Variation of the detection efficiency of a Ge detector with the height of the sample in Marinelli beaker

Experimental point-projection shadow microscope (PPM) images of uncoated, unstained purple membrane (PM, bacteriorhodopsin, a membrane protein from Halobacterium holobium) were obtained recently using 100 volt electrons. The membrane thickness is about 5 nm and the hexagonal unit cell dimension 6 nm. The images show contrast around the edges of small holes, as shown in figure 1. The interior of the film is opaque. Since the inelastic mean free path for 100V electrons in carbon (about 6 Å) is much less than the sample thickness, the question arises that how much, if any, transmission of elastically scattered electrons occurs. A large inelastic contribution is also expected, attenuated by the reduced detection efficiency of the channel plate at low energies. Quantitative experiments using an energy-loss spectrometer are planned. Recently Shedd has shown that at about 100V contrast in PPM images of thin gold films can be explained as Fresnel interference effects between different pinholes in the film, separated by less than the coherence width.

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Effect of energy filtering on micro-diffraction in the SEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010017075x ◽

1994 ◽

Vol 52 ◽

pp. 604-605

Author(s):

James F. Mancuso ◽

Leo A. Fama ◽

William B. Maxwell ◽

Jerry L. Lehman ◽

Hasso Weiland ◽

...

Keyword(s):

Detection Efficiency ◽

Ccd Camera ◽

High Gain ◽

Microchannel Plate ◽

Electrostatic Energy ◽

Energy Filtering ◽

Electron Channelling ◽

Low Energy Electrons ◽

Low Sensitivity ◽

Angular Field Of View

Micro-diffraction based crystallography is essential to the design and development of many classes of ‘crafted materials’. Although the scanning electron microscope can provide crystallographic information with high spatial resolution, its current utility is severely limited by the low sensitivity of existing diffraction techniques (ref: Dingley). Previously, Joy showed that energy filtering increased contrast and pattern visibility in electron channelling. This present paper discribes the effect of energy filtering on EBSP sensitivity and backscattered SEM imaging.The EBSP detector consisted of an electron energy filter, a microchannel plate detector, a phosphor screen, optical coupler, and a slow scan CCD camera. The electrostatic energy filter used in this experiment was constructed as a cone with 5 coaxial electrodes. The angular field-of-view of the filter was approximately 38°. The microchannel plate, which was the initial sensing component, had high gain and had 50% to 80% detection efficiency for the low energy electrons that passed through the retarding field filter.

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Trace nanoanalysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100172437 ◽

1994 ◽

Vol 52 ◽

pp. 940-941

Author(s):

D. E. Newbury ◽

R. D. Leapman

Keyword(s):

High Performance ◽

Secondary Ion Mass Spectrometry ◽

Detection Efficiency ◽

Volume Element ◽

And Performance ◽

Trace Constituents ◽

Secondary Ion ◽

Concentration Levels ◽

Structure Properties

Trace constituents, which can be very loosely defined as those present at concentration levels below 1 percent, often exert influence on structure, properties, and performance far greater than what might be estimated from their proportion alone. Defining the role of trace constituents in the microstructure, or indeed even determining their location, makes great demands on the available array of microanalytical tools. These demands become increasingly more challenging as the dimensions of the volume element to be probed become smaller. For example, a cubic volume element of silicon with an edge dimension of 1 micrometer contains approximately 5×1010 atoms. High performance secondary ion mass spectrometry (SIMS) can be used to measure trace constituents to levels of hundreds of parts per billion from such a volume element (e. g., detection of at least 100 atoms to give 10% reproducibility with an overall detection efficiency of 1%, considering ionization, transmission, and counting).

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MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

Current Bioinformatics ◽

10.2174/1574893615999200607173829 ◽

2020 ◽

Vol 15 ◽

Author(s):

Yi Zou ◽

Hongjie Wu ◽

Xiaoyi Guo ◽

Li Peng ◽

Yijie Ding ◽

...

Keyword(s):

Dna Binding ◽

Binding Proteins ◽

Detection Efficiency ◽

Dna Binding Proteins ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Multiple Kernel ◽

Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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