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Record W2513960358 · doi:10.15278/isms.2016.wj06

DEVELOPMENT OF A NEW DETECTION SCHEME TO PROBE PREDISSOCIATED LEVELS OF THE S1 STATE OF ACETYLENE

2016· article· en· W2513960358 on OpenAlexaff
Jun Jiang, Robert W. Field, A. J. Merer, Trevor J. Erickson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAcetyleneState (computer science)Scheme (mathematics)Computer scienceChemistryMathematicsAlgorithmOrganic chemistryMathematical analysis

Abstract

fetched live from OpenAlex

A new spectroscopic scheme has been developed to probe the predissociated levels of the S$_1$ state of acetylene. Our new scheme is based on detection of visible fluorescence that is a result of multi-photon excitation of acetylene (resonantly through single rovibronic S$_1$ levels). The new detection scheme is not subject to decreases in fluorescence quantum yield of S$_1$ levels that lie above the predissociation limit, and laser scatter-light can be easily eliminated by a long-pass filter with a cutoff in the visible range. For the S$_1$ predissociated levels, the new detection scheme offers much improved signal-to-noise ratio compared to the conventional laser-induced fluorescence technique, based on detection of UV fluorescence from the S$_1$ levels. The new method is also easier to implement than various H-atom detection schemes, which involve one additional laser of different wavelength than the excitation wavelength. Based on the power dependence and lifetime of the fluorescence signals, electronically excited \\chem{C_2H} and/or \\chem{C_2} fragments are the likely emitters of the detected visible fluorescence. The new method is currently being used to extend the vibrational and rotational assignments of both gerade and ungerade levels of the S$_1$ state of acetylene in the region of the $cis$-$trans$ isomerization barrier, $>$1000 cm$^{-1}$ above the onset of S$_1$ predissociation.

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 score0.078
Threshold uncertainty score0.173

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 head0.018
GPT teacher head0.221
Teacher spread0.204 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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