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Synthetic amorphous silica (SAS) is used as additive in a variety of industrial applications for many decades and has been approved to be used in food, food contact materials, pharmaceuticals, and cosmetics. Due its internal structure, SAS is considered as a nanomaterial, thus it is affected by a general safety discussion. Based on the production process, SAS for cosmetic application is a nanomaterial by the EU Recommendation, although it was not considered as such, because the solely size-dependent definitions of the term "nanomaterial" emerged in recent times first in Recommendation 2011/696/EU. Therefore, former physicochemical and toxicological evaluations of SAS were already performed on nanomaterials, however, without being addressed as such. Safety concerns can only emerge if two criteria, (toxicological) hazard and exposure towards the substance is fulfilled at the same time. In case of SAS, the Scientific Committee on Consumer Safety (SCCS) challenged provided data to be insufficient to draw a conclusion regarding the safety of SAS and thus, requested further investigations, in particular by exploring skin penetration of particulate SAS.Investigation of specific particulate substances in skin penetration tests is an analytical challenge. The number of available analytical techniques that are capable to detect nanomaterials in complex matrices, like receptor fluids from skin penetration testing, are limited and still emerging. In the new studies, a comprehensive set of analytical techniques were used to investigate the skin penetration potential of SAS. Particle-sensitive, element and particle-specific combinations of techniques and different sample preparation procedures, that respected the particulate nature of SAS, were used to detect SAS in receptor fluids directly. In addition, electron microscopic techniques were used to examine different layers of skin to detect adsorbed SAS.The combination of Asymmetric Flow Field-Flow Fractionation (AF4) in combination with Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for examination of receptor fluids and Scanning Electron Microscopy coupled with Energy Dispersive X-ray spectroscopy (SEM/EDX) for examination of skin itself, were identified as suitable techniques for the detection of SAS in skin penetration tests. Data from literature was used to compare the results of the studies with the outcome of other test systems (other particles, other techniques). Both, the test results, and literature evaluation led to the conclusion, that SAS does not penetrate skin. Based on this outcome and local and systemic dermal toxicity review of SAS, it can be concluded that dermal application of SAS in cosmetic formulations is negligible. • New analytical systems to detect particles in report fluids are presented. • Synthetic amorphous particles do not penetrate through pig and human skin. • A critical literature review is provided to understand different results reported. [ABSTRACT FROM AUTHOR]

The automated search software integrated with a scanning electron microscope (SEM/EDS) has been the standard tool for detecting inorganic gunshot residues (iGSR) for several decades. The detection of these particles depends on various factors such as collection, preservation, contamination with organic matter, and the method for sample analysis. This article focuses on the influence of equipment resolution setup on the backscattered electron images of the sample. The pixel size of these images plays a crucial role in determining the detectability of iGSR particles, especially those with sizes close to the pixel size. In this study, we calculated the probability of missing all characteristic iGSR particles in a sample using an SEM/EDS automated search and how it depends on the image pixel resolution setup. We developed and validated an iGSR particle detection model that links particle size with equipment registers and applied it to 320 samples analyzed by a forensic science laboratory. Our results show that the probability of missing all characteristic iGSR particles due to their size is below 5% for pixel sizes below 0.32 μm2. These findings indicate that pixel sizes as large as twice the one commonly used in laboratory casework, that is, 0.16 μm2, are effective for initial sample scanning, yielding good detection rates of characteristic particles that could exponentially reduce laboratory workload. [ABSTRACT FROM AUTHOR]

Identifying large particles are a prerequisite for slurry health monitoring. This study presents a real-time monitoring system using an infrared coaxial light source to detect large particles in slurry. The imaging system acquires real-time images under infrared light, which are processed and analyzed to quantify the particles within the fluid flowing through the monitored area. The proposed approach consists of different illumination modes—fixed, random, and continuous. For particles with regular and irregular shapes, various scintillation modes are applied. The results show that the method is highly feasible and effective, and enables real-time monitoring of the slurry. Moreover, the real-time detection approach for different particle samples, particularly, undiluted opaque fluids, is proposed. The feasibility of the proposed method is verified in the irregular particle detection experiment, where the difference between the detection value and the real value is very small. The accuracy and intuitive properties of the method indicate a massive potential across diverse applications in the forthcoming years. [ABSTRACT FROM AUTHOR]

