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nanoparticle-synthesis-optimizer

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Synthesis parameter optimization skill for metal, semiconductor, and oxide nanoparticle production with automated protocol generation and reproducibility validation

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Source SKILL.md: https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/domains/science/nanotechnology/skills/nanoparticle-synthesis-optimizer/SKILL.md

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Nanoparticle Synthesis Optimizer

Purpose

The Nanoparticle Synthesis Optimizer skill provides systematic optimization of synthesis parameters for metal, semiconductor, and oxide nanoparticle production, enabling reproducible synthesis protocols with controlled size, morphology, and surface chemistry.

Capabilities

  • Precursor stoichiometry calculation
  • Reaction temperature/time optimization
  • Surfactant and capping agent selection
  • Nucleation and growth kinetics modeling
  • Size distribution targeting
  • Batch reproducibility assessment

Usage Guidelines

Synthesis Parameter Optimization

  1. Precursor Selection

    • Match precursor reactivity to desired kinetics
    • Consider thermal decomposition temperatures
    • Evaluate purity requirements

  2. Temperature Programming

    • Optimize nucleation temperature for burst nucleation
    • Control growth temperature for size focusing
    • Manage heating ramp rates

  3. Surfactant Systems

    • Balance steric vs electrostatic stabilization
    • Consider binding affinity to specific facets
    • Optimize surfactant-to-precursor ratios

Process Integration

  • Nanoparticle Synthesis Protocol Development
  • Nanomaterial Scale-Up and Process Transfer
  • Green Synthesis Route Development

Input Schema

{
  "target_material": "string",
  "target_size": "number (nm)",
  "target_morphology": "sphere|rod|cube|plate",
  "size_tolerance": "number (%)",
  "synthesis_method": "thermal_decomposition|hot_injection|coprecipitation"
}

Output Schema

{
  "optimized_protocol": {
    "precursors": [{"name": "string", "concentration": "number"}],
    "temperature_profile": [{"temp": "number", "duration": "number"}],
    "surfactants": [{"name": "string", "ratio": "number"}]
  },
  "predicted_outcomes": {
    "size": "number (nm)",
    "size_distribution": "number (%)",
    "yield": "number (%)"
  }
}