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Record W4200133765 · doi:10.3390/coatings11121506

Recent Advances in Hollow Cathode Technology for Plasma-Enhanced ALD—Plasma Surface Modifications for Aluminum and Stainless-Steel Cathodes

2021· article· en· W4200133765 on OpenAlexfundno aff
Kenneth Butcher, Vasil Georgiev, Dimka Georgieva

Bibliographic record

VenueCoatings · 2021
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsCathodeMaterials sciencePlasmaSurface modificationOxideAluminiumLangmuir probeWork functionHot cathodeSputteringCathodic arc depositionLayer (electronics)Chemical engineeringMetallurgyNanotechnologyElectrodeThin filmChemistryCathodic protectionPlasma diagnosticsElectrochemistry

Abstract

fetched live from OpenAlex

Recent designs have allowed hollow cathode gas plasma sources to be adopted for use in plasma-enhanced atomic layer deposition with the benefit of lower oxygen contamination for non-oxide films (a brief review of this is provided). From a design perspective, the cathode metal is of particular interest since—for a given set of conditions—the metal work function should determine the density of electron emission that drives the hollow cathode effect. However, we found that relatively rapid surface modification of the metal cathodes in the first hour or more of operation has a stronger influence. Langmuir probe measurements and hollow cathode electrical characteristics were used to study nitrogen and oxygen plasma surface modification of aluminum and stainless-steel hollow cathodes. It was found that the nitridation and oxidation of these metal cathodes resulted in higher plasma densities, in some cases by more than an order of magnitude, and a wider range of pressure operation. Moreover, it was initially thought that the use of aluminum cathodes would not be practical for gas plasma applications, as aluminum is extremely soft and susceptible to sputtering; however, it was found that oxide and nitride modification of the surface could protect the cathodes from such problems, possibly making them viable.

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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.271
Teacher spread0.248 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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