Types Of Detectors Research Articles

Influence of dead layer on the response function of planar and coaxial Ge detector using Monte Carlo method

Germanium crystals have a dead layer that causes a decrease in efficiency since the layer is not useful for detection but strongly attenuates photons. The thickness of this inactive layer is not well known due to the existence of a transition zone where photons are increasingly absorbed. Therefore, using data provided by manufacturers in the detector simulation model, some strong discrepancies appear by changing the dead layer. Investigations into the Ge detector response functions for gamma rays have been conducted using straightforward physical mechanisms implemented by Monte Carlo simulations. The detector response function feature's most probable interaction mechanisms are described. The Monte Carlo method is applied to simulate the calibration of a HPGe detector in order to determine the total inactive germanium layer thickness and the active volume that is needed in order to study the response function for both types of detectors. Results indicated a strong impact of dead layer variations on the response function of the simulated detectors.

Terahertz detector based on Bi1-xSbx/Cu thermoelectric branches

Although most types of room-temperature THz detectors involving photothermoelectric (PTE) elements operate in a relatively narrow frequency band due to their antenna-coupled structure, such detectors may be implemented on the basis of non-resonant structures with broadband response due to broadband nature of PTE effect. This work is devoted to improvement of PTE detector sensitivity using serial connection of PTE elements consisting of bismuth-antimony thin films and Cu electrodes. The responsivity of this detector is studied at the frequency of 140 GHz. It is shown that the average voltage produced by PTE detector reaches up to 0.6 mV and is 15 times higher than one produced by continuous film consisting of the same material, while the maximum rate of voltage change is 17 times higher in comparison with continuous film response. Noise equivalent power (NEP) of PTE detector based on Bi1-xSbx/Cu branches is equal to 12.6 nWHz−0.5, and noise equivalent temperature difference (NETD) is equal to 4.9 m °C. This detector may be applied in the field of THz and microwave photonics in scanning security systems, THz signal detection in wireless communications, imaging and diagnostics in medicine, food industry and material characterization.

Dual optical detection approach for capillary electrophoresis following two-step liquid-liquid extraction to determine ten phenols in water samples

In this research, the analytical method was developed and evaluated for determining phenol and its nine derivatives belong to the US EPA priority pollutant list in water samples by using dual-channeled capillary electrophoresis (CE) coupled with two types of optical detectors, namely LED-induced fluorescence (LEDIF) and ultraviolet (UV) detectors. The optimal background electrolytes for the first and second CE channels were 20 mM borate (pH 9.80) with 400 µM fluorescein and 55 mM borate (pH 11.75), respectively. The two-step liquid-liquid extraction (LLE) was used for sample preparation and enrichment, in which phenol and its derivatives were extracted from the aqueous phase using 10 mL of n-hexane/1-octanol (60/40, v/v) and then were back extracted into a 0.1 M NaOH as a final acceptor phase. Under the optimal CE and two-step LLE conditions, the enrichment factors of 10 phenols were 184 – 1120-fold, and the method detection limits were lowered to 0.02–0.60 µg/L. The obtained intra-day and inter-day precisions in terms of relative standard deviations (RSD) were between 4.0 and 7.3 % and 6.7 and 14 %, respectively. This approach was used to determine phenols in water samples, with recoveries ranging from 82.0 to 108.9 %. In combination with sample enrichment by two-step LLE extraction, this is the first CE study conducted to determine phenols in the EPA list using two detector approaches, specifically CE-LEDIF/CE-UV.

