Ti-6Al-4V β Phase Selective Dissolution: In Vitro Mechanism and Prediction

Author

Michael A KurtzFollow

Date of Award

12-2023

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Bioengineering

Committee Chair/Advisor

Jeremy L. Gilbert, PhD

Committee Member

Martine LaBerge, PhD

Committee Member

John D. DesJardins PhD

Committee Member

Brian C. Dean, PhD

Abstract

Retrieval studies document Ti-6Al-4V β phase dissolution within total hip replacement systems. A gap persists in our mechanistic understanding and existing standards fail to reproduce this damage. This thesis aims to (1) elucidate the Ti-6Al-4V selective dissolution mechanism as functions of solution chemistry, electrode potential and temperature; (2) investigate the effects of adverse electrochemical conditions on additively manufactured (AM) titanium alloys and (3) apply machine learning to predict the Ti-6Al-4V dissolution state. We hypothesized that (1) cathodic activation and inflammatory species (H₂O₂) would degrade the Ti-6Al-4V oxide, promoting dissolution; (2) AM Ti-6Al-4V selective dissolution would occur and (3) near field electrochemical impedance spectra (nEIS) would distinguish between dissolved and polished Ti-6Al-4V, allowing for deep neural network prediction. First, we show a combinatorial effect of cathodic activation and inflammatory species, degrading the oxide film’s polarization resistance (R_p) by a factor of 10⁵ Ωcm² (p = 0.000) and inducing selective dissolution. Next, we establish a potential range (-0.3 V to –1 V) where inflammatory species, cathodic activation and increasing solution temperatures (24 ^oC to 55 ^oC) synergistically affect the oxide film. Then, we evaluate the effect of solution temperature on the dissolution rate, documenting a logarithmic dependence. In our second aim, we show decreased AM Ti-6Al-4V R_p when compared with AM Ti-29Nb-21Zr in H₂O₂. AM Ti-6Al-4V oxide degradation preceded pit nucleation in the β phase. Finally, in our third aim, we identified gaps in the application of artificial intelligence to metallic biomaterial corrosion. With an input of nEIS spectra, a deep neural network predicted the surface dissolution state with 96% accuracy. In total, these results support the inclusion of inflammatory species and cathodic activation in pre-clinical titanium devices and biomaterial testing.

Recommended Citation

A Kurtz, Michael, "Ti-6Al-4V β Phase Selective Dissolution: In Vitro Mechanism and Prediction" (2023). All Dissertations. 3486.
https://tigerprints.clemson.edu/all_dissertations/3486

Author ORCID Identifier

0000-0001-6195-579X