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Record W2014443598 · doi:10.1088/0953-4075/36/19/010

Interpreting the dynamics of HCl<sup>+</sup>dissociation in a strong laser field at   = 10.3 µm

2003· article· en· W2014443598 on OpenAlexaff
Jeffrey T. Paci, David M. Wardlaw, André D. Bandrauk

Bibliographic record

VenueJournal of Physics B Atomic Molecular and Optical Physics · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversité de SherbrookeQueen's University
Fundersnot available
KeywordsDissociation (chemistry)Potential energyExcited stateQuantum tunnellingKinetic energyWave packetMolecular dynamicsAtomic physicsQuantumPhotodissociationChemistryPotential fieldGround statePhysicsQuantum mechanicsPhysical chemistry

Abstract

fetched live from OpenAlex

The strong field dissociation of HCl+ at a wavelength of λ = 10.3 µm is examined in detail, using quantum wavepacket and trajectory methods. A three-potential-curve treatment, which currently offers the most theoretically complete description of the molecular dynamics, is used to simulate the dissociation process, and provides predictions of the dissociation probability, electronic branching ratios and kinetic energy distributions. Classical trajectory simulations on a single potential curve (ground state of HCl+) are used to increase understanding of simulations performed using three potential curves. The impact on predicted product properties of using two potential curves (ground and first excited states) versus three potential curves is also explored. The fragment's kinetic energy distributions are discussed extensively and quantum simulation results interpreted with the aid of classical trajectories as well as simple models such as a wagging potential tail model, developed by Thachuk and Wardlaw (1995 J. Chem. Phys. 102 7462). A tunnelling variant of a barrier suppression model, used for predicting the dissociation threshold, is developed, and shown to be more accurate than previous models.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.455

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 head0.005
GPT teacher head0.246
Teacher spread0.241 · 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 designTheoretical or conceptual
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

Citations13
Published2003
Admission routes1
Has abstractyes

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