Agent skill
nanoparticle-synthesis-optimizer
Synthesis parameter optimization skill for metal, semiconductor, and oxide nanoparticle production with automated protocol generation and reproducibility validation
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
-
Precursor Selection
- Match precursor reactivity to desired kinetics
- Consider thermal decomposition temperatures
- Evaluate purity requirements
-
Temperature Programming
- Optimize nucleation temperature for burst nucleation
- Control growth temperature for size focusing
- Manage heating ramp rates
-
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 (%)"
}
}
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