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Record W4407941666 · doi:10.1016/j.jmrt.2025.02.216

A new understanding on the electrochemical coloration mechanism of titanium surface

2025· article· en· W4407941666 on OpenAlexaff
Tengfei Yu, Yanpeng Xue, Man Zheng, Benli Luan

Bibliographic record

VenueJournal of Materials Research and Technology · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsWestern University
FundersFundamental Research Funds for the Central Universities
KeywordsMaterials scienceElectrochemistryTitaniumMechanism (biology)Surface (topology)MetallurgyNanotechnologyChemical engineeringElectrodePhysical chemistryGeometry

Abstract

fetched live from OpenAlex

Electrochemical coloration technology has been extensively studied for many years due to its effectiveness in enhancing the aesthetics, corrosion resistance, and biocompatibility of Ti materials. However, the underlying coloration mechanism has not been systematically investigated and verified. Our new finding based on spectrophotometer characterization reveals that the color of oxide film is determined by the diffuse reflection light. The coloration mechanism, attributed to the selective absorption of visible light by the TiO x semiconductor film, is therefore proposed and confirmed. This absorption originates from electron transitions from the impurity levels to the conduction band. Oxide films of various colors exhibit particular absorption peaks, along with specific flat band potentials and charge carrier densities. The energy differences between the conduction band minimum and the Fermi level ( E C - E F ) of oxide films with orange, red, blue, and green colors are 1.77 eV, 1.50 eV, 1.25 eV, and 0.76 eV, respectively, corresponding to the redshift of their visible light absorption peaks. This new discovery provides a new understanding of Ti coloration mechanism and promotes the development of related processes and potential applications.

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 score0.065
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.064
GPT teacher head0.342
Teacher spread0.278 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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