A lightweight Mask R-CNN instance segmentation model was developed here to analyze particle size and shape accurately and quickly. Firstly, a hybrid Depthwise Dilated Convolutional Network (DDNet) is proposed, and the feature pyramid layers and the shared convolutional layers of the region proposal network are simplified, reducing the model complexity while ensuring robust feature extraction capabilities. Then, segmentation accuracy is significantly improved without sacrificing computational speed and performance by introducing the Dice loss function and clustering algorithm. Experimental results show that the model parameters are significantly reduced by 49.46%, and the segmentation speed increases from 2.15 FPS (frames per second) to 5.88 FPS. Meanwhile, the segmentation accuracy (AP50) increased from 90.56% to 91.21%. In addition, it was proven that the particle size distribution and shape could be analyzed accurately and rapidly with the proposed model, providing essential information for multiphase flow process optimization and equipment design in industrial applications. [Display omitted] • A lightweight Mask R-CNN instance segmentation model was proposed. • Adopting lightweight hybrid network increases segmentation speed markedly. • Loss function and candidate box significantly improved segmentation accuracy. • Particle size distribution and shape were measured accurately and rapidly. [ABSTRACT FROM AUTHOR]

In the context of the CERN EP R&D on monolithic sensors and the ALICE ITS3 upgrade, the Tower Partners Semiconductor Co (TPSCo) 65 nm process has been qualified for use in high energy physics, and adopted for the ALICE ITS3 upgrade. An Analog Pixel Test Structure (APTS) featuring fast per pixel operational-amplifier-based buffering for a small matrix of four by four pixels, with a sensor with a small collection electrode and a very non-uniform electric field, was designed to allow detailed characterization of the pixel performance in this technology. Several variants of this chip with different pixel designs have been characterized with a 120 GeV / c positive hadron beam. This result indicates that the APTS-OA prototype variants with the best performance achieve a time resolution of 63 ps with a detection efficiency exceeding 99% and a spatial resolution of 2 μm, highlighting the potential of TPSCo 65 nm CMOS imaging technology for high-energy physics and other fields requiring precise time measurement, high detection efficiency, and excellent spatial resolution. [ABSTRACT FROM AUTHOR]

Current methods used for testing the rehydration quality of infant formula (IF) are mainly subjective. For a better understanding of rehydration, objective measurements are required. A computer vision (CV) system was synchronized with a collaborative robot (cobot) to automatically estimate foam height, sediment height, and the number of white particles after IF powder rehydration. Two different robotic agitations were used to prepare the mixtures in a commercially available baby bottle. To evaluate the platform, twenty-four stage-1 IF powders were rehydrated. Cobot-captured images were processed by CV algorithms and independently rated by eight participants. The participants' and platform's estimates of foam height, sediment height, and white particles score, respectively, showed agreements of 2.1 mm, 3.4 mm, and 1.7 scores, and correlation coefficients of 0.82, 0.77, and 0.68. The results show that the platform has the potential to enable objective rehydration tests and to monitor changes in visible foam and sediment over time. [Display omitted] • An automated platform for testing infant formula rehydration quality was developed. • Using computer vision, the platform estimated three rehydration attributes. • Platform estimates were compared to estimates from eight human participants. • Reference "white particles" images were digitally generated for participants. • The platform can monitor foam and sediment levels over time. [ABSTRACT FROM AUTHOR]

