AgentSkillsCN

nanoparticle-synthesis-optimizer

精通金属、半导体与氧化物纳米颗粒生产的合成参数优化,可实现自动化工艺流程生成与重复性验证

SKILL.md
--- frontmatter
name: nanoparticle-synthesis-optimizer
description: Synthesis parameter optimization skill for metal, semiconductor, and oxide nanoparticle production with automated protocol generation and reproducibility validation
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Bash
metadata:
  specialization: nanotechnology
  domain: science
  category: synthesis-materials
  priority: high
  phase: 6
  tools-libraries:
    - Custom synthesis planners
    - Reaction kinetics models
    - DOE frameworks

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 (%)"
  }
}