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Record W2901048932 · doi:10.1002/cjce.23338

Phase transition properties, chemical purity, and solubility of coniferyl alcohol and D‐mannose: Experimental and Cosmo‐RS predictions

2018· article· en· W2901048932 on OpenAlexvenueno aff
Mood Mohan, Tamal Banerjee, Vaibhav V. Goud

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCrystallization and Solubility Studies
Canadian institutionsnot available
FundersDepartment of Science and Technology, Philippines
KeywordsDifferential scanning calorimetryChemistryEnthalpy of fusionSolubilityAnalytical Chemistry (journal)Coniferyl alcoholMelting pointThermodynamicsPhysical chemistryOrganic chemistryLignin

Abstract

fetched live from OpenAlex

Abstract The main objective of the study was to investigate the phase transition properties (melting temperature and heat of fusion) and chemical purity of coniferyl alcohol and D‐mannose by differential scanning calorimetry (DSC). The measurements have been performed at different heating rates from 1–20 °C/min under atmospheric inert gas. From the DSC analysis, the onset temperature, peak temperature, and the heat of fusion of both the compounds were measured. They were found to increase with an increase in the heating rate. The chemical purity of coniferyl alcohol and D‐mannose were determined based on the van't Hoff equation. The chemical purity of coniferyl alcohol was observed to be 99.54 mol% with a correction factor of 1.44 % at a 15 °C/min heating rate. The corresponding purity values for D‐mannose were 98.98 mol% at a correction factor of 6.76 % and 20 °C/min. The obtained results showed that the chemical purity of both the compounds was found to decrease with an increase in the correction factor. Furthermore, this study also focuses on the solubility of both substances in ionic liquids by applying the continuum solvation model (COSMO‐RS) using DSC measured melting properties as input.

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)0.000
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 score0.004
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.023
GPT teacher head0.245
Teacher spread0.222 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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