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Record W4410953639 · doi:10.1021/acsanm.5c01049

Water-Dispersible Glassy Organic Dots Exhibiting Near-Infrared Delayed Emission for Bioimaging

2025· article· en· W4410953639 on OpenAlexafffund
Tinotenda Rose Masvikeni, William L. Primrose, Sydney Mikulin, Zachary M. Hudson

Bibliographic record

VenueACS Applied Nano Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence and Fluorescent Materials
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Research ChairsCanada Foundation for Innovation
KeywordsMaterials scienceInfraredAggregation-induced emissionNanotechnologyFluorescenceOptics

Abstract

fetched live from OpenAlex

Red to near-infrared (NIR) emitting thermally activated delayed fluorescence (TADF) emitters and room temperature phosphorescence (RTP) emitters are attractive for time-gated biological imaging. Luminophores that emit red to NIR light can operate within the biological transparency window (650–1350 nm), enabling deeper penetration into tissue while also being easier to distinguish from cellular autofluorescence. Herein, we report a TADF emitter NAI-Q-MeOTPA and an RTP emitter NAI-Py-MeOTPA which incorporate the strong electron-donating group bis(4-methoxyphenyl)amine and strong acceptor cores quinoxaline-naphthalimide and pyrido[2,3- b ]pyrazine-naphthalimide, respectively. Delayed emission was observed for both luminophores with emission maxima for NAI-Q-MeOTPA and NAI-Py-MeOTPA of 746 and 749 nm in toluene, respectively. When encapsulated into glassy organic dots (g-Odots), the luminophores still maintained NIR emission with significant delayed lifetimes. The NAI-Q-MeOTPA and NAI-Py-MeOTPA g-Odots were probed for bioimaging in HeLa cells in which g-Odot uptake was observed and demonstrated bioimaging capability in the 630–740 nm imaging window.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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 teacher head, not a consensus.

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

Citations4
Published2025
Admission routes2
Has abstractyes

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