(Invited) Understanding PEMFC by Advanced Electron Microscopy

A thorough knowledge of the atomic structure and composition of catalyst nanoparticles is paramount to the development of advanced materials for proton exchange membrane fuel cells (PEMFC), one of the most promising energy conversion devices for automotive and stationary applications. Pt and Pt-based alloys nanoparticles (NPs) are currently used as the catalyst to promote the kinetics of the hydrogen oxidation and oxygen reduction reactions in the anode and cathode of the fuel cell, respectively. Yet, the durability of the catalysts remains a major issue for their commercialization. In addition, it is essential to characterize the ionomer distribution and coverage over the catalyst NPs and the carbon support, as the ionomer/catalyst interface plays a critical role in the ORR and MEA fuel cell performance.Ideally, voltage cycling of the membrane electrode assemblies would be performed in-situ within a TEM, but these experiments are highly challenging, since separate gas phase reactants need to be present simultaneously, in particular hydrogen and oxygen Instead, a very close approximation to a true in-situ experiment is done via potential cycling in a half-cell in the presence of liquid electrolyte, as opposed to solid electrolyte in a single cell. This allows us to observe the exact same location before and after cycling through a technique called identical location TEM/STEM (IL-TEM/STEM). Under these conditions, various electron microscopy techniques, such as bright-field TEM and HAADF STEM coupled with EDS and EELS can be performed. To better acquire and understand TEM/STEM-based experimental images, in particular the selection of appropriate operating conditions, and interpretation/validation of the results, it is critical to perform multislice TEM/STEM image simulations to calculate the interaction of an electron beam with a material system.In this talk, the focus is to understand the behavior of Pt and Pt-alloy NPs during the various stages of fuel cell cycling. For this purpose a set-up was developed to simulate the effect of voltage cycling on the cathode side of the fuel cell. In this set up, catalyst NPs supported on carbon nanotubes and amorphous carbon were observed by advanced transmission electron microscopy, before and after cycling. In particular, we studied Pt and Pt-alloy NPs and carbon support degradation mechanisms as a function of cycling using for the first time a combination of 2D IL TEM and 3D IL TEM. This new approach reveals many unpredictable features of morphology changes/behavior of Pt NPs and carbon support, after experiencing the long durability test. These new findings provide an insight which great surpasses what have been observed using solely 2D IL TEM.The experiments show particle migration in conjunction with carbon corrosion during the initial cycles, whereas the appearance of single atoms and atomic clusters on the surface of the carbon support appear after additional voltage cycling as a result of surface dissolution of NPs. For the case of alloyed NPs, the experiments show a heterogeneous deposition of Pt on the NPs. Moreover, we demonstrate a novel approach to distinguish the ionomer phase from the carbon support in PEMFCs, by employing the carbon signal in STEM-EELS, using two types of detectors. In this fashion, not only the ionomer, but also the carbon support, can be mapped in high spatial resolution, despite some damage caused by the electron beam.

Magnetic field influence on the light yield from fiber-coupled BCF-60 plastic scintillators of relevance for output factor dosimetry in MR-linacs

Organic plastic scintillators are of interest for ionizing radiation dosimetry in megavoltage photon beams because plastic scintillators have a mass density very similar to that of water. This leads to insignificant perturbation of the electron fluence at the point of measurement in a water phantom. This feature is a benefit for dosimetry in strong magnetic fields (e.g., 1.5 T) as found in linacs with magnetic resonance imaging. The objective of this work was to quantify if the light yield per dose for the scintillating fiber BCF-60 material from Saint-Gobain Ceramics and Plastics Inc. is constant regardless of the magnetic flux density. This question is of importance for establishing traceable measurement in MR linacs using this detector type. Experiments were carried out using an accelerator combined with an electromagnet (max 0.7 T). Scintillator probes were read out using chromatic stem-removal techniques based on two optical channels or full spectral information. Reference dosimetry was carried out with PTW31010 and PTW31021 ionization chambers. TOPAS/GEANT4 was used for modelling. The light yield per dose for the BCF-60 was found to be strongly influenced by the magnitude of the magnetic field from about 1 mT to 0.7 T. The light yield per dose increased (1.3 ± 0.2)% (k = 1) from 1 mT to 10 mT and it increased (4.5 ± 0.9)% (k = 1) from 0 T to 0.7 T. Previous studies of the influence of magnetic fields on medical scintillator dosimetry have been unable to clearly identify if observed changes in scintillator response with magnetic field strength were related to changes in dose, stem signal removal, or scintillator light yield. In the current study of BCF-60, we see a clear change in light yield with magnetic field, and none of the other effects.

