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Record W4413888356 · doi:10.1039/d5tb00890e

Optimizing bio-imaging with computationally designed polymer nanoparticles

2025· article· en· W4413888356 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Materials Chemistry B · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsUniversity of Waterloo
FundersAlliance de recherche numérique du CanadaUniversity of Waterloo
KeywordsMaterials scienceNanoparticlePolymerNanotechnologyComposite material

Abstract

fetched live from OpenAlex

the experimental value of 450.00 nm). TD-DFT analysis of selected PPE dimer chains revealed strong fluorescence, characterized by high oscillator strengths (2.689-4.004) and large Stokes shifts (134.51-156.31 nm), which minimize spectral overlap and improve imaging resolution. Highest occupied molecular orbitals (HOMO)-lowest unoccupied molecular orbitals (LUMO) orbital analysis confirmed that π → π* transitions dominate (>90%), indicating efficient electronic behavior. These results reinforce the potential of PPE-NPs as effective fluorescent probes for bio-imaging, supported by a reliable computational approach for designing future CPNs. By comparing computational predictions with experimental data, this study contributes to the development of customized nanomaterials for biomedical applications.

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.

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 categoriesInsufficient 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 score0.028
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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