Nanoparticle Synthesis Optimizer
SkillDev toolsSynthesis parameter optimization skill for metal, semiconductor, and oxide nanoparticle production with automated protocol generation and reproducibility validation
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Nanoparticle Synthesis Optimizer skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/nanotechnology/skills/nanoparticle-synthesis-optimizer/SKILL.md and read by ahel’s review.
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 (%)"
}
}
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
nanoparticle-synthesis-optimizer- Source
- github.com/a5c-ai/babysitter
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