Analogue test structures were fabricated using the Tower Partners Semiconductor Co. CMOS 65 nm ISC process. The purpose was to characterize and qualify this process and to optimize the sensor for the next generation of Monolithic Active Pixels Sensors for high-energy physics. The technology was explored in several variants which differed by: doping levels, pixel geometries and pixel pitches (10–25 μ m). These variants have been tested following exposure to varying levels of irradiation up to 3 MGy and 1 0 16 1 MeV n eq cm−2. Here the results from prototypes that feature direct analogue output of a 4 × 4 pixel matrix are reported, allowing the systematic and detailed study of charge collection properties. Measurements were taken both using 55Fe X-ray sources and in beam tests using minimum ionizing particles. The results not only demonstrate the feasibility of using this technology for particle detection but also serve as a reference for future applications and optimizations. [ABSTRACT FROM AUTHOR]

Wear is an unavoidable phenomenon that generates debris particles. i.e., ferromagnetic and non-ferromagnetic particles. If the system's condition is not monitored effectively, wear can lead to the breakdown of the system. Therefore, an inductive-resistance sensor was designed to monitor and detect debris particles. Moreover, Finemet (Fe 73.5 Si 13.5 B 9 Nb 3 Cu 1) was applied to the sensor to improve its sensitivity. A comparison simulation and experiment examined the sensor's performance and Finemet's impact. The study shows that the induction mode has greater sensitivity in detecting ferromagnetic particles, while the resistance mode is more sensitive in detecting non-ferromagnetic particles. This study also demonstrates that the application of Finemet boosts the sensitivity of the sensors in both findings, indicating the significance of Finemet for the sensor's applications. [ABSTRACT FROM AUTHOR]

• Impact of Solid Particles in Transformer Oil: The text highlights the significant influence of solid particles in transformer oil on insulation strength, indicating that these particles can form weak points in insulation, leading to local discharge exceeding limits or insulation breakdown. • Emphasis on Clean New Transformer Oil: Manufacturing companies are encouraged to prioritize the cleanliness of new transformer oil, using methods such as vacuum filtration to reduce the introduction of impurity particles, thereby enhancing the reliability of transformer oil. • Innovative No-Lens Holographic Microscope: The text introduces the rapid development of lensless imaging systems based on CMOS image sensors, which address the high cost and lack of portability associated with traditional high-resolution optical microscopes, providing an effective solution for downsized microscopic imaging and detection. • Simplicity and Speed of Operation: It emphasizes that the lensless holographic microscope is characterized by its simplicity of operation, rapid measurement, and high precision, making it a convenient tool for assessing the condition of impurity particles in power transformers and preemptively identifying issues. • Enhancing Power Transformer Reliability: By employing this novel detection method, power transformer manufacturing companies can more effectively control and manage impurity particles in transformer oil, ultimately improving the reliability of power transformers, reducing maintenance costs, and extending equipment lifespan. The insulation effectiveness of transformer oil has a substantial impact on the safety of transformers. In the presence of particles within the transformer oil, they are able to adversely affect its performance, potentially originating from electrical arcs or severe carbonization processes. These particles are commonly capable of reducing the insulating capability of the transformer oil, thereby enhancing the risk of transformer failure. To overcome this dilemma, we propose a miniaturized quantitative particle detection approach in transformer oil based on a lensless digital holographic microscope. In general, methods based on traditional high-resolution optical microscopy are essentially limited by their fairly huge cost and importability. However, the proposed approach utilizes a homemade miniaturized model combined with a CMOS image sensor to achieve microscopic imaging. The deployed device here weighs ∼ 50 g, making it portable and affordable for on-the-go detection. By simplifying the microscope system, we can quickly and accurately detect tiny particles in transformer oils. This enables us to efficiently assess the status of impurities in power transformers and to identify potential issues in advance, thereby enhancing the reliability of transformer oil. This detection method not only features simplicity of operation, convenient on-the-go detection, low cost, and high precision but also provides a convenient approach for accurately assessing the condition of impurity particles in power transformers and preemptively identifying potential issues. [ABSTRACT FROM AUTHOR]

Monolithic sensors, and CMOS sensors in particular, revolutionized visible imaging, and will be widely applied in high energy physics, but with different requirements and implementations than sensors for visible light (Snoeys, 2023). [ABSTRACT FROM AUTHOR]

