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Record W4403986874 · doi:10.1016/j.psep.2024.10.109

Experimental investigation of an integrated electro-cation exchange reactor for better carbon dioxide extraction and environmental management

2024· article· en· W4403986874 on OpenAlexaff
Hilal Sayhan Akci Turgut, İbrahim Dinçer

Bibliographic record

VenueProcess Safety and Environmental Protection · 2024
Typearticle
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCarbon dioxideExtraction (chemistry)Environmental scienceWaste managementProcess engineeringChemistryEnvironmental chemistryEngineeringChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

The research focuses on a uniquely designed three-compartment type integrated electrochemical reactor developed in a laboratory setting. This innovative reactor uses an electrolytic cation exchange technique to extract substantial quantities of carbon dioxide from ocean water as bicarbonate and carbonate while simultaneously producing hydrogen gas for potential hydrocarbon production. The study employs the Design Expert software package to evaluate and optimize the implementation of electrochemical carbon dioxide extraction from ocean water. The reactor is divided into three compartments by two cation-exchange membranes: a middle part and two electrode parts for the cathode and anode sides. The membranes possess acidic properties that facilitate cation transport while inhibiting anion transport. The laboratory studies, including 35-minute long experimental runs, are conducted to determine the system's feasibility by assessing carbon dioxide extraction under different conditions, such as electrolyte concentration, applied voltage, and pH levels. The reactor achieves a maximum CO₂ extraction rate of 1479.73 mg/min, with optimal conditions yielding 1514.6 mg/min. The ideal operating parameters are found to be a voltage of 14.8 V, an electrolyte concentration of 0.557 M, and a pH of 2.26. The results of the optimization study further reveal that carbon dioxide extraction increases with decreasing pH levels.

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 score0.127
Threshold uncertainty score0.765

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 communication0.0000.001
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 head0.010
GPT teacher head0.223
Teacher spread0.214 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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