Transmission, energy distribution, and SE excitation of fast electrons in thin solid films

Fitting, H.‐J.

doi:10.1002/pssa.2210260216

Cited by 246 publications

(111 citation statements)

References 35 publications

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“…For higher energy electrons (>10 keV) Fitting gives another relation with an exponent of 1.7. [18] For a test of the influence of this approximation we used both relations to calculate the shelf for an SDD detector at 2.622 keV (Cl-Kα 1 ). The results are shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

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Modelling the response function of energy dispersive X‐ray spectrometers with silicon detectors

2009

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A new, analytical description of the physical processes determining the spectral response of an energy dispersive X-ray spectrometer with a silicon detector (Si(Li) or silicon drift detector (SDD)) is presented. The model considers the detector statistical noise, the electronic noise, the incomplete charge collection (ICC) that gives rise to the peak tailing, the escape effect, the fluorescence of the front contact or the dead layer and hot photoelectrons that cause the shelf. Only five free parameters are necessary to model the response function: the electronic noise, three parameters describing the shape of the charge collection efficiency beneath the front contact and the thickness of the detector front layer. Once the five parameters are adjusted to have agreement between a measured and a calculated response function, the response function can be calculated for any other photon energy in the range from 0.1 keV to 30 keV.The algorithm is implemented in IDL and MATLAB and is available also as MATLAB stand-alone program. It enables the determination of the optimum parameter set by fitting a calculated response function to a measured one for monochromatic radiation. A (m,n)-type matrix can be calculated whereby m represents the number of channels for the response function and n the number of photon energies in the selected range. The matrix can be used to convolute a calculated spectrum for comparison with a measured one.The calculated response functions are in agreement with the pulse height distributions measured with monochromatic synchrotron radiation in the energy range from 0.1 keV to 10 keV for three spectrometers with detector crystals different in construction. It is shown that the improved description of the detector response enables the detection of minor components of characteristic lines in fluorescence spectra, which have been attributed earlier to the detector.

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Section: Resultsmentioning

confidence: 99%

“…For R < D we get again Eqn (14). For R > D, we have to divide the integration range into the ranges 0 < d < D and D < d < R: For the first subrange we get instead of Eqn (14) (18) and for the range…”

Section: Shelf From the Detector Front Layermentioning

confidence: 99%

Modelling the response function of energy dispersive X‐ray spectrometers with silicon detectors

2009

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“…For practical use, e.g., for calculation of energy dependence of coefficient of backscattering η, it is possible to use these values as W min in the model similar to (Liljequist, 1978) and calculate the bacscattering coefficient. Even though this model is theoretically far from reality, the energy dependence of calculated and measured values of backscattering coefficient η (Fitting, 1974) (Figure 2) shows relatively good agreement. By this way it is possible to use for simulation IMFP values from various sources and compare them.…”

Section: Electron Backscatteringmentioning

confidence: 83%

Monte-Carlo Simulation in Electron Microscopy and Spectroscopy

Starý¹

2011

Applications of Monte Carlo Method in Science and Engineering

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“…However, Whiddington's law was confirmed only for the electron energy range above 10 keV, but not for low energy (≤10 keV) electrons (Young, 1956;Fitting, 1974). Lye and Dekker (1957) have suggested that the energy loss of primary particles is described by the general form,…”

Section: Energy Loss Of Primariesmentioning

confidence: 99%

“…The range-energy relation of electrons in matter for their energy range 0.1 keV < ∼ E 0 < ∼ 1 MeV can be approximated by (cf. Fitting, 1974) R (E 0 ) = 50 nm ρ 10 3 kg m −3…”

Section: Energy Loss Of Primariesmentioning

confidence: 99%

Filtering of the interstellar dust flow near the heliopause: the importance of secondary electron emission for the grain charging

Kimura¹,

Mann²

2014

Earth Planet Sp

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The deflection of interstellar dust grains in the magnetic field near the heliopause depends on their surface electric charge. We study the electric charging of the grains with emphasis on the secondary electron emission because of its importance in the hot plasma environment near the heliopause. We correct previous models of the secondary electron emission that overestimate the electric charge of dust near the heliopause. Our model calculations of the grain charge, when combined with results from in situ measurements of interstellar dust in the heliosphere, place an upper limit on the magnetic field strength. We find that the detection of interstellar dust with mass of 10 −18 kg indicates the component of the magnetic field perpendicular to the interstellar dust flow to be less than 0.4 nT.

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Transmission, energy distribution, and SE excitation of fast electrons in thin solid films

Cited by 246 publications

References 35 publications

Modelling the response function of energy dispersive X‐ray spectrometers with silicon detectors

Modelling the response function of energy dispersive X‐ray spectrometers with silicon detectors

Monte-Carlo Simulation in Electron Microscopy and Spectroscopy

Filtering of the interstellar dust flow near the heliopause: the importance of secondary electron emission for the grain charging

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