MétaCan
Menu
Back to cohort
Record W2626495198 · doi:10.1002/aic.15833

The development of kinetics model for CO<sub>2</sub> absorption into tertiary amines containing carbonic anhydrase

2017· article· en· W2626495198 on OpenAlexaff
Bin Liu, Xiao Luo, Zhiwu Liang, Wilfred Olson, Helei Liu, Raphael Idem, Paitoon Tontiwachwuthikul

Bibliographic record

VenueAIChE Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
FundersNational Natural Science Foundation of China
KeywordsChemistryAlkanolamineAqueous solutionTertiary amineAmine gas treatingKineticsCarbonic anhydraseAbsorption (acoustics)Carbonic anhydrase IIReaction rate constantCatalysisInorganic chemistryNuclear chemistryOrganic chemistryEnzymeMaterials science

Abstract

fetched live from OpenAlex

CO 2 absorption into aqueous solutions of two tertiary alkanolamines, namely, MDEA and DMEA with and without carbonic anhydrase (CA) was investigated with the use of the stopped‐flow technique at temperatures in the range of 293–313 K, CA concentration varying from 0 to 100 g/m 3 in aqueous MDEA solution with the amine concentration ranging from 0.1 to 0.5 kmol/m 3 , and CA concentration varying from 0 to 40 g/m 3 in aqueous DMEA solution with the amine concentration ranging from 0.05 to 0.25 kmol/m 3 . The results show that the pseudofirst‐order reaction rate (k 0, amine ; s −1 ) is significantly enhanced in the presence of CA as compared with that without CA. The enhanced values of the kinetic constant in the presence of CA has been calculated and a new kinetics model for reaction of CO 2 absorption into aqueous tertiary alkanolamine solutions catalyzed by CA has been established and used to make comparisons of experimental and calculated pseudo first‐order reaction rate constant (k 0, with CA ) in CO 2 ‐MDEA‐H 2 O and CO 2 ‐DMEA‐H 2 O solutions. The AADs were 15.21 and 15.17%, respectively. The effect of pKa on the CA activities has also been studied by comparison of CA activities in different tertiary amine solutions, namely, TEA, MDEA, DMEA, and DEEA. The pKa trend for amines were: DEEA > DMEA > MDEA > TEA. In contrast, the catalyst enhancement in amines was in the order: TEA> MDEA> DMEA> DEEA. Therefore, it can be seen that the catalyst enhancement in the amines decreased with their increasing pKa values. © 2017 American Institute of Chemical Engineers AIChE J , 2017

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.018
GPT teacher head0.251
Teacher spread0.234 · 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 designSimulation or modeling
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

Citations24
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAIChE JournalSame topicCarbon Dioxide Capture TechnologiesFrench-language works237,207