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Related Concept Videos

Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Properties of DTFT II01:24

Properties of DTFT II

In the study of discrete-time signal processing, understanding the properties of the Discrete-Time Fourier Transform (DTFT) is crucial for analyzing and manipulating signals in the frequency domain. Several properties, including frequency differentiation, convolution, accumulation, and Parseval's relation, offer powerful tools for signal analysis.
The frequency differentiation property is illustrated by considering a DTFT pair and differentiating both sides with respect to ω. Multiplying by j...
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
Two-Dimensional (2D) NMR: Overview01:12

Two-Dimensional (2D) NMR: Overview

The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse.

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Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid
08:54

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid

Published on: January 25, 2020

Spectrum simulation in DTSA-II.

Nicholas W M Ritchie1

  • 1National Institute of Standards and Technology, Gaithersburg, MD 20889-8371, USA. nicholas.ritchie@nist.gov

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|September 17, 2009
PubMed
Summary
This summary is machine-generated.

Spectrum simulation software aids in understanding analytical limits for complex samples. DTSA-II offers flexible tools for electron probe microanalysis, enhancing measurement optimization.

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Computational Physics

Background:

  • Spectrum simulation is crucial for understanding analytical techniques, especially for complex samples and trace elements.
  • Optimizing instrument parameters and measurement limits requires effective simulation tools.

Purpose of the Study:

  • To introduce DTSA-II, a versatile software for electron probe microanalysis (EPMA) spectrum simulation.
  • To highlight DTSA-II's capabilities in modeling various sample geometries and materials for enhanced analysis.

Main Methods:

  • Utilizes analytical models based on (rhoz) curves for rapid simulations of simple samples.
  • Employs Monte Carlo models for detailed electron and X-ray transport simulations in complex geometries.
  • Provides a framework with interchangeable physical models for diverse simulation needs.

Main Results:

  • DTSA-II offers both straightforward and advanced simulation tools for EPMA.
  • The software supports the simulation of common and uncommon sample types and materials.
  • Includes functionalities for visualizing, comparing, manipulating, and quantifying simulated and measured spectra.

Conclusions:

  • DTSA-II is a powerful and flexible tool for practical and pedagogical spectrum simulation in electron probe microanalysis.
  • The software aids researchers in understanding analytical limitations and optimizing measurement strategies for diverse sample types.