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Record W4307640273 · doi:10.1139/cjc-2022-0134

Theoretical studies of the excited electronic states of the molecule ScLi and its ions ScLi<sup>±</sup> with a feasibility study of laser cooling

2022· article· en· W4307640273 on OpenAlexvenueno aff
Sahar Kassem, Israa Zeid, Mahmoud Korek

Bibliographic record

VenueCanadian Journal of Chemistry · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRotational–vibrational spectroscopyChemistryAtomic physicsExcited stateDipoleBond-dissociation energyPotential energyMolecular electronic transitionLaser coolingIonDissociation (chemistry)LaserPhysicsQuantum mechanicsPhysical chemistry

Abstract

fetched live from OpenAlex

For the transition metal lithides ScLi and ScLi±, the adiabatic potential energy and the static and transition dipole moment curves of the low-lying electronic states in the representation 2 s+1Λ(+/−) have been investigated. The spectroscopic constants, the electronic transition energy with respect to the ground state Te, the internuclear distance Re, the harmonic frequency ωe, the rotational constant Be, the permanent dipole moment µe, and the dissociation energies De have been computed for the bound and excited states. Using the canonical function approach, these calculations have been followed by a rovibrational calculation from which the rovibrational constants Ev, Bv, and Dv and the abscissas of the turning points Rmin and Rmax for the investigated bound states are calculated. A feasibility study of laser cooling of ScLi and its ions ScLi± has been done. New 62 electronic states have been investigated in the present work for the first time. No useful cooling scheme was found for those molecules.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.240
Teacher spread0.230 · 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 designSimulation or modeling
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

Citations1
Published2022
Admission routes1
Has abstractyes

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