MétaCan
Menu
Back to cohort
Record W4417443304 · doi:10.1039/d6ta03239g

Dual-Function Electrochemical Cell for Simultaneous Carbon Capture and Lithium Extraction from Saline Waters

2025· preprint· en· W4417443304 on OpenAlexafffund
Omer Shinnawy, Seyyed Arman Hejazi, Kiana Amini

Bibliographic record

VenueJournal of Materials Chemistry A · 2025
Typepreprint
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrochemistryExtraction (chemistry)Lithium (medication)Carbon fibersBismuthElectrode

Abstract

fetched live from OpenAlex

Effective utilization of saline waters as a large reservoir of both dissolved inorganic carbon (DIC) and lithium would simultaneously support carbon mitigation and secure critical mineral supply. Here, we report the first demonstration of a dual-function electrochemical system capable of coupling CO2 capture and Li⁺ extraction from saline waters. Using a pH-swing architecture with bismuth and LiFePO₄ electrodes, we achieve stable pH cycling between 8.1 and 4.7 in 2.1 mM DIC, while extracting lithium at concentrations down to 0.17 ppm Li⁺ (seawater levels). Under impurity-free conditions, the system maintains 80% bismuth utilization and 50% lithium utilization, with attractive energetic costs of 128 kJ mol⁻¹ CO2 and 121 kJ mol⁻¹ Li, comparable to state-of-the-art stand-alone electrochemical systems. By integrating Li⁺ and CO2 extraction using shared pumping, membrane, and reactor infrastructure, this system offers a compelling path toward co-production of critical materials and integrated CO2 capture using a single seawater-processing platform.

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 score0.004

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)0.0010.001

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 head0.233
Teacher spread0.227 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Materials Chemistry ASame topicMembrane-based Ion Separation TechniquesFrench-language works237,207