Self-Healing Materials for Extreme Environments

K.S. Raja – University of Idaho

Research Overview

Nuclear and power plant components are exposed to aggressive service conditions — high temperatures, high stresses, irradiation, and corrosion — which lead to damage accumulation and restricted service lifetime. If structural materials can self-heal microscopic damages induced by irradiation, creep, and other degradation mechanisms, their lifetime can be extended considerably.

In this research, metal-core oxide-shell (MCOS) nano-capsules will be prepared by controlled oxidation of nano-particles of low melting point metals such as Sn and Zn. Using a powder metallurgy approach, the MCOS nano-capsules will be distributed in a metal matrix (preferably low-carbon steel). Test samples will be prepared using different manufacturing techniques, and self-healing behavior will be demonstrated by conducting tensile testing at high temperatures.

Student Research Experience

Students will be trained in materials processing, advanced manufacturing, materials characterization, and materials testing. Processing techniques include controlled oxidation of nanomaterials, high-energy ball milling, and high-pressure compaction. Manufacturing processes include conventional sintering and laser powder bed fusion.

Material characterization techniques include optical and electron microscopy, and confocal Raman spectroscopy. Testing techniques are micro-hardness survey and tensile testing at different temperatures. Students will be trained by working with graduate students and faculty, will learn to critically analyze experimental data using a statistical approach, write reports, and participate in weekly research progress meetings.

Skills & Techniques

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  • AI/ML for Automated Critical Materials Resource Mapping

    Ahamed – INL R&D Staff Scientist

    Research Overview

    Efficient exploration and development of critical mineral resources increasingly depend on integrating large volumes of geologic and geophysical data. Traditional log‑analysis workflows for solution‑mined deposits require detailed interpretation of lithology, stratigraphic boundaries, fluid‑flow‑controlled alteration zones, and grade‑related signals. These processes are often labor‑intensive and subject to interpreter variability.

    This project seeks to apply modern machine learning methodologies to automate essential components of mineral resource mapping. Students will work with representative geophysical and lithologic datasets to:

    • Clean and preprocess well‑log data (e.g., resistivity, natural gamma, spontaneous potential)
    • Correct depth misalignments between lithologic and geophysical logs
    • Build ML models for lithology prediction, stratigraphic correlation, and geologic zonation analysis
    • Investigate pattern recognition methods to identify alteration fronts and mineralization indicators
    • Explore workflow expansion using physics‑informed ML or sparse sensing approaches to improve mapping under limited‑data conditions

    The overarching goal is to develop scalable data‑driven methods that enhance subsurface interpretation for critical materials and solution‑mining applications.

    Student Research Experience

    Students will engage in interdisciplinary work spanning geoscience, machine learning, and data engineering. Activities include:

    • Data Integration & Preprocessing – preparing and aligning multi‑source well‑log datasets, correcting sample‑lag effects, and constructing geologically meaningful features.
    • Machine Learning Model Development – implementing algorithms such as PCA, dynamic time warping, tree‑based models, gradient‑boosting methods, and neural networks to support automated interpretation.
    • Geologic Interpretation – learning how lithologic variation, stratigraphic architecture, and fluid‑alteration signatures relate to mineral resource distribution within solution‑mined systems.
    • Model Evaluation & Visualization – generating correlation panels, prediction plots, and zonation maps to assess machine‑learning results in a geologic context.

    Students will collaborate with INL staff and gain experience applying AI/ML techniques to real‑world subsurface problems relevant to critical materials, energy security, and advanced resource extraction technologies.

    Skills & Techniques