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Record W2562155358 · doi:10.1021/acs.jced.6b00504

Density, Viscosity, and N<sub>2</sub>O Solubility of Aqueous 2-(Methylamino)ethanol Solution

2016· article· en· W2562155358 on OpenAlexaff
Xiao Luo, Liusong Su, Hongxia Gao, Xitian Wu, Raphael Idem, Paitoon Tontiwachwuthikul, Zhiwu Liang

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2016
Typearticle
Languageen
FieldChemical Engineering
TopicThermodynamic properties of mixtures
Canadian institutionsUniversity of Regina
FundersNatural Science Foundation of Hunan ProvinceMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsSolubilityAqueous solutionEthanolViscosityChemistryThermodynamicsNuclear chemistryInorganic chemistryChemical engineeringPhysical chemistryOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

In the present work, the density and viscosity of 2-(methylamino)ethanol (MAE) solution were measured over the temperature range of 293.15 to 323.15 K with MAE mass fractions of w 1 = 0.075, 0.15, 0.225, and 0.30 and CO 2 loadings varying between 0 and 0.677 mol CO 2 /mol MAE. The physical solubility of N 2 O in aqueous MAE solution was measured in a stirred cell reactor over the temperature range of 289.31–348.18 K with MAE mass fraction w 1 = 0.075, 0.15, 0.225, 0.30, 0.375, 0.45, 0.60, 0.75, and 1. The experimental density data for both CO 2 loaded and unloaded aqueous MAE solutions were fitted by Redlich–Kister equation. The Weiland’s model was used to correlate the viscosity data of aqueous MAE solution. Finally, N 2 O solubility data were correlated by using an empirical polynomial model and compared with both the semiempirical model and the Redlich–Kister equation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.219
Teacher spread0.204 · 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 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

Citations46
Published2016
Admission routes1
Has abstractyes

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