A Comparative Analysis of Single-Stage Detectors from the Perspectives of Anchor-Free and Anchor-Based Approaches

Since the emergence of deep learning in object detection in 2014, there has been rapid development and widespread application of detectors across various domains such as healthcare, road traffic, and industrial inspection. The evolution of convolutional neural network (CNN)-based detectors has reached a mature stage, with single-stage detectors playing a crucial role within CNN-based frameworks. This paper provides an analysis of the developmental process and distinctive features of classic single-stage detectors categorized into two types: Anchor-free and Anchor-based. Furthermore, the paper also provides a comparison of the accuracy trends for these two types of single-stage detectors over different years. Through this analysis and comparison, potential reasons behind the advancements in single-stage detectors during different time periods can be explored, such as network architecture optimization, increase in datasets, and algorithm improvements. Additionally, this paper offers insights into current research focal points and future prospects.

Selection of Aptamers for Use as Molecular Probes in AFM Detection of Proteins.

Currently, there is great interest in the development of highly sensitive bioanalytical systems for diagnosing diseases at an early stage, when pathological biomarkers are present in biological fluids at low concentrations and there are no clinical manifestations. A promising direction is the use of molecular detectors-highly sensitive devices that detect signals from single biomacromolecules. A typical detector in this class is the atomic force microscope (AFM). The high sensitivity of an AFM-based bioanalysis system is determined by the size of the sensing element of an atomic force microscope-the cantilever-the radius of the curvature of which is comparable to that of a biomolecule. Biospecific molecular probe-target interactions are used to ensure detection system specificity. Antibodies, aptamers, synthetic antibodies, and peptides can be used as molecular probes. This study has demonstrated the possibility of using aptamers as molecular probes for AFM-based detection of the ovarian cancer biomarker CA125. Antigen detection in a nanomolar solution was carried out using AFM chips with immobilized aptamers, commercially available or synthesized based on sequences from open sources. Both aptamer types can be used for antigen detection, but the availability of sequence information enables additional modeling of the aptamer structure with allowance for modifications necessary for immobilization of the aptamer on an AFM chip surface. Information on the structure and oligomeric composition of aptamers in the solution was acquired by combining small-angle X-ray scattering and molecular modeling. Modeling enabled pre-selection, before the experimental stage, of aptamers for use as surface-immobilized molecular probes.

Computational design of a smoke detector with high sensitivity considering three-dimensional flow characteristics

Early fire detection is essential for preventing catastrophic events, and fire detectors play a critical role in ensuring fire safety. Among the various types of fire detectors, photoelectric smoke detectors are widely used because of their ability to detect smoke at an early stage. The present study investigates the effects of changes in flow directions and inlet size on the smoke detection ability of a photoelectric smoke detector by conducting computational fluid dynamics (CFD) simulations. The results indicated that when the flow direction changes, the components in the optical chamber may hinder the smoke inflow and increase the detector activation time. Moreover, the smoke inlet size was found to affect the carbon monoxide concentration inside the chamber. The velocity magnitude distributions confirmed the correlation between the smoke velocity in the labyrinth structures entering the optical chamber and the detection ability of the smoke detector. These findings suggested that when optimizing the performance of a smoke detector, flow direction, inlet size, and amount of smoke introduced into the optical chamber should be considered. The current study suggested the modified design of the smoke detector to have superior detection ability in both directions compared to the base case in terms of activation time.

