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Record W2322733033 · doi:10.1021/jp409062k

Influence of Structure on Chemical and Thermal Stability of Aliphatic Diesters

2013· article· en· W2322733033 on OpenAlexaff
Latchmi Raghunanan, Suresh S. Narine

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2013
Typearticle
Languageen
FieldMaterials Science
TopicThermal and Kinetic Analysis
Canadian institutionsTrent University
Fundersnot available
KeywordsThermal stabilityChemical stabilityPolyesterChemistryMethyleneChemical structureChemical shiftChemical modificationOrganic chemistryKineticsPolymer chemistryFourier transform infrared spectroscopyChemical engineeringPhysical chemistry

Abstract

fetched live from OpenAlex

Ester group interactions with each other and with the atoms between them were investigated in order to determine dependence of chemical and thermal stabilities of aliphatic diesters on structure. Novel glycol-derived diesters with chemical formula (C17H33COO)2C(n)H(2n) were used as model systems. Chemical stability was determined using (1)H NMR and FTIR, and thermal stability and weight-loss kinetics were examined using nonisothermal TGA. Chemical stability increased with the number of methylene units (n, carbon) between the ester groups until n = 6, and no significant improvement was observed past n > 6. It is argued that other ester-dense materials, including polyesters, would behave similarly. Evidence of a strong dependence of thermal stability on chemical stability is also provided. This work shows that the chemical and thermal stabilities of ester-dense functional materials such as diesters, oligo-esters, and polyesters can be manipulated by varying the distance between the ester groups, and hence the interactions of the electron-withdrawing ester groups with its neighbors.

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)0.000
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 score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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

Citations33
Published2013
Admission routes1
Has abstractyes

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