MétaCan
Menu
Back to cohort
Record W4206191942 · doi:10.1039/d1cp04264e

Electronic relaxation and dissociation dynamics in formaldehyde: pump wavelength dependence

2021· article· en· W4206191942 on OpenAlexafffund
Tomoyuki Endo, Simon P. Neville, Philippe Lassonde, Chen Qu, Hikaru Fujise, Mizuho Fushitani, Akiyoshi Hishikawa, Paul L. Houston, Joel M. Bowman, François Légaré, Michael S. Schuurman, Heide Ibrahim

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversity of OttawaNational Research Council CanadaInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaNagoya UniversityFonds de recherche du Québec – Nature et technologiesNuclear Safety and Security CommissionCanada Foundation for InnovationH2020 European Research CouncilJapan Society for the Promotion of ScienceNational Aeronautics and Space Administration
KeywordsDissociation (chemistry)Excited stateWavelengthAb initioChemistryAtomic physicsRelaxation (psychology)Ab initio quantum chemistry methodsVibrational energy relaxationFormaldehydeMolecular physicsMaterials sciencePhysical chemistryMoleculePhysicsOptoelectronics

Abstract

fetched live from OpenAlex

simulation and experimental results, that the rate of excited state decay and subsequent dissociation displays a prominent dependence on which vibronic transition in the absorption band is prepared by the pump. Our simulations predict that nonadiabatic transition rates and dissociation yields will increase by a factor of >100 as the pump wavelength is decreased from 337 to 304 nm. The experimental results and theoretical simulations are in broad agreement and both indicate that the dissociation rate plateaus rapidly after ≈2 ps following an ultrafast sub-ps rise.

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 score0.061
Threshold uncertainty score0.784

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 head0.239
Teacher spread0.234 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

Explore more

Same venuePhysical Chemistry Chemical PhysicsSame topicLaser-Matter Interactions and ApplicationsFrench-language works237,207