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Record W2012723890 · doi:10.1021/ed100285w

Excited-State Processes in Slow Motion: An Experiment in the Undergraduate Laboratory

2010· article· en· W2012723890 on OpenAlexaff
William C. Galley, Oleh M. Tanchak, Kevin G. Yager, Grażyna Wilczek-Vera

Bibliographic record

VenueJournal of Chemical Education · 2010
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhosphorescenceChemistryExcited stateLaserRuby laserQuenching (fluorescence)SpectroscopyEngineering physicsNanotechnologyAtomic physicsFluorescenceMaterials scienceOpticsPhysics

Abstract

fetched live from OpenAlex

Lasers have transformed chemistry and the everyday world. Therefore, it is not surprising that undergraduate chemistry students are frequently exposed to fairly advanced laser techniques. The usual topics studied with lasers are molecular spectroscopy and chemical kinetics. Static and dynamic fluorescence experiments seem to be particularly popular. The phenomenon of phosphorescence, on the other had, has received much less attention in the undergraduate physical chemistry laboratory. A few years ago, we developed a laser experiment that introduced students to the phosphorescence quenching of the carbazole−naphthalene system under steady-state and pulse-excitation conditions. The purpose of that experiment was to demonstrate the experimental and theoretical aspects of triplet−triplet nonradiative energy transfer between two aromatic molecules. Subsequently, we modernized the experiment by introducing an inexpensive and easy-to-use Ocean Optics data acquisition system. This not only greatly simplified the experiment, but also made it more accessible to students. This article presents the upgraded version of the experiment together with suggestions for practical implementation.

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 score0.003
Threshold uncertainty score0.309

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 integrity0.0000.001
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 head0.008
GPT teacher head0.280
Teacher spread0.272 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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