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Record W2101428634 · doi:10.1351/pac200173030503

Luminescent sensors and photonic switches

2001· article· en· W2101428634 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePure and Applied Chemistry · 2001
Typearticle
Languageen
FieldMaterials Science
TopicPhotochromic and Fluorescence Chemistry
Canadian institutionsnot available
FundersEuropean Social FundEngineering and Physical Sciences Research CouncilQueen's University
KeywordsLuminescenceChemistryPhotonicsExcited stateLanthanideNanotechnologyPhotoinduced electron transferMolecular switchScale (ratio)Electron transferMoleculeOptoelectronicsPhotochemistryPhysicsMaterials scienceIon

Abstract

fetched live from OpenAlex

Abstract The principles of photochemistry continue to fuel progress in luminescent sensors and photonic switches. Examples of sensors based on photoinduced electron transfer (PET) are discussed, including those which form the basis of successful systems used in physiology and medicine. More complex formats usually involve multiple receptors. One progression takes us to lanthanide complexes enabled with sensory capabilities. Another path takes us to molecular-scale implementation of logic gates such as AND and INHIBIT. Such luminescent switches can be enriched by combination with nonluminescent cousins. The latter are based on internal charge-transfer excited states (ICT). An example of rudimentary arithmetic at the molecular scale is presented by running a luminescent AND gate in parallel with a nonluminescent XOR gate. Thus, small molecules can process small numbers for the first time outside of our brains.

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)0.000
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 score0.015
Threshold uncertainty score0.633

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 science0.0000.000
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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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