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데이터 및 연구 · SKILL.md

tof-sims-analyzer

tof-sims-analyzer

Time-of-Flight Secondary Ion Mass Spectrometry skill for molecular surface analysis and imaging

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: tof-sims-analyzer description: Time-of-Flight Secondary Ion Mass Spectrometry skill for molecular surface analysis and imaging allowed-tools:

  • Read
  • Write
  • Glob
  • Grep
  • Bash metadata: specialization: nanotechnology domain: science category: surface-analysis priority: medium phase: 6 tools-libraries:
    • ION-TOF software
    • SurfaceLab
    • NESAC/BIO tools 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]

ToF-SIMS Analyzer

Purpose

The ToF-SIMS Analyzer skill provides molecular-level surface analysis capabilities for nanomaterials, enabling identification of surface species, chemical imaging, and depth profiling with high sensitivity.

Capabilities

  • Molecular ion identification
  • Surface contamination analysis
  • 2D and 3D chemical imaging
  • Isotope labeling detection
  • Depth profiling
  • Principal component analysis of spectra

Usage Guidelines

ToF-SIMS Analysis

  1. Spectral Analysis

    • Identify characteristic fragments
    • Build peak lists for materials
    • Apply multivariate analysis
  2. Imaging Mode

    • Optimize spatial resolution
    • Generate chemical maps
    • Correlate with topography
  3. Depth Profiling

    • Select appropriate sputter source
    • Monitor interface sharpness
    • Account for matrix effects

Process Integration

  • Multi-Modal Nanomaterial Characterization Pipeline
  • Nanomaterial Surface Functionalization Pipeline

Input Schema

{
  "sample_id": "string",
  "analysis_mode": "spectral|imaging|depth_profile",
  "primary_ion": "Bi3+|Bi1+|C60+",
  "polarity": "positive|negative",
  "area_of_interest": {"x": "number", "y": "number"}
}

Output Schema

{
  "identified_species": [{
    "mass": "number (amu)",
    "assignment": "string",
    "intensity": "number"
  }],
  "chemical_maps": [{
    "species": "string",
    "image_path": "string"
  }],
  "pca_results": {
    "principal_components": "number",
    "variance_explained": ["number"]
  }
}
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