playbooks

home / skills / a5c-ai / babysitter / dls-particle-sizer

dls-particle-sizer skill

/plugins/babysitter/skills/babysit/process/specializations/domains/science/nanotechnology/skills/dls-particle-sizer

This skill analyzes dynamic light scattering data to deliver hydrodynamic size distributions, PDI, and multi-angle insights for nanoparticle stability.

npx playbooks add skill a5c-ai/babysitter --skill dls-particle-sizer

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
2.0 KB
---
name: dls-particle-sizer
description: Dynamic Light Scattering skill for hydrodynamic size distribution and polydispersity analysis
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Bash
metadata:
  specialization: nanotechnology
  domain: science
  category: microscopy-characterization
  priority: high
  phase: 6
  tools-libraries:
    - Malvern Zetasizer software
    - ALV correlator
    - CONTIN algorithm
---

# DLS Particle Sizer

## Purpose

The DLS Particle Sizer skill provides dynamic light scattering analysis for nanoparticle hydrodynamic size determination, enabling rapid, non-destructive measurement of size distributions and stability assessment.

## Capabilities

- Hydrodynamic diameter measurement
- Polydispersity index (PDI) calculation
- Size distribution analysis (intensity, volume, number)
- Temperature-dependent measurements
- Multi-angle DLS analysis
- Particle concentration estimation

## Usage Guidelines

### DLS Measurement

1. **Sample Preparation**
   - Dilute to appropriate concentration
   - Filter to remove dust
   - Equilibrate temperature

2. **Data Analysis**
   - Use cumulants for monomodal samples
   - Apply CONTIN for multimodal
   - Report intensity-weighted Z-average

3. **Quality Metrics**
   - PDI < 0.1: Monodisperse
   - PDI 0.1-0.3: Narrow distribution
   - PDI > 0.3: Broad distribution

## Process Integration

- Statistical Particle Size Distribution Analysis
- Nanoparticle Synthesis Protocol Development
- Nanoparticle Drug Delivery System Development

## Input Schema

```json
{
  "sample_id": "string",
  "solvent": "string",
  "temperature": "number (C)",
  "refractive_index": "number",
  "viscosity": "number (cP)"
}
```

## Output Schema

```json
{
  "z_average": "number (nm)",
  "pdi": "number",
  "distribution": {
    "intensity": {"peaks": [{"size": "number", "percent": "number"}]},
    "volume": {"peaks": [{"size": "number", "percent": "number"}]},
    "number": {"peaks": [{"size": "number", "percent": "number"}]}
  },
  "quality_metrics": {
    "intercept": "number",
    "baseline": "number"
  }
}
```

Overview

This skill performs dynamic light scattering (DLS) analysis to determine nanoparticle hydrodynamic size distributions and polydispersity. It produces Z-average diameters, PDI values, and intensity/volume/number-weighted distributions, plus basic quality metrics for measurement assessment. The skill supports temperature settings, solvent properties, and multi-angle analysis to improve accuracy and interpretation.

How this skill works

Provide sample metadata (ID, solvent, temperature, refractive index, viscosity) and the skill processes correlation data to compute cumulants for monomodal samples or runs CONTIN-style inversion for multimodal results. Outputs include Z-average, PDI, and peak lists for intensity, volume, and number distributions, along with intercept and baseline quality metrics. Temperature-dependent and multi-angle options adjust hydrodynamic calculations and improve resolution for complex mixtures.

When to use it

  • Rapid characterization of nanoparticle hydrodynamic diameter during synthesis workflows
  • Assessing colloidal stability or aggregation over time or temperature ramps
  • Distinguishing monomodal versus multimodal samples and reporting appropriate metrics
  • Estimating particle concentration trends alongside size distributions
  • Integrating DLS steps into automated or agentic lab workflows

Best practices

  • Prepare samples by diluting to optical suitable concentration and filtering to remove dust
  • Equilibrate samples to the target temperature before acquisition to avoid convection artifacts
  • Use cumulants (Z-average) for clearly monodisperse samples and CONTIN for suspected multimodal distributions
  • Report intensity-weighted Z-average and include PDI and intercept/baseline to validate data quality
  • Provide accurate solvent refractive index and viscosity values to ensure correct hydrodynamic sizing

Example use cases

  • Automated feedback loop during nanoparticle synthesis: measure size after each reaction step and adjust conditions
  • Stability testing: run temperature ramps and report size/PDI trends for formulation stability decisions
  • Formulation screening: compare particle size distributions across solvent systems or surfactant concentrations
  • Integration into agentic workflows to trigger downstream steps (e.g., purification) when target size/PDI criteria are met

FAQ

What PDI values indicate good monodispersity?

PDI < 0.1 is typically monodisperse; 0.1–0.3 indicates a narrow distribution; >0.3 suggests broad or multimodal samples.

Which analysis method should I choose for multimodal samples?

Use CONTIN-style inversion for multimodal samples and cumulants for single, symmetric peaks; review intensity, volume, and number outputs together.