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Record W4319032823 · doi:10.1002/cjce.24865

On the <scp> CO <sub>2</sub> </scp> absorption kinetics, loading capacity, and catalytic desorption of aqueous solutions of <i>N</i> ‐methyl‐ <i>D</i> ‐glucamine

2023· article· en· W4319032823 on OpenAlexvenueno aff
Urvashi K. Sarode, Prakash D. Vaidya

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsnot available
FundersDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsChemistryDesorptionCatalysisAqueous solutionSolventChemical engineeringKineticsAnalytical Chemistry (journal)ChromatographyPhysical chemistryAdsorptionOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract CO 2 separation with harmful chemicals will damage the environment. It is essential to explore greener solvents that are producible from renewable resources such as biomass. The suitability of N ‐methyl‐ D ‐glucamine (MG), also known as meglumine, for capturing CO 2 , was explored in this work. This nontoxic amino sugar, which is derived from sorbitol, represents a renewable bio‐solvent. It was found that MG is especially reactive with CO 2 . Trials were performed in a stirred cell reactor with a flat gas–liquid interface between 303 and 313 K. The values of the pseudo‐first‐order reaction rate constant, reaction orders, and activation energy were found. The loading capacity ( α ) of 0.5 M MG solution was measured at T = 308 K. For a typical value of α = 0.524 mol CO 2 /mol MG, the corresponding equilibrium partial pressure of CO 2 was 22 kPa. Finally, it was found that the catalyst Al 2 O 3 aided in the desorption of CO 2 ‐loaded MG solutions. Desorption efficiency using Al 2 O 3 was higher (74%) than that achieved without this catalyst (45%). It is thus clear that MG represents a potential solvent for improved CO 2 separation from gases.

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)0.001
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 score0.036
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.014
GPT teacher head0.178
Teacher spread0.164 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

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