a5c-ai/colloidal-stability-analyzer
library/specializations/domains/science/nanotechnology/skills/colloidal-stability-analyzer/SKILL.md
Colloidal stability assessment skill for evaluating nanoparticle dispersion stability through zeta potential, aggregation kinetics, and shelf-life prediction
$ install --global
skillsauthnpx skillsauth add a5c-ai/babysitter colloidal-stability-analyzerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- colloidal-stability-analyzer
- description:
- Colloidal stability assessment skill for evaluating nanoparticle dispersion stability through zeta potential, aggregation kinetics, and shelf-life prediction
- specialization:
- nanotechnology
- domain:
- science
- category:
- synthesis-materials
- priority:
- high
- phase:
- 6
Colloidal Stability Analyzer
Purpose
The Colloidal Stability Analyzer skill provides comprehensive assessment of nanoparticle dispersion stability, enabling prediction of aggregation behavior, shelf-life estimation, and optimization of stabilization strategies through DLVO theory and experimental validation.
Capabilities
- Zeta potential analysis
- DLVO theory-based stability prediction
- Aggregation kinetics modeling
- pH and ionic strength effects
- Steric stabilization assessment
- Shelf-life prediction algorithms
Usage Guidelines
Stability Assessment
-
Zeta Potential Analysis
- Measure at multiple pH values
- Determine isoelectric point
- Assess stability window (|zeta| > 30 mV)
-
DLVO Theory Application
- Calculate van der Waals attraction
- Estimate electrostatic repulsion
- Determine energy barrier height
-
Shelf-Life Prediction
- Monitor size over time
- Apply accelerated aging protocols
- Predict long-term stability
Process Integration
- Nanoparticle Synthesis Protocol Development
- Nanomaterial Surface Functionalization Pipeline
- Nanoparticle Drug Delivery System Development
Input Schema
{
"nanoparticle_type": "string",
"size": "number (nm)",
"surface_chemistry": "string",
"dispersion_medium": "string",
"pH_range": {"min": "number", "max": "number"},
"ionic_strength": "number (mM)"
}
Output Schema
{
"zeta_potential": "number (mV)",
"stability_classification": "stable|marginally_stable|unstable",
"aggregation_rate": "number (nm/day)",
"predicted_shelf_life": "number (days)",
"optimization_recommendations": ["string"]
}
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