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Effect of Hybrid Alumina/Aluminium Foil Dome Diaphragms on Sound Performance of Loudspeaker

2019· article· en· W2967159204 on OpenAlexaff
Christian Zung, Ran Cai, I.A. Levitsky, Xueyuan Nie

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceAluminiumAluminium foilCoatingComposite materialCorrosionLayer (electronics)MetallurgyAcoustics

Abstract

fetched live from OpenAlex

Abstract Plasma electrolytic oxidation (PEO) technology enables to fabricate a nanostructured ceramic top layer on an aluminium foil, called hybrid alumina/aluminium material, which has the enhanced properties like hardness, stiffness, wear resistance and corrosion resistance. In this work, PEO nanostructured coatings were applied to aluminium speaker domes to study what effects PEO coatings could offer. A set of treated domes were processed to have a thin coating and the other processed to have a thicker coating. Afterwards, the speaker domes were conducted with several tests and measurements. From the frequency range of a low frequency (200 Hz to 5 kHz) there was very little downside to adding the coating, only at middle frequencies (5 kHz to 12 kHz) a slight decrease in sensitivity can be found. In high range frequencies (12 kHz to 14 kHz) the sensitivity in dampening and sound distortion resistance was increased. In general, these qualities should lead to a better, cleaner sound quality reproduction.

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 score0.005

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)0.0010.000

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 head0.007
GPT teacher head0.204
Teacher spread0.196 · 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
Published2019
Admission routes1
Has abstractyes

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