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Record W2573027055 · doi:10.1002/crat.201600240

Sputtering and amorphization of crystalline semiconductors by Nanodroplet Bombardment

2017· article· en· W2573027055 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCrystal Research and Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsUniversity of Waterloo
FundersGovernment of Jiangsu ProvinceMinistry of FinanceMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsSputteringAmorphous solidMaterials scienceEtching (microfabrication)EvaporationChemical physicsKinetic energyQuenching (fluorescence)NanotechnologyChemistryThin filmLayer (electronics)CrystallographyOpticsFluorescence

Abstract

fetched live from OpenAlex

In this review we expose how Nanodroplet Bombardment of surfaces by charged particles produced through electrospray atomization offers unparalleled opportunities for surface engineering of chemically inert crystalline materials. The sputtering yields and rates are comparable or higher than reactive etching techniques and significantly higher than other physical sputtering systems. Moreover, bombardment can amorphatize a thin layer of the target. The imposed physical characteristics of the electrospray, droplet diameter, molecular mass of the spray, and kinetic energy will determine the sputtering and amorphization efficiency, and the topography of the processed target. Molecular dynamics studies have clarified the mechanisms of both processes; amorphous layers appear due to ultra‐fast quenching of melted target pools around the impact area while sputtering is driven by a combination of collision cascades, thermal evaporation, and, for large and fast projectiles, of hydrodynamic forces.

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.

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 score0.103
Threshold uncertainty score0.291

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 head0.023
GPT teacher head0.303
Teacher spread0.280 · 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