Strategies for revealing deformation and wear mechanisms of polymer composites by combining scanning electron microscopy, energy dispersive x‐ray spectroscopy and cross‐section techniques—Exemplified by glass fiber reinforced poly‐phenylene‐sulphide

AbstractPolymers and polymer composites are frequently used in tribological applications. However, their use is often limited by excessive wear or plastic deformation, therefore research and development aiming to improve the materials is ongoing. Tribological evaluations of polymer composites often study the friction coefficient and wear rate for different types, sizes and amounts of fillers. But rarely are any mechanisms presented. Although polymer materials differ from metals, the techniques typically used for metal components, for example, scanning electron microscopy (SEM) and energy dispersive x‐ray spectroscopy (EDS), can be adapted to polymer materials, to achieve more informative SEM micrographs and EDS analyses. The aim of this article is to present useful analysis strategies, from sample preparation and selection of viewing angles, to selection of instrument settings and detector types. The strategies are exemplified by analysis of poly‐phenylene‐sulphide filled with glass fiber, evaluated against steel in a reciprocating ball‐on‐flat test set‐up. This article takes its starting point with the worn surfaces, and subsequently analyze them using SEM and EDS. A selection of cross section preparation techniques, analysis parameters and microscopy settings are presented and discussed. By combining these techniques and settings, the observation of a strongly modified surface layer, as well as sub‐surface plastic deformation and imbedded wear particles, is facilitated.

Expert teacher based on foundation image segmentation model for object detection in aerial images

Despite the remarkable progress of general object detection, the lack of labeled aerial images limits the robustness and generalization of the detector. Teacher–student learning is a feasible solution on natural image domain, but few works focus on unlabeled aerial images. Inspired by foundation models with the powerful generalization in computer vision field, we propose an expert teacher framework based on foundation image segmentation model called ET-FSM. Our approach provides the performance gains for the student detector by generating high-quality pseudo-labels for unlabeled aerial images. In the ET-FSM, we design the binary detector with expert guidance mechanism to sufficiently leverage the extra knowledge obtained from the foundation image segmentation model, which accurately detects object positions in the complex backgrounds. Also, we present the momentum contrast classification module to distinguish confused object categories in aerial images. To demonstrate the effectiveness of the proposed method, we construct an unlabeled aerial image dataset covering various scenes. The experiments are conducted on diverse types of student detectors. The results show that the proposed approach achieves superior performance compared to existing methods, and allows the student detector to achieve fully supervised performance with much less labeled aerial images. Our dataset and code are available at https://github.com/cq100/ET-FSM.

Configuration Approaches of CFAST for Prediction of Smoke and Heat Detector Activation Times in Corridor Fires

In performance-based design for domestic buildings, there is a growing need for real-time comparison between the Available Safe Egress Time and Required Safe Egress Time through the integration of fire and evacuation simulations. Therefore, the utilization of the Consolidated Model of Fire and Smoke Transport (CFAST) has been discussed as an alternative to the Fire Dynamics Simulator (FDS), which has high computational costs; requires sufficient experience in the numerical calculation of fire dynamics, along with various input parameters; and has limitations in coupling with evacuation simulations. In this study, the prediction performance of CFAST for the activation times of smoke and heat detectors was evaluated. Specifically, it is essential to configure the mass movement between adjacent computational regions for smoke concentration. For achieving adequate predictive performance, the temperature should be determined according to the ceiling jet velocity generated by the fire source. Therefore, a method for setting a computational domain that can produce reasonable prediction results while considering the characteristics of CFAST for different types of smoke and heat detectors is proposed.