An automated hologram acquisition system for big-data analysis and for improving the statistical precision of phase analysis has been upgraded with automated particle detection technology. The coordinates of objects in low-magnification images are automatically detected using zero-mean normalized cross-correlation with preselected reference images. In contrast with the conventional scanning acquisitions from the whole area of a microgrid and/or a thin specimen, the new method allows efficient data collections only from the desired fields of view including the particles. The acquisition time of the cubic/triangular nanoparticles that were observed was shortened by about one-fifty eighth that of the conventional scanning acquisition method because of efficient data collections. The developed technology can improve statistical precision in electron holography with shorter acquisition time and is applicable to the analysis of electromagnetic fields for various kinds of nanoparticles. [ABSTRACT FROM AUTHOR]

Particle identification and shape evaluation of granular materials from their realistic packing images are challenging and of great interest to many engineers and researchers. In this study, a systematic tool is developed based on computing techniques, including deep learning and computational geometry. First, image datasets of the target granular particles with well-labeled masks are established. The Mask Region Convolutional Neural Network (Mask R-CNN) is employed to implement the end-to-end instance segmentation and contour extraction of particles on different realistic images. Since Mask R-CNN models have several different feature extraction backbones, the optimal model is selected and then trained on the established datasets using transfer learning technique. After the particles are successfully identified from images of cobble and ballast, the elongation, angularity, and roughness are evaluated and the statistical shape analysis is conducted. The proposed method has strong generalization ability, especially for densely-packed particles. [Display omitted] • Automated particle identification using the Mask R-CNN model. • Successful instance segmentation of densely-packed granular particles. • Application to automated shape evaluation of ballast and cobble samples. [ABSTRACT FROM AUTHOR]

The correlation of molecular neuroimaging and behavior studies in preclinical PET imaging is of major interest to unlock progress in the understanding of brain processes and assess the validity of preclinical studies in drug development. However, fully achieving this ambition requires performing molecular images of awake and freely moving animals, whereas most of the preclinical imaging procedures are currently performed under anesthesia. To overcome this issue, the MAPSSIC project aims to develop a pixelated intracerebral probe to be implanted into awake and freely moving rats. The aforementioned probe relies on IMIC (Imageur Moléculaire Intra Cérébral), a Monolithic Active Pixel Sensor (MAPS) prototype set to directly detect positrons. The IMIC sensors were produced in 5 different configurations. Measurements using a 204Tl source showed that the sensor parameters can be optimized to boost its performance allowing to increase the sensitivity and reduce the average cluster size. In addition, comparisons between sensor configurations show a clear gain provided by the introduction of CMOS process modifications. Finally, the choice of the optimal sensor configuration will depend on the expected in vivo conditions. [ABSTRACT FROM AUTHOR]

Monolithic active pixel sensors (MAPS) produced in a 65 nm CMOS imaging technology are being investigated for applications in particle physics. The MAPS design has a small collection electrode characterized by an input capacitance of ∼ fF, granting a high signal-to-noise ratio and low power consumption. Additionally, the 65 nm CMOS imaging technology brings a reduction in material budget and improved logic density of the readout circuitry, compared to previously studied technologies. Given these features, this technology was chosen by the TANGERINE project to develop the next generation of silicon pixel sensors. The sensor design targets temporal and spatial resolutions compatible with the requirements for a vertex detector at future lepton colliders. Simulations and test-beam characterization of technology demonstrators have been carried out in close collaboration with the CERN EP R&D program and the ALICE ITS3 upgrade. TCAD device simulations using generic doping profiles and Monte Carlo simulations have been used to build an understanding of the technology and predict the performance parameters of the sensor. Technology demonstrators of a 65 nm CMOS MAPS with a small collection electrode have been characterized in laboratory and test-beam facilities by studying performance parameters such as cluster size, charge collection, and efficiency. This work compares simulation results to test-beam data. The experimental results establish this technology as a promising candidate for a vertex detector at future lepton colliders and give valuable information for improving the simulation approach. [ABSTRACT FROM AUTHOR]

