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Record W2969986438 · doi:10.1002/ijch.201900075

Reflections on the Evolution of Smart Polymers

2019· article· en· W2969986438 on OpenAlexafffund
Elizabeth R. Gillies

Bibliographic record

VenueIsrael Journal of Chemistry · 2019
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSmart polymerChemistryPolymerNanotechnologySmart materialDrug deliveryKey (lock)Covalent bondComputer scienceOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract Since Staudinger's recognition that polymers were long chain molecules with covalent bonds between repeating units, the field has evolved tremendously. In addition to their many structural roles, polymers have been developed to exhibit “smart” stimuli‐responsive behavior. This article will describe the evolution of selected classes of smart polymers including those responsive to changes in pH, temperature, light, and mechanical stimuli, as well as self‐immolative polymers and their application in drug delivery, sensors, and actuators. It will also highlight key advancements in polymer chemistry that enabled rapid progress over the past ∼20 years. Whether the key achievements were predictable will be discussed, and the extent to which polymer science remains an independent science versus a service tool will be addressed. Finally, some possibilities for the evolution of the field over the next 20–30 years will be 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 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)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.006
Scholarly communication0.0030.007
Open science0.0010.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0060.002

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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2019
Admission routes2
Has abstractyes

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