METAL DETECTOR STUDY

Treasure hunting is one of the things that people have been interested in and engaged in for many years. Because land is a treasure and extremely important for human life. The land provides us with food and nourishment. Without land and water, there would certainly be no life. People and societies have always relied on land. It was like that in the past and it is like that today. The world has been struggling with wars and social problems for many years. In ancient times, metals such as gold were much more valuable and were even used as money at some point. For this reason, ancient peoples, tribes, and states had a lot of physical gold and similar precious metals. During the war, these precious metals were hidden in the ground in response to the risk of theft or loss, and this was symbolized by certain maps and signs. However, it was not always possible to return to the lands abandoned during social unrest and wars. For this reason, the gold remained in the places where it was buried and has somehow survived to this day in existing maps and similar diagrams. For this reason, people today believe that there is gold in the soil and in certain regions, and in this sense they seriously search for treasure. Today is the age of technology. Taking advantage of technology is one of the most important rules of treasure hunting. The most important technological tools in treasure hunting are detectors. So what is detector logic? How does it work and what types of detectors are there? Let's try to explain this in the article.The most commonly used type of detector is metal detectors. The logic of the metal detector is based on the electronic working principle. These types of detectors work according to the logic of receiving and sending electromagnetic waves. These devices send certain electromagnetic waves to the area to be searched and scanned. When it detects a metal object, it generates a current. This generated current affects the coils and some signal changes start to warn. This generated current reaches the speaker through sound systems and warns the person. Briefly speaking, the logic of the detector can be explained as follows. Different types of metal detectors are also used in this field. One of them is Bfo type detectors. It has a search coil and a single coil at the end. If the coil approaches any metal, the frequency changes and is compared with other frequencies. There are two types of detectors: 2 transistors 1 integrated and 7 transistors. The first has two coils at the search end. And this type of detector is sensitive to the signal. One of the coils is receiving and the other is transmitting. Here again, the collected frequencies reach the coils and the sound is transmitted to the speaker as current. The user receives alerts and directs his work here. Another has a working system that is sensitive to the signal phase. Another different type of detector is the detector based on the pulse system. The logic of the detector in this system is slightly different. There is only one coil in the search coil. A high current pulse is supplied to this coil for a short period of time. This creates a high magnetic field around. After finishing the conversion, it scans the surroundings and sends a signal if it detects something. If there is metal, it makes a loud sound and in this way the seeker works in a certain direction. Keywords: Working principle of metal detector, types of metal detector, IB induction balance detector, PI shock induction detector.

Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection

Colorectal cancer is a leading cause of cancer-related deaths globally. In recent years, the use of convolutional neural networks in computer-aided diagnosis (CAD) has facilitated simpler detection of early lesions like polyps during real-time colonoscopy. However, the majority of existing techniques require a large training dataset annotated by experienced experts. To alleviate the laborious task of image annotation and utilize the vast amounts of readily available unlabeled colonoscopy data to further improve the polyp detection ability, this study proposed a novel self-supervised representation learning method called feature pyramid siamese networks (FPSiam). First, a feature pyramid encoder module was proposed to effectively extract and fuse both local and global feature representations among colonoscopic images, which is important for dense prediction tasks like polyp detection. Next, a self-supervised visual feature representation containing the general feature of colonoscopic images is learned by the siamese networks. Finally, the feature representation will be transferred to the downstream colorectal polyp detection task. A total of 103 videos (861,400 frames), 100 videos (24,789 frames), and 60 videos (15,397 frames) in the LDPolypVideo dataset are used to pre-train, train, and test the performance of the proposed FPSiam and its counterparts, respectively. The experimental results have illustrated that our FPSiam approach obtains the optimal capability, which is better than that of other state-of-the-art self-supervised learning methods and is also higher than the method based on transfer learning by 2.3 mAP and 3.6 mAP for two typical detectors. In conclusion, FPSiam provides a cost-efficient solution for developing colorectal polyp detection systems, especially in conditions where only a small fraction of the dataset is labeled while the majority remains unlabeled. Besides, it also brings fresh perspectives into other endoscopic image analysis tasks.

