home / skills / a5c-ai / babysitter / xrd-crystallography-analyzer
This skill analyzes X-ray diffraction data to identify phases, estimate crystallite size, and refine structures for nanomaterial characterization.
npx playbooks add skill a5c-ai/babysitter --skill xrd-crystallography-analyzerReview the files below or copy the command above to add this skill to your agents.
---
name: xrd-crystallography-analyzer
description: X-ray Diffraction skill for crystal structure, phase identification, and crystallite size analysis of nanomaterials
allowed-tools:
- Read
- Write
- Glob
- Grep
- Bash
metadata:
specialization: nanotechnology
domain: science
category: spectroscopy
priority: high
phase: 6
tools-libraries:
- HighScore
- JADE
- GSAS-II
- FullProf
- PDFgui
---
# XRD Crystallography Analyzer
## Purpose
The XRD Crystallography Analyzer skill provides crystallographic characterization of nanomaterials through X-ray diffraction analysis, enabling phase identification, crystallite size determination, and structural refinement.
## Capabilities
- Phase identification and Rietveld refinement
- Crystallite size (Scherrer equation)
- Lattice parameter calculation
- Preferred orientation analysis
- In-situ XRD capabilities
- PDF (Pair Distribution Function) analysis
## Usage Guidelines
### XRD Analysis
1. **Phase Identification**
- Match peaks to database entries
- Identify multiple phases
- Assess phase purity
2. **Crystallite Size**
- Apply Scherrer equation: D = Kl/(B cos theta)
- Account for instrumental broadening
- Use Williamson-Hall for strain
3. **Structural Refinement**
- Perform Rietveld refinement
- Extract lattice parameters
- Quantify phase fractions
## Process Integration
- Multi-Modal Nanomaterial Characterization Pipeline
- Structure-Property Correlation Analysis
- Nanoparticle Synthesis Protocol Development
## Input Schema
```json
{
"diffraction_file": "string",
"analysis_type": "phase_id|crystallite_size|refinement|pdf",
"wavelength": "number (Angstrom)",
"expected_phases": ["string"]
}
```
## Output Schema
```json
{
"phases": [{
"name": "string",
"pdf_number": "string",
"weight_fraction": "number"
}],
"crystallite_size": {
"value": "number (nm)",
"method": "string"
},
"lattice_parameters": {
"a": "number",
"b": "number",
"c": "number",
"space_group": "string"
},
"refinement_quality": {
"Rwp": "number",
"chi_squared": "number"
}
}
```
This skill provides X-ray diffraction analysis for nanomaterials to identify phases, estimate crystallite sizes, and refine crystal structures. It delivers practical outputs such as phase lists, lattice parameters, crystallite size estimates, and refinement quality metrics. The focus is on fast, reproducible characterization for research and quality control workflows.
The skill ingests diffraction files and runs peak matching against databases to propose phase IDs and weight fractions. It computes crystallite size using the Scherrer equation and optional Williamson–Hall strain analysis, and performs Rietveld-style structural refinement to extract lattice parameters and refinement statistics. Outputs follow a clear schema so downstream pipelines can consume phases, sizes, lattice info, and fit quality metrics.
What input formats are supported?
Provide standard diffraction text files (2θ vs intensity). Ensure the file includes correct wavelength metadata when possible.
How is crystallite size calculated?
Primary method is the Scherrer equation with optional instrumental broadening correction. Williamson–Hall analysis is available to separate strain contributions.
How reliable are automated phase identifications?
Automated matching accelerates screening but should be confirmed for complex chemistries. Supplying expected phases improves accuracy.