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Record W4401798120 · doi:10.1088/1402-4896/ad7352

Multilayer film coating for laser ion source target for increase of low charge state production

2024· article· en· W4401798120 on OpenAlexaff
Giovanni Ceccio, Shunsuke Ikeda, Takeshi Kanesue, Antonino Cannavò, M. Cutroneo, Pavel Pleskunov, Kazumasa Takahashi, M. Okamura

Bibliographic record

VenuePhysica Scripta · 2024
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsPolytechnique Montréal
FundersU.S. Department of Energy
KeywordsMaterials scienceCoatingCharge (physics)LaserIonProduction (economics)OptoelectronicsAtomic physicsEngineering physicsNanotechnologyOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract The use of Laser Ion Source for accelerator facilities has the advantage to tune the characteristic of the produced beams by changing the laser parameters using the same primary target. The advantageous and innovative opportunity to manipulate the characteristic of charge state distributions by the use of composed target, may open new possibilities for the ion sources. In this experiment we characterize and study the plasma produced by the laser ablation of coated targets at constant laser parameters. The performed investigation has the double purpose to have a better understanding of penetration depth of laser in composed materials and understand how to tune the charge states by adding coating films. The obtained results showed that for particular thickness of coating, the low charge states were produced with higher yield than in the case of pure material.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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