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Record W2090772932 · doi:10.1021/jp905010w

Crystallization Kinetics of Ultraviscous Acetaminophen by Heat Capacity and Enthalpy Measurements and Diffusion Control

2009· article· en· W2090772932 on OpenAlexaff
E. Tombari, Sabrina Presto, Ravi Shanker, G. P. Johari

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2009
Typearticle
Languageen
FieldMaterials Science
TopicCrystallization and Solubility Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsKineticsEnthalpyDiffusionCrystallizationThermodynamicsHeat capacityAcetaminophenMaterials scienceChemistryPhysicsBiochemistry

Abstract

fetched live from OpenAlex

Crystallization kinetics of ultraviscous acetaminophen has been studied at 308.2, 318.2, and 328.2 K by measuring the heat capacity, C(p), and the heat release in real time up to a period of 2.5 days. C(p) decreases according to an inverted sigmoid-shape curve, and the heat released increases according to a similar shape. The extent of crystallization determined from the two measurements differs, thus indicating that the interfacial liquid's C(p) may be slightly different from that of the bulk liquid. Both the excess C(p) of the liquid over the crystal phase and the corresponding excess enthalpy decrease with decrease in the temperature. The kinetics of crystallization follows the Kolmogorov-Johnson-Mehl-Avrami relation, alpha(cryst)(t) = 1 - exp(-kt(m)). The logarithm of the rate constant, ln k, increases from -40.78 at 308.2 K to -32.85 at 328.2 K, and m from 3.60 to 3.83. The product of k and the calorimetric relaxation time remains constant with changing temperature thus showing that the two are inversely related. This shows that crystallization may be diffusion-controlled in the ultraviscous melt. The C(p) data indicate that slow crystallization of melt produces acetaminophen's (monoclinic) form I. Several effects usually overlooked in the crystallized kinetics formalisms have been described.

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 score0.003
Threshold uncertainty score0.218

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 studies0.0000.000
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 head0.237
Teacher spread0.221 · 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
Published2009
Admission routes1
Has abstractyes

Explore more

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