Analysis of statistical properties of variables in log data for advanced anomaly detection in cyber security

Log lines consist of static parts that characterize their structure and enable assignment of event types, and event parameters, i.e., variable parts that provide specific information on system processes, such as host and user names, IP addresses, and file operations. Many detection approaches only focus on anomalous event type occurrences, i.e., they parse log lines to derive unique event identifiers and subsequently detect anomalies in event sequences or event count vectors, but neglect variable parts of log lines entirely during analysis. This is especially problematic, when monitoring strongly structured log data that contains only a small number of distinct event types, for example, logs that consist of strict key value pairs, i.e., parameters that occur consistently throughout all log lines, such as it is case in access and audit logs. Thus, novel approaches are required, which focus on analysis of log lines' variable parts. In this paper, we propose the variable type detector (VTD), a novel unsupervised approach that autonomously analyzes variable log line parts to enable anomaly detection. It assigns data types to each variable, which also include probability distributions for discrete and continuous variables. The VTD raises an alarm if a variable's data type changes. Furthermore, it implements a robust indicator function that reduces false positives by tracking the data type history of each variable and reports only significant data type changes. Additionally, an event indicator enables event-based anomaly detection by taking into account the data types of all variables of a single event type. The evaluation conducted on open-source log data, demonstrates the effectiveness of the VTD compared to conventional anomaly detection approaches, such as time series analysis and PCA. Consequently, the VTD acts as a solution that extends the intrusion detection capabilities of security information and event management (SIEM) and integrates with modern concepts of endpoint detection and response (EDR) and extended detection and responses (XDR), while simultaneously serving as an asset for process monitoring that supports user and entity behavior analytics (UEBA).

High-Performance Liquid Chromatography (HPLC): Primary Mechanism and Popular Applications

The concept chromatography can be used to refer to a huge variety of distinct analytical procedures. Each of these procedures utilizes a mobile phase and an immobile phase as a basic component, yet they all belong under the chromatography umbrella concept. The subsequent component separation that is carried out is based on the disparity in concentration ratio that can be detected between the two phases. This disparity acts as the basis for the subsequent component separation that is carried out. Due to the limits of current technology, there is presently no detector that is universally applicable to high-performance liquid chromatography (HPLC), it is required to choose a detector in accordance with the characteristics of the chemicals that are to be examined. The measurements of absorbance in ultraviolet and visible spectrum, known as UV-Vis, or disclosing fluorescence are the foundations of the most common types of fluorescence detectors. In HPLC the components of the apparatus consist of a carrier phase, which is typically a solvent with a buffer solution; a pump; an injector; a column; a detector; and an integrator. The column is the most important part of the system where the various components are separated from one another. Based on that, there are many various kinds of columns through which the behavior of the components to be separated by these columns is understood. Due to the fact that chromatography is such a widely used technology.

WFRE-YOLOv8s: A New Type of Defect Detector for Steel Surfaces

During the production of steel, in view of the manufacturing engineering, transportation, and other factors, a steel surface may produce some defects, which will endanger the service life and performance of the steel. Therefore, the detection of defects on a steel surface is one of the indispensable links in production. The traditional defect detection methods have trouble in meeting the requirements of high detection accuracy and detection efficiency. Therefore, we propose the WFRE-YOLOv8s, based on YOLOv8s, for detecting steel surface defects. Firstly, we change the loss function to WIoU to address quality imbalances between data. Secondly, we newly designed the CFN in the backbone to replace C2f to reduce the number of parameters and FLOPs of the network. Thirdly, we utilized RFN to complete a new neck RFN to reduce the computational overhead and, at the same time, to fuse different scale features well. Finally, we incorporate the EMA attention module into the backbone to enhance the extraction of valuable features and improve the detection accuracy of the model. Extensive experiments are carried out on the NEU-DET to prove the validity of the designed module and model. The mAP0.5 of our proposed model reaches 79.4%, which is 4.7% higher than that of YOLOv8s.

