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Record W4410741683 · doi:10.1021/acsenergylett.5c00452

Solar-Driven Carbon Dioxide Capture Using a Photoelectrochemical Redox Flow System

2025· article· en· W4410741683 on OpenAlexaff
Maryam Abdinejad, Maryam G. Elmahgary, Vittoria Bolongaro, Michael Massen‐Hane, Alexandra Tavasoli, T. Alan Hatton

Bibliographic record

VenueACS Energy Letters · 2025
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRedoxCarbon dioxideCarbon fibersChemistryMaterials scienceChemical engineeringNanotechnologyInorganic chemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Carbon dioxide (CO 2 ) capture is essential for mitigating climate change, but scaling existing technologies to address global CO 2 emissions will place significant demands on energy systems. To overcome this challenge, we present a photoelectrochemical flow system using anthraquinone-2,7-disulfonate (AQDS) as a sorbent, which is activated by sunlight to capture CO 2 under dark conditions and releases it upon reillumination. This system successfully cycled for 70 h, demonstrating both stability and efficiency. Inspired by solar rechargeable redox flow batteries, the system expands on current solar-driven CO 2 capture technologies by enabling CO 2 release via photodesorption at 0 V vs OCV. While the system is still in its early stages, it presents a promising addition to the range of photo- and electrically driven CO 2 capture methods, with potential for future advancements toward more effective and scalable CO 2 capture solutions.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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