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Record W2044391751 · doi:10.1021/jp907662z

Neutral Dissociation of Superexcited Oxygen Molecules in Intense Laser Fields

2009· article· en· W2044391751 on OpenAlexaff
Di Song, A. Azarm, Y. Kamali, Kai Liu, Andong Xia, Yoshiaki Teranishi, Sheng-Hsien Lin, Fanao Kong, See Leang Chin

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDissociation (chemistry)OxygenAtomic physicsLaserMoleculeChemical physicsChemistryMaterials sciencePhotochemistryPhysicsOpticsPhysical chemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Superexcited states (SESs) of oxygen molecules and their neutral dissociation processes have been studied both experimentally and theoretically using intense femtosecond laser. We find that at the laser intensity of approximately 2 x 10(14) W/cm(2), ultrashort laser pulse causes neutral dissociation of oxygen molecule by way of SESs. The dissociation products are the excited neutral oxygen atoms, which are observed through fluorescence spectroscopy. Laser power dependence of the fluorescence intensity shows that each molecule effectively absorbs an average of ten laser photons. The total energy absorbed is sufficient to stimulate the molecule to many of the SESs. The effect is equivalent to single photon excitation in the extreme-ultraviolet (XUV) region by synchrotron radiation (SR). Morse potential energy curves (PECs) are constructed for the SESs of O(2) molecules. In light of the PECs, predissociation mechanism is proposed for the neutral dissociation. Quasi-classical trajectory (QCT) calculations show that the predissociation time is as short as 100 fs, which is consistent with our experimental measurement using ultrafast pump-probe technique.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
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 head0.006
GPT teacher head0.255
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2009
Admission routes1
Has abstractyes

Explore more

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