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Record W2333100581 · doi:10.1021/ma4009753

Kinetics of Self-Immolative Degradation in a Linear Polymeric System: Demonstrating the Effect of Chain Length

2013· article· en· W2333100581 on OpenAlexaff
Ryan A. McBride, Elizabeth R. Gillies

Bibliographic record

VenueMacromolecules · 2013
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsWestern University
Fundersnot available
KeywordsDepolymerizationPolymerMonomerDegradation (telecommunications)KineticsChemistryLinear polymerChain (unit)Polymer chemistryPolymer degradationChain scissionCleavage (geology)Branching (polymer chemistry)Chemical engineeringMaterials scienceOrganic chemistryComputer scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

We describe here a study demonstrating that the degradation time of self-immolative linear polymers is dependent on chain length. These materials are unique relative to most degradable polymers, in that they undergo end-to-end depolymerization in response to the cleavage of an end-cap. Although one of their cited attributes is a dependence of their degradation time on chain length, no conclusive study has been conducted to demonstrate and study this effect. In this work, using a linear self-immolative polymer backbone derived from alternating 4-hydroxybenzyl alcohol and N, N ′-dimethylethylenediamine based spacers, we show that there is a proportional relationship between chain length and depolymerization time. This is first accomplished using a series of oligomers synthesized using a convergent, iterative route and then applied to the polydisperse case on a set of polymers displaying varying molecular weights. We also report the first development and validation of a self-immolative degradation model relating monomer kinetics to polymer degradation and show its application in explaining oligomeric and polymeric degradation profiles.

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 score0.003

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 integrity0.0000.001
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 head0.218
Teacher spread0.213 · 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

Citations70
Published2013
Admission routes1
Has abstractyes

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