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Record W3093843349 · doi:10.1080/00268976.2020.1836411

Photoelectron angular distributions from resonant two-photon ionisation of adiabatically aligned naphthalene and aniline molecules

2020· article· en· W3093843349 on OpenAlexaff
Jacqueline Arlt, D. P. Singh, James O. Thompson, Adam S. Chatterley, Paul Hockett, Henrik Stapelfeldt, Katharine L. Reid

Bibliographic record

VenueMolecular Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsSteacie Institute for Molecular Sciences
FundersH2020 Marie Skłodowska-Curie ActionsEngineering and Physical Sciences Research CouncilEuropean Commission
KeywordsIonizationAnilineMoleculeAtomic physicsAnisotropyChemistryMolecular physicsMaterials scienceOpticsPhysicsIon

Abstract

fetched live from OpenAlex

Photoelectron images have been measured following the ionisation of aligned distributions of gas phase naphthalene and aniline molecules. Alignment in the adiabatic regime was achieved by interaction with a 100 ps infrared laser pulse, with ionisation achieved in a two-photon resonant scheme using a low intensity UV pulse of ∼6 ps duration. The resulting images are found to exhibit anisotropy that increases when the alignment pulse is present, with the aniline PADs peaking along the polarisation vector of the ionising light and the naphthalene PADs developing a characteristic four-lobed structure. Photoelectron angular distributions (PADs) that result from the ionisation of unaligned and fully aligned distributions of molecules are calculated using the ePolyScat ab initio suite and converted into two-dimensional photoelectron images. In the case of naphthalene excellent agreement is observed between experiment and the simulation for the fully aligned distribution, showing that the alignment step allows us to probe the molecular frame, but in the case of aniline it is clear that additional processes occur following the one-photon resonant step.

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 score0.306
Threshold uncertainty score0.973

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 head0.008
GPT teacher head0.236
Teacher spread0.227 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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