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Record W4402905423 · doi:10.1016/j.jmrt.2024.09.213

Characterization of thin oxide layers formed by Ti-Nb alloy anodization

2024· article· en· W4402905423 on OpenAlexaff
Rildo Ribeiro dos Santos, Carlos Nelson Elias, Elivelton Alves Ferreira, J. A. O. Huguenin, E. E. Farias, Michele Lemos de Souza, Ladário da Silva

Bibliographic record

VenueJournal of Materials Research and Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversity of Victoria
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFinanciadora de Estudos e ProjetosCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMaterials scienceAnodizingCharacterization (materials science)AlloyOxideMetallurgyThin filmNanotechnologyAluminium

Abstract

fetched live from OpenAlex

Ti-Nb alloys are gaining more prominence in the industrial and scientific environment due to their high biocompatibility, mechanical strength/weight ratio, and exceptional corrosion resistance compared to other metallic materials. Metals exhibiting this passivation characteristic are known as metal valves and may exhibit semiconductive or insulating oxides. Controlling the growth of distinct thickness layer films is possible through tunning anodization conditions. In this work, a Ti-Nb binary system is anodized, and the grown oxide layer is comprehensively characterized using ellipsometry based on its optical properties and thickness at different applied potentials. Confocal Microscopy assessed their surface roughness, and SEM/EDS probed the surface material's elemental composition. CF-LIBS successfully confirmed the 70/30 composition of the Ti-Nb alloy. Raman spectra have detected the presence of amorphous TiO 2 and Nb 2 O 5 at potentials 80 V, 100 V, and 120 V. Finally, a linear dependence of the oxide thickness with the applied potential was observed.

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 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)0.001
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 score0.010
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.272
Teacher spread0.251 · 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 teacher head, 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

Citations8
Published2024
Admission routes1
Has abstractyes

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