📅 Day 1: MXenes, Nanohybrids, and Energy Storage Research Trends
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- Introduction to MXenes and MXene-derived nanohybrids
- Why MXenes are important for batteries and supercapacitors
- Structure-property relationship in 2D nanomaterials
- Surface terminations, functional groups, defects, and intercalation
- Current research trends from MDPI-based literature
- Role of DFT in predicting material stability and performance
🛠️ Hands-on Activity
MDPI Research Trend Mining using Google Colab
Participants will use Python to analyze MXene-related research titles/keywords and identify trending themes such as DFT, batteries, supercapacitors, adsorption, ion diffusion, and surface functionalization.
📅 Day 2: DFT-Based Electronic Structure Analysis of MXene Nanohybrids
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- Basics of Density Functional Theory for material researchers
- Geometry optimization and total energy calculation
- Band structure and density of states analysis
- Charge density, charge transfer, and electronic conductivity
- Adsorption energy and ion interaction in MXene surfaces
- Understanding Li, Na, Zn, and H ion storage behavior
🛠️ Hands-on Activity
Electronic Structure Visualization in Google Colab
Participants will plot and interpret sample DOS, band structure, and adsorption energy data of MXene-based materials using Python.
📅 Day 3: Optimization of MXene-Derived Nanohybrids for High-Performance Energy Storage
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- Material design strategies for improving conductivity and stability
- Doping, defect engineering, and surface functionalization
- MXene-based nanohybrids for Li-ion, Na-ion, Zn-ion batteries, and supercapacitors
- Linking DFT descriptors with energy storage performance
- AI-assisted screening of MXene nanohybrids
- Future research directions and publication-ready problem framing
🛠️ Hands-on Activity
DFT Descriptor-Based Material Ranking using Google Colab
Participants will use Python to rank MXene-derived nanohybrids based on key descriptors such as adsorption energy, band gap, conductivity indicator, and ion diffusion score.
