Conference Logo ProgramMaster Logo
Conference Tools for MS&T26: Materials Science & Technology
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools

About this Abstract

Meeting MS&T26: Materials Science & Technology
Symposium Uncertainty Quantification in Ultra-High Temperature Materials Manufacturing
Presentation Title MXene and Polymer Derived TiC–SiC Ceramics with Enhanced Electrical Conductivity and Tailored Thermal–Mechanical Performance for High-Temperature Applications
Author(s) Kathy Lu, Mubina Shaik, Kishore Behera
On-Site Speaker (Planned) Kathy Lu
Abstract Scope TiC–SiC ceramic composites were synthesized via incorporation of Ti₃C₂ MXene into allylhydridopolycarbosilane (SMP-10), followed by pyrolysis and spark plasma sintering (SPS) at 2000 °C. Phase analysis reveals the formation of β-SiC, substoichiometric TiCₓ phases, and turbostratic carbon. The incorporation of Ti₃C₂ leads to a dramatic enhancement in electrical conductivity, reaching 645 S·cm⁻¹ at 1000 °C for the 10TiC–SiC composition—approximately two orders of magnitude higher than that of pure SMP-10-derived SiC and exceeding values reported for other polymer-derived ceramics (PDCs). Thermal conductivity decreases with increasing temperature for all compositions. Hardness and elastic modulus increase systematically with TiC content, reflecting the evolving composite microstructure. In addition, high-temperature oxidation studies demonstrate good phase stability with limited microstructural degradation. These findings suggest that Ti₃C₂-derived TiCₓ and carbon networks enable simultaneous enhancement of electrical conductivity and controlled thermal transport while maintaining robust mechanical properties and oxidation resistance.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Autonomous Materials Characterization Through Simulation to Experiment Analysis with Continual Deep Learning
Bayesian Design of Experiments for Calphad Modeling
Computational Tools for Predicting High-Temperature Materials Properties via DFT, MD, and Deep Learning
Data to Discovery: A Closed-Loop Ecosystem for Designing Compositionally Complex Alloys
End-to-End Machine Learning for Creep Modeling: Data Processing, Parameter Learning, and Uncertainty Analysis
From Design to Melt: Rare Earth Retention in Ni-Based Superalloys
From Dirty Processing to Enhanced Performance: Hidden Variables for Strength Consistency in UHTCs
Generalization of a Crystal Plasticity Model from Grade 91 to Grade 92 Steel: A Coupled High-Throughput Constitutive Model and Data-Driven Analysis Approach
MXene and Polymer Derived TiC–SiC Ceramics with Enhanced Electrical Conductivity and Tailored Thermal–Mechanical Performance for High-Temperature Applications
UHTM and the Materials R&D Landscape
Uncertainty-Guided Experimental Determination of Phase Diagrams
Uncertainty Quantification of In-Situ Densification of Polymer-Derived Ceramics
Uncertainty Quantification via Deep Kernel Learning on Synchrotron Diffraction Patterns

Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement