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Record W4382989458 · doi:10.33140/pcii.06.03.05

Sustainable System for CO2 Capturing with Multiple Products

2023· article· en· W4382989458 on OpenAlexafffund
Hossam A. Gabbar, Kenji Sorimachi

Bibliographic record

VenuePetroleum and Chemical Industry International · 2023
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Ontario Institute of Technology
FundersUniversity of Ontario Institute of Technology
KeywordsAmine gas treatingSeawaterElectrolysisChemistryCo2 removalCarbon fixationCarbon dioxideChemical engineeringEnvironmental chemistryOrganic chemistryGeologyEngineering

Abstract

fetched live from OpenAlex

Currently, we are facing several serious issues, including diseases, climate change, economics, and wars. Among them, which needs to be resolved imminently to protect the future generations. Recently, we developed an innovative CO2 fixation and storage method based on the use of chemical compounds such as NaOH and CaCl2, to prevent the climate crisis. Herein, the electrolysis of a NaCl solution or seawater was performed to make the resultant NaOH react with CO2. Amines that react with CO2 were also examined. Moreover, seawater was used to produce CaCO3 rather than the CaCl2 solution. Although amines are currently used to capture CO2 from exhaust gases, the thermal treatment of the amine–CO2 complex solution is necessary to release CO2. Thermal treatment requires energy that eventually produces CO2 and induces the degradation of organic amines. However, the CO2 captured in the amine or NaOH solution was released easily by acidifying the CO2-containing solutions. Moreover, HCl could release CO2 from hydro carbonates, Ca(OH)2, Mg(OH)2, and mMgCO3·Mg(OH)2·nH2O, as well as from mineral carbonates, CaCO3 and MgCO3. This simple method of releasing the captured CO2 from the amine–CO2 complexes, hydro carbonates, and mineral carbonates is crucial to obtain pure CO2 for additional usage as an original material.

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 score0.050
Threshold uncertainty score0.445

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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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