Silicon Photomultipliers (SiPMs) are state-of-the-art photon detectors used in particle physics, medical imaging, and beyond. They are sensitive to individual photons in the optical wavelength regime and achieve time resolutions of a few tens of picoseconds, which makes them interesting candidates for timing detectors in tracking systems for particle physics experiments. The Geiger discharges triggered in the sensitive elements of a SiPM, Single-Photon Avalanche Diodes (SPADs), yield signal amplitudes independent of the energy deposited by a photon or ionizing particle. This intrinsically digital nature of the signal motivates its digitization already on SPAD level. A digital SiPM (dSiPM) was designed at Deutsches Elektronen Synchrotron (DESY), combining a SPAD array with embedded CMOS circuitry for on-chip signal processing. A key feature of the DESY dSiPM is its capability to provide hit-position information on pixel level, and one hit time stamp per quadrant at a 3 MHz readout-frame rate. The pixels comprise four SPADs and have a pitch of about 70 μm. The four time stamps are provided by 12 bit Time-to-Digital Converters (TDCs) with a resolution better than 100 ps. The chip was characterized in the laboratory to determine dark count rate, breakdown voltage, and TDC characteristics. Test-beam measurements are analyzed to assess the DESY dSiPMs performance in the context of a 4D-tracking applications. The results demonstrate a spatial hit resolution on a pixel level, a minimum-ionizing particle detection efficiency of about 30 % and a time resolution in the order of 50 ps. [ABSTRACT FROM AUTHOR]

When optical navigation images acquired by the OSIRIS‐REx (Origins, Spectral Interpretation, Resource Identification, and Security‐Regolith Explorer) mission revealed the periodic ejection of particles from asteroid (101955) Bennu, it became a mission priority to quickly identify and track these objects for both spacecraft safety and scientific purposes. The large number of particles and the mission criticality rendered time‐intensive manual inspection impractical. We present autonomous techniques for particle detection and tracking that were developed in response to the Bennu phenomenon but that have the capacity for general application to particles in motion about a celestial body. In an example OSIRIS‐REx data set, our autonomous techniques identified 93.6% of real particle tracks and nearly doubled the number of tracks detected versus manual inspection alone. Key Points: We describe autonomous techniques for the identification and tracking of particles in motion about a celestial bodyWe demonstrate these techniques using images from the OSIRIS‐REx mission to the active asteroid (101955) BennuIn the OSIRIS‐REx dataset, our autonomous algorithms detected 93.6% of real particle tracks, including 244 tracks not identified by manual inspection [ABSTRACT FROM AUTHOR]

• A wavelet-EMD denoising method is developed for strong gas turbulence noise. • A multiscale statistical method is established to enhance weak sand signals. • Weak sand information in strong noise is characterized by deep learning algorithms. • A quantitative model to detect solid particles in sand-gas flow is constructed. A method to quantitate sand particles in turbulent gas flow that combines the multiscale triaxial vibration response and deep learning algorithms is proposed. First, an optimized adaptive wavelet-empirical mode decomposition (EMD) denoising method is proposed based on multifrequency coherent and statistical analysis. Second, complex gas–solid turbulent flow information under multiple gas–particle coupling is characterized based on Hilbert-Huang transform (HHT), Hurst analysis, EMD entropy, etc. In addition, a deep learning algorithm that integrates multiscale flow information to determine sand content that includes two independent branches, a pure deep convolutional neural network (CNN) model driven by microscale triaxial response and a shallow long short-term memory (LSTM) network with regularization driven by mesoscale triaxial response, is proposed. Finally, a quantitative model to characterize sand-carrying turbulent gas flow based on the entropy weight effect of the triaxial vibration response is constructed as follows: C sand = A · p. ∑ i = x z S i Q i . Experimental validation indicates that the proposed deep learning algorithm has recognition and prediction accuracy of 97.8% and 96.97% for sand particle size and the power spectrum, respectively, which are higher than those of the existing intelligent models to characterize sand information. Moreover, the quantitative sand content model based on the multiscale response and deep learning algorithm has a maximum error of only 1.56% under strong turbulence. [ABSTRACT FROM AUTHOR]

