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Record W2730855548 · doi:10.1021/acs.iecr.7b01540

Study of Ion Speciation of CO<sub>2</sub> Absorption into Aqueous 1-Dimethylamino-2-propanol Solution Using the NMR Technique

2017· article· en· W2730855548 on OpenAlexafffund
Helei Liu, Raphael Idem, Paitoon Tontiwachwuthikul, Zhiwu Liang

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
FundersChina Scholarship CouncilMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of ChinaNatural Sciences and Engineering Research Council of CanadaHunan Provincial Science and Technology DepartmentNational Natural Science Foundation of China
KeywordsAqueous solutionChemistryProtonationAmine gas treatingAbsorption (acoustics)Analytical Chemistry (journal)IonPropanolCarbon-13 NMRPhysical chemistryInorganic chemistryMaterials scienceStereochemistryOrganic chemistryMethanol

Abstract

fetched live from OpenAlex

In this work, the speciation (i.e., 1DMA2P molecule as well as 1DMA2PH +, HCO 3 –, and CO 3 2– ions) for the CO 2 reactive absorption of CO 2 in aqueous 1-dimethylamino-2-propanol solution (i.e., into a 1DMA2P–H 2 O–CO 2 system) was studied using the 13 C nulcear magnetic resonance (NMR) technique at a temperature of 301 K over the 1DMA2P concentration range of 0.5–2.0 mol/L and CO 2 loading range of 0–1.0 mol CO 2 /mol amine. Also, in addition to other material conservation laws, a new criterion for selection of the protonation calibration curves, the charge balance of the 1DMA2P–H 2 O–CO 2 system, was added in order to generate results with better accuracy. In addition, the equilibrium constants, K 1 and K 5, were also obtained using the NMR technique, which had good agreement with those from other works with absolute average deviations of 2.2% and 3.5%, respectively.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.015
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
Research integrity0.0010.001
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 head0.090
GPT teacher head0.330
Teacher spread0.240 · 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
Published2017
Admission routes2
Has abstractyes

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