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Record W4386374640 · doi:10.1002/cjce.25083

<scp>CO<sub>2</sub></scp> absorption kinetics and equilibrium solubility measurements in potassium salts of renewable amino acids from plant‐ and animal‐protein

2023· article· en· W4386374640 on OpenAlexvenueno aff
Hitesh D. Rawate, 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
KeywordsChemistryAmino acidAqueous solutionPotassiumSolubilityAlanineSalt (chemistry)ProlineAbsorption (acoustics)KineticsValineMolar concentrationLeucineGlycineInorganic chemistryChromatographyOrganic chemistryBiochemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract Aqueous solutions containing alkaline salts of natural amino acids, such as those from protein in plant seeds or high protein animal‐based waste, are green CO 2 ‐separation solvents. In the present work, potassium salts of nine such amino acids were chosen for an in‐depth study: alanine, arginine, aspartic acid, glutamic acid, glycine, leucine, proline, serine, and valine. The kinetics of CO 2 absorption in aqueous solutions of these salts was studied using a stirred cell. From the measurements of the absorption rate at different salt concentrations (molarity 0.1 and higher), CO 2 partial pressures (5–25 kPa), and temperatures (298–308 K), values of the reaction order, rate constant, and activation energy were determined. Additionally, the liquid‐side mass transfer coefficient (0.005 cm/s) was also found. Potassium salts of proline, glycine, and arginine were most reactive and, hence, were chosen for equilibrium study. The loading capacity of these salts was measured at 308 K in a vapour–liquid equilibrium setup at near‐ambient pressure. On the contrary, the other chosen acids were comparatively less reactive with CO 2 .

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 score0.013
Threshold uncertainty score0.684

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 integrity0.0000.000
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 head0.016
GPT teacher head0.187
Teacher spread0.172 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

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