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Equipments Used to Measure Body Temperature01:13

Equipments Used to Measure Body Temperature

Body temperature can be assessed using various devices and measured in Celsius or Fahrenheit.
Glass-bulb Thermometer:
Glass-bulb thermometers are hollow glass tubes with a bulb tip containing liquid such as ethanol or mercury. Historically, glass bulb mercury thermometers were the standard device to measure body temperature. Today, mercury thermometers are prohibited in many countries due to the hazardous effects of mercury and the risk of exposure if the glass bulb breaks. In general,...
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Thermal Measurement Techniques in Analytical Microfluidic Devices
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A physics based machine learning model to characterize room temperature semiconductor detectors in 3D.

Srutarshi Banerjee1, Miesher Rodrigues2, Manuel Ballester3

  • 1Northwestern University, 2145 Sheridan Road, Evanston, IL, 60208, USA. srutarshibanerjee2022@u.northwestern.edu.

Scientific Reports
|April 2, 2024
PubMed
Summary

A new physics-based machine learning model characterizes room temperature semiconductor radiation detectors (RTSD) in 3D. This approach models charge transport properties within sub-pixel voxels for improved detector performance and advanced imaging applications.

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Area of Science:

  • Physics
  • Materials Science
  • Machine Learning

Background:

  • Room temperature semiconductor radiation detectors (RTSD), particularly Cadmium Zinc Telluride (CZT), are crucial for X-ray and gamma-ray detection in fields like medical imaging and astrophysics.
  • Current characterization methods for RTSDs often assume bulk homogeneity and lack detailed 3D sub-pixel resolution, hindering advanced event reconstruction.

Purpose of the Study:

  • To introduce a novel physics-based machine learning (PBML) model for detailed 3D characterization of RTSDs at a sub-pixel level.
  • To enable precise modeling of charge transport properties within individual voxels of a discretized detector volume.

Main Methods:

  • The study discretizes the RTSD into 3D voxels, modeling charge transport phenomena (drift, trapping, detrapping, recombination) as trainable weights within each voxel.
  • A second-order non-linear drift model is incorporated to accurately represent observed charge movement.
  • The PBML model is trained using electron-hole pair injections as input and electrode signals as output, with weights determined via backpropagation of a loss function.

Main Results:

  • The PBML model successfully characterizes 3D charge transport properties within voxelized detector volumes.
  • The trained model weights establish a direct correlation with the actual physical charge transport characteristics in each voxel.
  • This represents the first comprehensive 3D charge transport model for RTSDs.

Conclusions:

  • The developed PBML model offers an unprecedented level of detail for RTSD characterization in 3D space.
  • This approach overcomes the limitations of traditional bulk characterization, paving the way for enhanced sub-pixel level analysis.
  • The findings are expected to advance the performance of RTSDs in critical applications requiring high-resolution radiation detection.