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Record W4401420358 · doi:10.1364/ao.530314

Characteristics of an ion beam in the figuring process on an optical ultra-low expansion glass surface

2024· article· en· W4401420358 on OpenAlexaff
Hsing-Yu Wu, Li-Siang Shen, Shaorong Huang, Wen‐Wei Lin, Li‐Jen Hsiao, Ching-Ling Cheng, Guoyu Yu, Yung-Shin Sun, Jin–Cherng Hsu

Bibliographic record

VenueApplied Optics · 2024
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsMD Precision (Canada)
FundersNational Science and Technology Council
KeywordsFiguringMaterials scienceOpticsIon beamBeam (structure)Full width at half maximumIonOptoelectronicsChemistryPhysics

Abstract

fetched live from OpenAlex

In this study, an ion source figured out the surface of a glass-ceramic material with an ultra-low thermal expansion coefficient for space optical elements. The investigation of the single-point, line, and square figuring patterns assessed the detailed characteristics of the ion beam. At a fixed ion beam current and processing time, a beam voltage of 600 V led to the greatest removal depth with the narrowest full width at half-maximum (FWHM). The surface roughness under different beam voltages was also examined and discussed. Line figuring with an ion beam voltage of 600 V and a one-dimensional sample moving speed of 0.25 mm/s exhibited a maximum depth removal rate of 19.71 nm/min after being polished 15 times. Two-dimensional square figuring was performed to polish a plane mirror with a diameter of 60 mm, and it successfully reduced its surface’s peak-to-valley value to 18 nm due to the melting heat phenomenon of the glass-ceramic material in ion beam figuring (IBF).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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