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Record W4221106661 · doi:10.1002/chem.202200552

Luminescent Surface‐Tethered Polymer Brush Materials

2022· review· en· W4221106661 on OpenAlexafffund
Jade Poisson, Zachary M. Hudson

Bibliographic record

VenueChemistry - A European Journal · 2022
Typereview
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBrushMaterials sciencePolymerPolymer brushSurface (topology)LuminescenceComposite materialPolymer scienceNanotechnologyGeometryOptoelectronicsPolymerizationMathematics

Abstract

fetched live from OpenAlex

Surface-tethered polymers are unique molecular architectures that have been recently used in advanced sensors, electronics and biomedical applications. However, techniques for characterizing these materials in their surface-tethered form remain limited. The incorporation of luminescent functionality into these materials has enabled new characterization methods, while also unlocking new applications in optoelectronics, stenography and sensing. Micron-scale photolithography techniques have recently enabled the preparation of high-resolution patterns, as well as architectures with unique photophysical properties. Herein, we provide an overview of the techniques used to prepare luminescent polymer brush materials and their applications in stimuli-responsive sensors, cell adhesion materials, and optoelectronics. We also provide our perspective on the promising future uses of surface-tethered polymers, as well as the short-term challenges and opportunities in the field.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.073
GPT teacher head0.315
Teacher spread0.242 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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