scholarly journals The detection efficiency study of NaI(Tl) scintillation detector with the different numbers of SiPMs

2022 ◽  
Author(s):  
Bao Wang ◽  
Xiongjie Zhang ◽  
Qingshan Wang ◽  
Dongyang Wang ◽  
Dong Li ◽  
...  
Keyword(s):  
Scintillation Detector ◽  
Detection Efficiency

scholarly journals A Monte Carlo Method for Determining the Response Relationship between Two Commonly Used Detectors to Indirectly Measure Alpha Particle Radiation Activity

Molecules ◽  
2019 ◽  
Vol 24 (18) ◽  
pp. 3397
Author(s):  
Christopher J. Tichacek ◽  
Mikalai M. Budzevich ◽  
Thaddeus J. Wadas ◽  
David L. Morse ◽  
Eduardo G. Moros
Keyword(s):  
Monte Carlo ◽  
Alpha Particle ◽  
Clinical Studies ◽  
Scintillation Detector ◽  
Ionization Chamber ◽  
Detection Efficiency ◽  
Monte Carlo Model ◽  
Response Relationship ◽  
Monte Carlo Modeling ◽  
Oak Ridge

Using targeted ligands to deliver alpha-emitting radionuclides directly to tumor cells has become a promising therapeutic strategy. To calculate the radiation dose to patients, activities of parent and daughter radionuclides must be measured. Scintillation detectors can be used to quantify these activities; however, activities found in pre-clinical and clinical studies can exceed their optimal performance range. Therefore, a method of correcting scintillation detector measurements at higher activities was developed using Monte Carlo modeling. Because there are currently no National Institute of Standards and Technology traceable Actinium-225 (225Ac) standards available, a well-type ionization chamber was used to measure 70.3 ± 7.0, 144.3 ± 14.4, 222.0 ± 22.2, 299.7 ± 30.0, 370.0 ± 37.0, and 447.7 ± 44.7 kBq samples of 225Ac obtained from Oak Ridge National Lab. Samples were then placed in a well-type NaI(Tl) scintillation detector and spectra were obtained. Alpha particle activity for each species was calculated using gamma abundance per alpha decay. MCNP6 Monte Carlo software was used to simulate the 4π-geometry of the NaI(Tl) detector. Using the ionization chamber reading as activity input to the Monte Carlo model, spectra were obtained and compared to NaI(Tl) spectra. Successive simulations of different activities were run until a spectrum minimizing the mean percent difference between the two was identified. This was repeated for each sample activity. Ionization chamber calibration measurements showed increase in error from 3% to 10% as activities decreased, resulting from decreasing detection efficiency. Measurements of 225Ac using both detector types agreed within 7% of Oak Ridge stated activities. Simulated Monte Carlo spectra of 225Ac were successfully generated. Activities obtained from these spectra differed with ionization chamber readings up to 156% at 147.7 kBq. Simulated spectra were then adjusted to correct NaI(Tl) measurements to be within 1%. These were compared to ionization chamber readings and a response relationship was determined between the two instruments. Measurements of 225Ac and daughter activity were conducted using a NaI(Tl) scintillation detector calibrated for energy and efficiency and an ionization chamber calibrated for efficiency using a surrogate calibration reference. Corrections provided by Monte Carlo modeling improve the accuracy of activity quantification for alpha-particle emitting radiopharmaceuticals in pre-clinical and clinical studies.

Fresnel interference in point-projection imaging of purple membrane

1995 ◽  
Vol 53 ◽  
pp. 846-847
Author(s):  
X. Zhang ◽  
J. Spence ◽  
W. Qian ◽  
D. Taylor ◽  
K. Taylor
Keyword(s):  
Detection Efficiency ◽  
Mean Free Path ◽  
Purple Membrane ◽  
Unit Cell Dimension ◽  
Experimental Point ◽  
Membrane Thickness ◽  
Low Energies ◽  
Channel Plate ◽  
Point Projection ◽  
Inelastic Mean Free Path

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.

Effect of energy filtering on micro-diffraction in the SEM

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.

Trace nanoanalysis

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).

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

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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