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Related Experiment Video

Updated: Apr 18, 2026

Quantifying X-Ray Fluorescence Data Using MAPS

Quantifying X-Ray Fluorescence Data Using MAPS

Published on: February 17, 2018

Optimizing detector geometry for trace element mapping by X-ray fluorescence.

Yue Sun¹, Sophie-Charlotte Gleber², Chris Jacobsen³

¹Graduate Program in Applied Physics, Northwestern University, Evanston, IL 60208, USA.

Ultramicroscopy

|January 21, 2015

Summary

This summary is machine-generated.

This study presents an analytical model to determine factors limiting trace element detection using X-ray Fluorescence (XRF) microscopy. The model optimizes detector geometry for enhanced signal-to-noise ratio in elemental analysis.

Keywords:

Detector geometry Signal-to-noise ratio Trace element detection X-ray fluorescence X-ray fluorescence microscopy

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Area of Science:

Materials Science
Analytical Chemistry
Physics

Background:

Trace metals are crucial in diverse systems, from biological cells to photovoltaic devices.
X-Ray Fluorescence (XRF) microscopy offers high sensitivity for sub-100 nm trace metal imaging.
Advancements in synchrotron sources and detectors necessitate understanding detection limits.

Purpose of the Study:

To identify factors limiting trace element detectability in XRF microscopy.
To develop an analytical model for predicting XRF signals and optimizing detection.
To assess the impact of detector geometry on signal-to-noise ratio for elemental analysis.

Main Methods:

Developed an analytical model for total signal incident on energy-dispersive XRF detectors.
Validated the model against experimental X-ray fluorescence spectra.
Calculated contrast and signal-to-noise ratio (S/N) based on elemental and background signals.

Main Results:

The analytical model provides accurate predictions with reduced computation time compared to Monte Carlo simulations.
Increased detector collection solid angle generally improves detectability.
Detector geometry significantly impacts trace element detection: 90° for thick samples, 180° for thin samples.

Conclusions:

The developed model aids in optimizing XRF microscopy for trace element detection.
Detector geometry choice is critical for maximizing sensitivity in different sample types.
This work advances elemental analysis in biological and semiconductor materials.