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Record W4388564788 · doi:10.1111/iwj.14475

Effect of a Novel sputtering process on the chemical and biological properties of <scp>silver‐gold</scp> alloys

2023· article· en· W4388564788 on OpenAlexafffund
Keeley Kathryn Anne Hatch, Robert Burrell, C. Ward

Bibliographic record

VenueInternational Wound Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesUniversity of Alberta
KeywordsMedicineSputteringProcess (computing)MetallurgyNanotechnologyComputational biologyMaterials scienceThin film

Abstract

fetched live from OpenAlex

Silver-gold nanocrystalline films were sputtered on HDPE substrates by a physical vapour deposition process using alloys with a nominal composition of 65% silver/35% gold or 35% silver/65% gold by weight, with comparison to a 100% silver target. Novel process conditions were introduced to include both water and oxygen as reactive gases. X-ray diffraction and chemical digests were used to assess the structure and chemical composition of the films. Log reductions and corrected zone of inhibition tests were used to measure the biological properties. Despite a range of physical and chemical properties, biological tests showed that the bactericidal properties of all silver-gold films were comparable with silver-only films in the short term and 65% silver films made with Novel sputtering conditions had comparable bacteriostatic abilities to silver-only over a 7-day period. The benefit of including gold may be seen in future studies of anti-inflammatory activity.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.021
GPT teacher head0.258
Teacher spread0.236 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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Same venueInternational Wound JournalSame topicIon-surface interactions and analysisFrench-language works237,207