Agent skill

nanoparticle-synthesis-optimizer

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

Stars 514
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/science/nanotechnology/skills/nanoparticle-synthesis-optimizer

Metadata

Additional technical details for this skill

phase
6
domain
science
category
synthesis-materials
priority
high
specialization
nanotechnology
tools libraries
[
    "Custom synthesis planners",
    "Reaction kinetics models",
    "DOE frameworks"
]

SKILL.md

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

json
{
  "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

json
{
  "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 (%)"
  }
}

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