Revealing degradation mechanisms in YSZ ceramics through machine learning-guided aging and multiscale characterization

TL;DR

This paper combines machine learning-guided aging with multiscale analysis to uncover the two-stage degradation process in YSZ ceramics, emphasizing the role of grain boundary chemistry in low-temperature degradation.

Contribution

It introduces a novel approach integrating machine learning with multiscale characterization to elucidate degradation mechanisms in YSZ ceramics.

Findings

Degradation involves two stages: initial surface relief and subsequent microstructural refinement.

Triple-junction grain boundaries act as hotspots accelerating degradation.

Grain boundary chemistry, not just size, is key to low-temperature degradation.

Abstract

The long-term performance of yttria-stabilized zirconia (YSZ) based energy and biomedical devices is compromised by low-temperature degradation (LTD). This study presents a novel integration of machine learning-guided hydrothermal aging with multiscale characterization to resolve a two-stage degradation mechanism in 3 mol% YSZ. Stage 1 (0 to 30 hrs) features initial surface relief building, which transitions to partial refinement and relief distribution in stage 2 (30 to 60 hrs), alongside a rise in monoclinic phase content. The evolving microstructure increases triple-junction grain boundary density, and these junctions act as degradation hotspots, where vacancy exchange and water access accelerate the transformation. These findings highlight grain boundary chemistry, rather than grain size alone, as a key LTD driver, suggesting boundary engineering as a strategy to enhance YSZ stability for energy, biomedical, and thermal applications.