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Record W2944254746 · doi:10.1039/c9cs00153k

Boron-based stimuli responsive materials

2019· review· en· W2944254746 on OpenAlexafffund
Soren K. Mellerup, Suning Wang

Bibliographic record

VenueChemical Society Reviews · 2019
Typereview
Languageen
FieldMaterials Science
TopicLuminescence and Fluorescent Materials
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaAlexander von Humboldt-Stiftung
KeywordsBoronNanotechnologyIntermolecular forceMaterials scienceDopingMoleculeChemistryOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

Boron-based stimuli responsive systems represent an emerging class of useful materials with a wide variety of applications. Functions within these boron-doped molecules are derived from external stimuli such as light, heat, and force, which alter their intra- and/or intermolecular interactions, yielding unique electronic/photophysical or mechanical properties that can be exploited as optical probes or switchable materials. In this review, the various state-switching mechanisms of these boron-based materials will be introduced, followed by a detailed account of recent advances in the field. Emphasis will be placed on structure-property relationships and the potential applications of the stimuli-responsive boron compounds.

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 score0.003
Threshold uncertainty score0.010

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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.115
GPT teacher head0.369
Teacher spread0.254 · 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

Citations501
Published2019
Admission routes2
Has abstractyes

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