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Record W2560293435 · doi:10.1021/acs.jpclett.6b02613

Identifying Strong-Field Effects in Indirect Photofragmentation Reactions

2016· article· en· W2560293435 on OpenAlexaff
Chuan‐Cun Shu, Kaijun Yuan, Daoyi Dong, Ian R. Petersen, André D. Bandrauk

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversité de Sherbrooke
FundersAustralian Research CouncilUniversity of New South Wales
KeywordsField (mathematics)BreakupChemical physicsAngular momentumWave packetChemistryPhotodissociationQuantumPhysicsAtomic physicsMolecular physicsPhotochemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Exploring molecular breakup processes induced by light-matter interactions has both fundamental and practical implications. However, it remains a challenge to elucidate the underlying reaction mechanism in the strong field regime, where the potentials of the reactant are modified dramatically. Here we perform a theoretical analysis combined with a time-dependent wavepacket calculation to show how a strong ultrafast laser field affects the photofragment products. As an example, we examine the photochemical reaction of breaking up the molecule NaI into the neutral atoms Na and I, which due to inherent nonadiabatic couplings are indirectly formed in a stepwise fashion via the reaction intermediate NaI*. By analyzing the angular dependencies of fragment distributions, we are able to identify the reaction intermediate NaI* from the weak to the strong field-induced nonadiabatic regimes. Furthermore, the energy levels of NaI* can be extracted from the quantum interference patterns of the transient photofragment momentum distribution.

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 score0.006
Threshold uncertainty score0.221

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 head0.010
GPT teacher head0.271
Teacher spread0.262 · 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

Citations64
Published2016
Admission routes1
Has abstractyes

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