nanoparticle-synthesis-optimizer

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

509 stars

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

$curl -o ~/.claude/skills/nanoparticle-synthesis-optimizer/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/nanotechnology/skills/nanoparticle-synthesis-optimizer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/nanoparticle-synthesis-optimizer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How nanoparticle-synthesis-optimizer Compares

Feature / Agentnanoparticle-synthesis-optimizerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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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