor components deteriorated rapidly with the strong alkali and high oxidation potential around the anode. In some circumstances and with further development such an electrochemical system could potentially replace the thermochemical regeneration of alkaline absorbent for carbon capture. Graphical abstract

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 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 score0.137
Threshold uncertainty score0.422

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 head0.003
GPT teacher head0.200
Teacher spread0.197 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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