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Record W2395841620 · doi:10.1039/c6cc03864f

Using low-energy near infrared light and upconverting nanoparticles to trigger photoreactions within supramolecular assemblies

2016· article· en· W2395841620 on OpenAlexaff
Tuoqi Wu, Neil R. Branda

Bibliographic record

VenueChemical Communications · 2016
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence and Fluorescent Materials
Canadian institutionsBurnaby HospitalSimon Fraser University
Fundersnot available
KeywordsLanthanideSupramolecular chemistryChromophorePhoton upconversionNanoparticlePhotochemistryMaterials scienceUltravioletDopantInfraredVisible spectrumNanotechnologyOptoelectronicsChemistryLuminescenceDopingOpticsCrystal structureCrystallographyIonOrganic chemistry

Abstract

fetched live from OpenAlex

This overview highlights how the high-energy ultraviolet or visible light required to drive photochemical reactions can be overcome by integrating the chromophores into supramolecular structures containing upconverting nanoparticles with trivalent lanthanide dopants (such as Tm(3+) and Er(3+)). These nanoparticles are particularly interesting systems because they absorb multiple photons of near infrared light and convert them into higher-energy light which is emitted in the ultraviolet and visible regions of the electromagnetic spectrum. The upconverting nanoparticles effectively act as nanoscopic 'light bulbs', and in this way, less damaging near infrared light can be used to trigger photochemical reactions for use in imaging and small molecule release. Several examples of how this phenomenon is being used in photochemistry will be presented with the focus being on self-assembled supramolecular systems, some of which are being used in cells and small animals.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.285
Teacher spread0.248 · 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.

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

Citations26
Published2016
Admission routes1
Has abstractyes

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