data/research.json

[
  {
    "index": "01",
    "tag": "Materials & Processing",
    "title": "Critical Mineral Recovery from Electronic Waste",
    "desc": "Data-driven separation process design for rare earth elements from end-of-life electronics using ML-guided solvent extraction optimization.",
    "pills": ["Machine Learning", "Separation"]
  },
  {
    "index": "02",
    "tag": "Computational Methods",
    "title": "Topology Optimization for Thermal Recovery Systems",
    "desc": "Novel multiscale topology optimization frameworks for heat exchanger design in waste heat recovery applications across industrial processes.",
    "pills": ["Topology Opt.", "Thermal"]
  },
  {
    "index": "03",
    "tag": "Circular Economy",
    "title": "Urban Material Flow Modeling & Optimization",
    "desc": "Agent-based and network flow models to characterize and optimize material stocks and flows in city-scale circular economy transitions.",
    "pills": ["Urban Systems", "Modeling"]
  },
  {
    "index": "04",
    "tag": "Energy Systems",
    "title": "Computational Design of Battery Recycling Pathways",
    "desc": "Process simulation and multi-objective optimization of lithium-ion battery recycling chains balancing economic and environmental objectives.",
    "pills": ["Battery Tech", "LCA"]
  },
  {
    "index": "05",
    "tag": "Machine Learning",
    "title": "Surrogate Models for Process-Level Recovery Simulation",
    "desc": "Physics-informed neural networks and Gaussian process surrogates to accelerate high-fidelity simulation of separation and recovery processes.",
    "pills": ["Neural Nets", "PINN"]
  },
  {
    "index": "06",
    "tag": "Policy & Systems",
    "title": "Life Cycle & Techno-Economic Analysis",
    "desc": "Integrated LCA and TEA frameworks to evaluate sustainability and economic viability of emerging resource recovery technologies at various scales.",
    "pills": ["TEA", "Policy"]
  }
]