| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
2023 Undergraduate Student Poster Contest
|
| Presentation Title |
Phase and Atomic Occupancy Stability Analysis of Double Transition Metal MAX Phases: A Step Toward Machine Learning Discovery of New 2D Materials |
| Author(s) |
Bethany G. Wright, Brian C. Wyatt, Babak Anasori |
| On-Site Speaker (Planned) |
Bethany G. Wright |
| Abstract Scope |
Double transition metal (DTM) MXenes are members of a large family of two-dimensional layered materials with properties such as electrical conductivity and mechanical behavior that are tailorable to various applications. Generally, MXenes are synthesized from bulk ceramics called MAX phases. DTM MAX phase stability is understudied compared to MXenes, but is critical for optimizing synthesis conditions with machine learning. This study explored DTM Mn+1AlCn (n=1-3) MAX phases by preparing them in six molar ratios of transition metals before firing at temperatures of 1400 ˚C, 1500 ˚C, or 1600 ˚C. We determined phase stability across these experimental variables with X-ray diffraction and transition metal atomic occupancies with Rietveld. Select DTM MXenes were synthesized and characterized with scanning electron microscopy, XRD, and measurement of film electrical conductivity. By expanding knowledge of DTM MAX phase stability and its effect on DTM MXenes, we are enabling machine learning-guided synthesis of tuned DTM MXenes. |