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Record W2470548731 · doi:10.1139/cjp-2016-0204

Laser gain calculations for soft X-ray and XUV radiation emitted from copper-like ions by electron collisional pumping

2016· article· en· W2470548731 on OpenAlexvenueno aff
Mohamed A. Sayed, S. H. Allam, Tharwat M. El-Sherbini

Bibliographic record

VenueCanadian Journal of Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsAtomic physicsIonizationIonExcited stateCollisional excitationElectron ionizationExcitationElectronPopulationLaserExtreme ultravioletWavelengthPopulation inversionRate equationRadiationOpticsNuclear physics

Abstract

fetched live from OpenAlex

Electron impact excitation rate coefficients, level population densities, and gain coefficients for six excited ions with Z = 51, 52, 53, 54, 55, 56 in the copper isoelectronic sequence have been calculated. The electron collisional excitation rate coefficients are calculated according to the analytical formulas of Vriens and Smeets. Fine structure energy levels, transition probabilities, and oscillator strengths needed in the calculations have been calculated using Cowan atomic structure code with relativistic corrections for [Ar]3d 10 nl with n = 4–7 and l = 0–6. The level population densities are calculated by solving the coupled rate equations involving 30 levels. Positive gain coefficients of the possible emitted lines are obtained at three selected electron temperatures, namely 1/4, 1/2, and 3/4 of the ionization energy. The present calculated data show promising values for the production of soft X-ray and XUV laser by collisional pumping for the transitions 5p–5s and 6d–5f with wavelengths between 108 and 571 Å. The values of the maximum gain coefficient are found to increase with atomic number and their order of magnitude ranges from10 2 to 10 4 cm −1 .

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.445

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 head0.007
GPT teacher head0.220
Teacher spread0.214 · 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 designNot applicable
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

Citations2
Published2016
Admission routes1
Has abstractyes

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