nanoparticle-synthesis-optimizer
Synthesis parameter optimization skill for metal, semiconductor, and oxide nanoparticle production with automated protocol generation and reproducibility validation
Best use case
nanoparticle-synthesis-optimizer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Synthesis parameter optimization skill for metal, semiconductor, and oxide nanoparticle production with automated protocol generation and reproducibility validation
Teams using nanoparticle-synthesis-optimizer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/nanoparticle-synthesis-optimizer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How nanoparticle-synthesis-optimizer Compares
| Feature / Agent | nanoparticle-synthesis-optimizer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Synthesis parameter optimization skill for metal, semiconductor, and oxide nanoparticle production with automated protocol generation and reproducibility validation
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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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