LS 6500

Analyze LS 6500 Liquid Scintillation Counter data with AI

Multi-channel liquid scintillation counter for radioactivity measurement with advanced spectral analysis

Channels: 32,768-channel analyzer
Detection: Detailed beta spectra analysis
Vial Compatibility: Various vial sizes supported
Correction: Quench correction curves
Data Export: CSV via PC software
Get Started For Free Fully compatible with Sourcetable

Quick Start Integration

Get your LS 6500 data into Sourcetable in 3 simple steps

  • Step 1

    Export Data

    Export scintillation counting data from your LS 6500 via PC software to CSV format

  • Step 2

    Upload to Sourcetable

    Import your radioactivity measurement data into Sourcetable for analysis

  • Step 3

    Start Analysis

    Perform automated radioactivity decay analysis and detection limit calculations

File Format Compatibility

Optimized for liquid scintillation counting with quench correction and spectral analysis

.csv

Raw Data

Full Support CPM/DPM results per sample, automatic quench correction, spectral graphs, batch statistics

Pre-built Analysis Workflows

Radiometric workflows for beta emitter analysis and environmental monitoring

🔬

Radioactivity Analysis

Measurement of H-3 and C-14 labeled compounds with spectral analysis

Radioactivity Analysis Beta Emitters Spectral Analysis
📊

Decay Analysis

Radioactivity decay analysis with quench correction

Decay Analysis Quench Correction DPM Calculation

Detection Limits

Background subtraction and detection limit calculations

Detection Limits Background Subtraction Environmental Monitoring

Technical Requirements

Software Dependencies

  • LS 6500 32,768-channel liquid scintillation counter
  • Control Software Optional PC software for data capture
  • File Export Format CSV via PC software
  • Applications Academic research, environmental monitoring

Sourcetable Libraries

  • Data Processing pandas, numpy
  • Statistical Analysis scipy, statsmodels
  • Visualization matplotlib, plotly
  • Radiometric Analysis scikit-learn, seaborn

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