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Record W2149820211 · doi:10.1351/pac200476071353

Morphology of blends of self-assembling long-chain carbamate and stearic acid

2004· article· en· W2149820211 on OpenAlexafffund
Mohammad Moniruzzaman, P. R. Sundararajan

Bibliographic record

VenuePure and Applied Chemistry · 2004
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStearic acidCarbamateChemistryDifferential scanning calorimetryAlkylHydrogen bondPolymer chemistryMorphology (biology)Organic chemistryChemical engineeringMoleculeThermodynamics

Abstract

fetched live from OpenAlex

Abstract The morphology of blends of two types of hydrogen-bonding systems was studied with a view to understanding the effect of blending on the extent of hydrogen bonding and changes in the morphology.One of them is the long-chain carbamate with a C 18 alkyl side chain, and the other is stearic acid, which also has a C 17 alkyl chain. At low concentrations, the C 18 carbamate reduces the size of the crystals of stearic acid. However, the stearic acid has no effect on the morphology of the carbamate. The morphological changes are due to the disruptive packing of the alkyl chains, rather than a change in the extent of hydrogen bonding. The blends of homologous carbamates studied before [Moniruzzaman, Goodbrand, Sundararajan, J. Phys. Chem. Part B 107 , 8416 (2003)] are more effective in mutually controlling the spherulite size and imparting transparency than the carbamate/stearic acid blend. The presence of the carbamate as the minor component changes the ratio of the intensities of the X-ray reflections at 2 (theta)=21.6 and 2.21°. It is found that in the blends with the carbamates quenched from the melt, the stearic acid exhibits a polymorphic transition from the E to the C form in the differential scanning calorimetry (DSC) analysis.

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 score0.009
Threshold uncertainty score0.358

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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

Citations16
Published2004
Admission routes2
Has abstractyes

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