experiment-planner-doe

name	experiment-planner-doe
description	Design of Experiments skill for systematic optimization of nanomaterial synthesis and processing
allowed-tools	["Read","Write","Glob","Grep","Bash"]
metadata	{"specialization":"nanotechnology","domain":"science","category":"infrastructure-quality","priority":"high","phase":6,"tools-libraries":["JMP","Design-Expert","Minitab","scipy.stats"]}
graph	{"domains":["domain:nanotechnology"],"skillAreas":["skill-area:mathematical-reasoning","skill-area:physics-simulation","skill-area:data-analysis"],"workflows":["workflow:experiment-design"],"roles":["role:research-engineer"]}

Experiment Planner DOE

Purpose

The Experiment Planner DOE skill provides systematic experimental design for nanomaterial synthesis and processing optimization, enabling efficient exploration of parameter space and robust process development.

Capabilities

• Factorial design generation
• Response surface methodology
• Taguchi method implementation
• ANOVA analysis
• Optimization predictions
• Robustness testing

Usage Guidelines

DOE Workflow

1. Design Selection

   • Identify factors and levels
   • Choose appropriate design
   • Calculate required runs

2. Execution Planning

   • Randomize run order
   • Include replicates
   • Plan blocking if needed

3. Analysis

   • Perform ANOVA
   • Build response models
   • Optimize parameters

Process Integration

• Nanoparticle Synthesis Protocol Development
• Thin Film Deposition Process Optimization
• Nanolithography Process Development

Input Schema

{
  "factors": [{
    "name": "string",
    "low": "number",
    "high": "number",
    "type": "continuous|categorical"
  }],
  "responses": ["string"],
  "design_type": "factorial|fractional|rsm|taguchi",
  "constraints": {
    "max_runs": "number",
    "blocking": "boolean"
  }
}

Output Schema

{
  "design": {
    "type": "string",
    "runs": "number",
    "run_table": [{
      "run": "number",
      "factors": {},
      "block": "number"
    }]
  },
  "analysis": {
    "anova_table": {},
    "significant_factors": ["string"],
    "r_squared": "number"
  },
  "optimization": {
    "optimal_settings": {}