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Record W2809085071 · doi:10.1021/acsami.8b04530

Reversibly Switching Molecular Spectra

2018· article· en· W2809085071 on OpenAlexaff
Yong Zhang, Hulie Zeng, Sifeng Mao, Shun Kondo, Hizuru Nakajima, Shungo Kato, Carolyn L. Ren, Katsumi Uchiyama

Bibliographic record

VenueACS Applied Materials & Interfaces · 2018
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence and Fluorescent Materials
Canadian institutionsUniversity of Waterloo
FundersJapan Society for the Promotion of Science
KeywordsMaterials scienceAbsorbanceProflavineMoleculeSpectral lineFluorescenceEmission spectrumAbsorption spectroscopyPolymerChemical physicsMolecular dynamicsElectric fieldPhotochemistryNanotechnologyOpticsChemistryOrganic chemistryComputational chemistryPhysics

Abstract

fetched live from OpenAlex

Manipulation of light transmission/absorbance and reflection/emission has a great significance in smart windows and displaying media like liquid crystal. Here, we report the usage of an external electric field to reversibly switch the molecular spectra of a model molecule on the basis of its interaction with an electroresponsible polymer brush. Both the UV-vis absorbance spectrum and the fluorescence emission spectrum of the model molecule were confirmed to be electroswitchable. The electroswitchable spectra were experimentally demonstrated to be induced by the electroswitchable statuses of medium anionic poly-allyloxy hydroxypropyl sulfonate (poly-AHPS) brush. Insightfully, the molecular aggregated status of model proflavine molecules could be electrically controlled via the electroresponsible poly-AHPS brushes and then the molecular spectra of the model proflavine molecule also could be electrically and controllably shifted. The success in the manipulation of molecular spectra opens up a wide range of applications not only for displaying but also for nonlinear optics, in vivo imaging, sensors, and environmental inspection.

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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

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