# Tem Image Analyzer

> Transmission Electron Microscopy image analysis skill for nanoparticle size, morphology, and crystallography assessment

- Skill: `a5c-ai/tem-image-analyzer` (Agent Skill)
- Install (CLI): `npx skillmds@latest add a5c-ai/tem-image-analyzer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/tem-image-analyzer/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/tem-image-analyzer

---


# TEM Image Analyzer

## Purpose

The TEM Image Analyzer skill provides comprehensive analysis of transmission electron microscopy data for nanomaterial characterization, enabling automated particle detection, size distribution analysis, and crystallographic structure determination.

## Capabilities

- Automated particle detection and sizing
- Morphology classification
- Lattice fringe analysis
- Selected area electron diffraction (SAED) indexing
- High-resolution TEM (HRTEM) analysis
- STEM-HAADF imaging

## Usage Guidelines

### Image Analysis Workflow

1. **Particle Detection**
   - Apply appropriate thresholding
   - Use watershed for touching particles
   - Count minimum 200 particles for statistics

2. **Size Measurement**
   - Calibrate pixel size from scale bar
   - Measure Feret diameter or equivalent circular diameter
   - Report mean, standard deviation, distribution

3. **Crystallographic Analysis**
   - Index SAED patterns to phase
   - Measure d-spacings from lattice fringes
   - Identify zone axis from HRTEM

## Process Integration

- Multi-Modal Nanomaterial Characterization Pipeline
- Statistical Particle Size Distribution Analysis
- In-Situ Characterization Experiment Design

## Input Schema

```json
{
  "image_path": "string",
  "analysis_type": "sizing|morphology|crystallography",
  "scale_bar": {"length": "number", "pixels": "number"},
  "expected_material": "string (for indexing)"
}
```

## Output Schema

```json
{
  "particle_statistics": {
    "count": "number",
    "mean_size": "number (nm)",
    "std_dev": "number (nm)",
    "size_distribution": {"bins": [], "counts": []}
  },
  "morphology": {
    "shapes": [{"type": "string", "fraction": "number"}],
    "aspect_ratio": "number"
  },
  "crystallography": {
    "phase": "string",
    "d_spacings": ["number (nm)"],
    "zone_axis": "string"
  }
}
```