Purpose: High-altitude ice crystals (HAICs) are causing one in-flight event or more per month for commercial aircraft. The effects include preventing air data probes (pitot pressure and total air temperature in particular) from functioning correctly and causing engines to roll back and shut down. The purpose of this study is to describe the process used by the National Research Council Canada (NRC) to develop and test a particle detection probe (PDP). The probe mounts on the fuselage of aircraft to sense and quantify the ice crystals in the environment. Design/methodology/approach: The probe was demonstrated on the NRC Convair and Airbus A340 research aircraft as part of the European Union HAIC programme. The probe was ruggedised, adapted for easy installation in standard aircraft fittings and tested in a variety of conditions for longevity and endurance. Findings: Efforts to achieve the safety requirements for flight on aircraft are discussed. The challenges, surprises and opportunities for testing on which the development group is capitalised are also presented. Practical implications: It was demonstrated that the detectors gave signals proportional to the ice crystal content of clouds, and results demonstrating the functionality of the probe are presented. Originality/value: This paper describes the multi-year process of developing the NRC PDP from a test cell sensor for detecting engine exhaust contaminants on an aircraft ice crystal detection probe. The work included over 20 flight tests on NRC aircraft and the Airbus HAIC test programme. [ABSTRACT FROM AUTHOR]

Diagnostic methods using a tomographic reconstruction allow for three-dimensional characterization of spraying processes. Since in many spraying applications, non-rotationally symmetric phenomena occur, the application of emission tomography methods seems most reasonable. Yet, to be able to apply this method the observed object has to be luminous and optically thin. Hence, to investigate a particle stream, the optical thickness of such a stream has to be estimated beforehand. Within this paper, a method to determine this parameter, based on particle rate, size and velocity measurements is presented and applied to a wire arc spray process, proving that it is possible to perform a tomographic investigation of a particle beam. [ABSTRACT FROM AUTHOR]

Dark-field illumination is a simple yet elegant imaging technique that can be used to detect the presence of particles on a specular surface. However, the sensitivity of dark-field illumination to initial conditions affects its repeatability. This is problematic in cases where automation is desired. We present an improvement to the current method of using a modulation field that relies on phase calculations rather than intensity. As a result, we obtain a computational method that is insensitive to noise and provides clearly defined particle information, allowing a global threshold to be set for autonomous measurement purposes. After introducing the theory behind our method, we present experimental results for various scenarios and compare them to those obtained using the dark-field approach. [ABSTRACT FROM AUTHOR]

• Detection of particles and classification according to their size. • Construction of a velocity distribution function characterizing the motion of particles within each distinct size group. • Determination of particle shape and orientation. • Application to the complex flow of red blood cells with agglomeration and breakage. • Through verification of the software for this application. Over the past several decades, image processing and automated tracking of particles has emerged as a useful tool for the study of biological particles behaviour. This article describes an open-source computational implementation of a method for determining particle velocity and size distributions of large groups of particles by analyzing video sequences acquired using a video-microscopic systems. Although, in this study, red blood cells are used as a subject, this implementation can be used for any particle-laden flow where particles present a range of sizes and details of the velocity distribution of each size is of interest. From each single image, the current program detects particles and classifies them according to their size. It uses sequential images to track particles and compute the instantaneous velocity distribution of the particles. The tool can also assign an ellipse to each particle and report the major axis, the minor axis and the orientation of particles in each image. Use of the program improves repeatability of image processing and is suitable for studies related to particle dynamics, colloids, and microfluidic flow measurement. The size distribution and the velocity distribution of particles is often useful in the study of effect of parameters like shear stress on particle collision rate, agglomeration and breakage rate. [ABSTRACT FROM AUTHOR]