Design and optimization of the performance of self-powered Sb2S3 photodetector by SCAPS-1D simulation and potential application in imaging

Antimony sulfide (Sb2S3) is considered an excellent semiconductor material for visible light photodetectors (PDs) due to the non-toxicity, stability and high visible photon absorption coefficient. However, the deep physical cascade relationship between Sb2S3 devices and key parameters, such as the thickness, the doping density, the defect density, and the inverse contact of the figure of merit, has not been thoroughly investigated. In order to systematically explore the impact of parameters on Sb2S3 device performance, self-powered optoelectronic devices of TiO2/Sb2S3 p-n heterojunction were simulated by using one-dimensional (SCAPS-1D) software. Base on simulation, we learned that the optimum thickness of Sb2S3 is about 400 nm, the donor and acceptor densities of TiO2 and Sb2S3 layers are 1020 and 1016 cm−3, respectively. In addition, Au was chosen as the back electrode, and the high-quality Sb2S3 functional layer required a defect density of less than 1016 cm−3. Based on the guidance of simulation results, we finally fabricated the FTO/TiO2/Sb2S3/Au self-powered photodetectors. Under an illumination intensity of 3 μW/cm2 at 530 nm, the responsivity (R) and specific detectivity (D*) of the device can be up to 0.357 A/W and 5.1 × 1011 Jones, respectively. Particularly, the device exhibits a fast response with a response time of 1.4 μs and 3 dB bandwidth of 383 kHz. In this regard, we constructed a single-pixel scanning imaging system around the Sb2S3 detector and showed excellent imaging results, which provided a new path for us to develop a new type of visible light detector and imaging system.

A benchmark for Monte Carlo simulations in gamma-ray spectrometry Part II: True coincidence summing correction factors

The goal of this study is to provide a benchmark for the use of Monte Carlo simulation when applied to coincidence summing corrections. The examples are based on simple geometries: two types of germanium detectors and four kinds of sources, to mimic eight typical measurement conditions. The coincidence corrective factors are computed for four radionuclides. The exercise input files and calculation results with practical recommendations are made available for new users on a dedicated webpage.

Improving the Time Resolution of Large-Area LaBr3:Ce Detectors with SiPM Array Readout

LaBr3:Ce crystals have good scintillation properties for X-ray spectroscopy. Initially, they were introduced for radiation imaging in medical physics with either a photomultiplier or SiPM readout, and they found extensive applications in homeland security and gamma-ray astronomy. We used 1″ round LaBr3:Ce crystals to realize compact detectors with the SiPM array readout. The aim was a good energy resolution and a fast time response to detect low-energy X-rays around 100 keV. A natural application was found inside the FAMU experiment, at RIKEN RAL. Its aim is a precise measurement of the proton Zemach radius with impinging muons, to contribute to the solution to the so-called “proton radius puzzle”. Signals to be detected are characteristic X-rays around 130 KeV. A limit for this type of detector, as compared to the ones with a photomultiplier readout, is its poorer timing characteristics due to the large capacity of the SiPM arrays used. In particular, long signal falltimes are a problem in experiments such as FAMU, where a “prompt” background component must be separated from a “delayed” one (after 600 ns) in the signal X-rays to be detected. Dedicated studies were pursued to improve the timing characteristics of the used detectors, starting from hybrid ganging of SiPM cells; then developing a suitable zero pole circuit with a parallel ganging, where an increased overvoltage for the SiPM array was used to compensate for the signal decrease; and finally designing ad hoc electronics to split the 1″ detector’s SiPM array into four quadrants, thus reducing the involved capacitances. The aim was to improve the detectors’ timing characteristics, especially falltime, while keeping a good FWHM energy resolution for low-energy X-ray detection.

An irradiation system for nuclear and materials research in a medical cyclotron

Development and commissioning of an irradiation system and experimental setup for nuclear research and materials analysis in a medical cyclotron facility is described. The setup, Low Energy in-Vacuum Irradiation System-LEVIS, is installed at the end of the existing R&D beamline. The components of LEVIS and their functions are explained and the characteristics such as energy, current and profile of the beam are measured. Different types of radiation detectors are installed and tested in the chamber for future applications of non-destructive analysis methods.