Mechanism of Electrochemical Oxidation of Titanium Via Isotopic Labeling, Medium Energy Ion Scattering, Nuclear Reaction Profiling, in-Situ Rutherford Backscattering Spectrometry, and Electrochemical Impedance Spectroscopy
Bibliographic record
Abstract
The few nanometres of titanium oxide present on titanium and Ti alloy surfaces are responsible for protecting the underlying metal from oxidation and enabling its use in many high-performance applications. The oxide provides a hard, uniform, and thermodynamically stable protective coating on an otherwise soft and very reactive metal. Because of its passivating oxide film, titanium has found uses in biomedical implants, aerospace engineering, corrosive industrial piping, and other areas where high strength and low weight are required. Our project is aimed at understanding the atomistic mechanisms of TiO2 formation and growth. We have taken several different approaches to investigate the details of this process. In one approach, the electrochemical oxidation mechanism of titanium at applied potentials between 0 and 10 V vs the saturated calomel reference electrode (SCE) was examined for ultra-thin Ti films sputtered onto Si(001) substrates and exposed in-situ to H2 18O, and then anodized in D2 16O. The effects of this isotopic labeling procedure were studied using medium energy ion scattering (MEIS) and nuclear reaction profiling (NRP). Both MEIS and NRP results are consistent in showing that the titanium oxide layer is composed of two distinct regions (Ti16O2/Ti18O2/Ti/Si) for the entire range of the formation voltages (0–10 V vs SCE). The oxygen component of the outermost region consists entirely of 16O, and the 18O region is always adjacent to the Ti metal. No Ti or 18O loss into the electrolyte solution during anodization was detected. The oxide thickened linearly as a function of potential in the 0–10 V range vs SCE, with experimental anodization ratio of 24.5±0.6 Å V−1. Mott–Schottky (MS) analyses showed positive slopes, indicating formation of an n-type TiO2 semiconductor, with O vacancies (or Ti interstitials) as major charge carriers in the 0–10 V range. Charge carrier densities, ND = (0.8-5.0)×1021 cm−3 were calculated from Mott–Schottky analysis and were well within the range of results reported in the literature. We observed a decrease in the charge carrier densities above ∼4 V vs SCE, that can be connected to defect annihilation or minor modification in the structure (electrostatic annealing) of the growing TiO2 film. In a second approach, we are using Rutherford backscattering spectrometry (RBS) for elemental depth profiling during oxide growth to determine oxidation rates and the role of the anodization potential on the Ti oxide layer structure and morphology. RBS is a powerful ion beam-based analytical tool used to determine thickness at the nanometer scale and elemental depth distribution, making it an excellent choice for studying the thin oxide on titanium. A difficulty with using RBS for such studies has been that RBS must function under ultra-high vacuum, and this has made it incompatible with performing aqueous electrochemistry; therefore, RBS had to be performed as an ex situ analysis, like MEIS and NRP. We are overcoming this limitation by using a specially designed in-situ cell with an ion-permeable silicon nitride window to provide a barrier between the ultra-high vacuum (UHV) required to perform RBS and the aqueous electrolyte solution required for anodization. In this cell, the thin silicon nitride window is coated with titanium and functions as the working electrode when exposed to the aqueous electrolyte solution. RBS measurements are taken as the titanium metal is anodized to titanium oxide. RBS is then performed during in-situ anodization, to obtain information about the growth mechanism of titanium oxide. Our initial in-situ RBS results show a significant increase in the oxidation rate of titanium compared to equivalent ex-situ measurements, and we also observe spontaneous TiO2 film growth without applying an anodic polarization. These effects are likely generated by strongly oxidizing water radiolysis products (e.g., H2O2 or ·OH) formed by the action of high-energy He+ ions interacting with the electrolyte solution. This paper discusses our experimental methods and presents an overview of the results and conclusions of this work.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".