Abstract Monolithic active pixel sensors manufactured in commercial CMOS technologies revolutionized imaging for consumer applications and are now moving into high energy physics: they have been adopted in the STAR and the ALICE experiments, and are being considered for the most demanding applications like the upgrade of the ATLAS pixel detector. These devices offer integration of sensor and readout in a single chip and low material budget. Deep submicron commercial CMOS technologies offer high speed and density, leading to high bandwidth capability and good spatial resolution for monolithic sensors, and volume production capability at low cost, important for large systems. This paper tries to give an overview. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a System on Programmable Chip (SoPC) design of a digitizer to determine particle features in nuclear physics covering arrival time and energy also for pileup events. The preamplified pulses from the radiation detector are digitized with a rate of 125 Ms/s. Pulse triggering and arrival time is measured by analysis of the pulse output after CR-RC filtering. Trapezoidal pulse shaping is applied for pulse-height energy measure and noise suppression. A new method is presented for trapezoidal flat top height analysis to ease calibration of the trapezoidal pulse shaping filter. The presented method also improves pulse analysis in terms of pileup identification and false pulse rejection. Experimental results obtain a repetitive pulse rate of 50 kHz. The digitizer is able to detect pileup events with a delay between pulses down to few micro seconds. [ABSTRACT FROM AUTHOR]

This paper is a pedagogical yet critical introduction to the quantum description of unstable systems, mostly at the level of a graduate quantum mechanics course. Quantum decays appear in many different fields of physics, and their description beyond the exponential approximation is the source of technical and conceptual challenges. In this article, we present both general methods that can be adapted to a large class of problems, and specific elementary models to describe phenomena like photo-emission, beta emission and tunneling-induced decays. We pay particular attention to the emergence of exponential decay; we analyze the approximations that justify it, and we present criteria for its breakdown. We also present a detailed model for non-exponential decays due to resonance, and an elementary model describing decays in terms of particle-detection probabilities. We argue that the traditional methods for treating decays face significant problems outside the regime of exponential decay, and that the exploration of novel regimes of current interest requires new tools. [ABSTRACT FROM AUTHOR]

Abstract A complete reconstruction scheme for synthetic aperture in-line digital holograms with seams is proposed. The width of seams and the stitching error are first estimated by an algorithm combined image matching with phase factor. Then a method based on the shift theorem of the Fourier transform is presented to correct the stitching error of the adjacent sub-holograms. After being correct synthesis, means prefilled synthetic aperture digital holograms are reconstructed by using improved phase retrieval algorithm. The particle detection experiment is conducted. The result shows lost particle in seams are recovered and the correlation coefficient between the reconstructed image and the particles target image tripled in contrast to the traditional method. The feasibility of the scheme is verified. [ABSTRACT FROM AUTHOR]

Lorentz force velocimetry (LFV) is a non-contact electromagnetic flow measurement technique to measure the flow rate of aggressive or high-temperature molten metal, such as steel or aluminum, and to give information about the local flow. In this paper, we present an extension of the LFV to detect solid particles in a conducting liquid metal based on the Lorentz force signals. The results are verified by magnetohydrodynamics volume element method using ANSYS FLUENT and validated by measurements. [ABSTRACT FROM AUTHOR]

CMOS cameras revolutionized the visible imaging world, and now also move into other fields. After years of intensive research with significant progress and introduction in a few experiments, monolithic active pixel sensors integrating sensor matrix and readout in one piece of silicon are considered for wider application in high energy physics. Detailed requirements and implementation of CMOS sensors in high energy physics differ from those for visible light. This paper tries to give an overview. • Monolithic Active Pixel Sensors (MAPS) will be used widely in High Energy Physics (HEP). • CMOS MAPS for HEP greatly profit from the progress of CMOS imagers for visible light. • Tolerance up to several 1016 1 MeV n e q /cm 2 and several MGy is within reach. • Design for yield and lower power densities will enable stitching and wafer stacking. • Efficient volume test, assembly, and mounting will enable large area detectors. • Experts in digital-on-top design, verification and TCAD are essential for MAPS in HEP. • Large area pixel sensors are enabling devices for many cutting edge research fields. • MAPS production volume for HEP should allow access to the most advanced technologies. • Our HEP community can have an impact on society through its MAPS development. [ABSTRACT FROM AUTHOR]

Within the ATTRACT FASTPIX project, a monolithic pixel sensor demonstrator chip was developed in a modified 180 nm CMOS imaging process technology, targeting sub-nanosecond timing precision for single ionizing particles. It features a small collection electrode design on a 25 μ m-thick epitaxial layer and contains 32 mini matrices of 68 hexagonal pixels each, with pixel pitches ranging from 8.66 to 20 μ m. Four pixels are transmitting an analog output signal and 64 are transmitting binary hit information. Various design variations are explored, aiming at accelerating the charge collection and making the timing of the charge collection more uniform over the pixel area. Signal treatment of the analog waveforms, as well as reconstruction of digital position, time and charge information, is carried out off-chip. This contribution introduces the design of the sensor and readout system and presents performance results for various pixel designs achieved in recent test-beam measurements with external tracking and timing reference detectors. A time resolution below 150 ps is obtained at full efficiency for all pixel pitches. [ABSTRACT FROM AUTHOR]

Abstract The emergence of the Time Projection Chamber concept in 1974 is an important chapter in the history of particle detection advances. Recalling an era very different from today, I describe the flow of events from the moment of inspiration through prototypes to realization as a major detector for the Positron–Electron Project (PEP) at SLAC. The sense of excitement in bringing forth a genuine paradigm shift carried the collaboration forward, through struggles, victories, defeats and exhaustion, yet ultimately leading to success. [ABSTRACT FROM AUTHOR]

[Display omitted] • The magnetic coupling effect in debris clusters changes the energy variation. • The aggregation of debris with the same magnetic properties leads to false alarms. • The aggregation of debris with different magnetic properties causes missing alarms. • Increasing the field frequency rises the probability of missing alarm failure. • The research lay the foundation of electromagnetic high-precision debris detectors. The electromagnetic wear particle detector is one of effective method to monitor wear debris in lubricating oil for assessing the wear condition of mechanical equipment. However, the motion of wear particles, especially the aggregation behavior in both fluid field and magnetic field, may make the particle detector generate false wear signals. Therefore, to estimate the impact on the detection accuracy of wear debris by the particle aggregation effect, the magnetic coupling model of multiple wear particles is established for studying the magnetic coupling effect between adjacent metal particles. The research results show that the effect changes the total magnetic energy variation induced by the particles and then affects the amplitude of particle signal. Meanwhile, the variation degree is closely associated with the frequency of magnetic field of particle detectors. Overall, the aggregation of wear particles with the same magnetic properties (both ferromagnetic or non-ferromagnetic) leads to false alarms of particle detectors; and the aggregation of wear particles with different magnetic properties causes missing alarms of particle detectors. Meanwhile, increasing the field frequency may increases the probability of missing alarm failure of particle detectors. [ABSTRACT FROM AUTHOR]

Hybrid pixel detectors were developed to meet the requirements for tracking in the inner layers at the LHC experiments. With low input capacitance per channel (10–100 fF) it is relatively straightforward to design pulse processing readout electronics with input referred noise of ∼ 100 e-rms and pulse shaping times consistent with tagging of events to a single LHC bunch crossing providing clean ‘images’ of the ionising tracks generated. In the Medipix Collaborations the same concept has been adapted to provide practically noise hit free imaging in a wide range of applications. This paper reports on the development of three generations of readout ASICs. Two distinctive streams of development can be identified: the Medipix ASICs which integrate data from multiple hits on a pixel and provide the images in the form of frames and the Timepix ASICs who aim to send as much information about individual interactions as possible off-chip for further processing. One outstanding circumstance in the use of these devices has been their numerous successful applications, thanks to a large and active community of developers and users. That process has even permitted new developments for detectors for High Energy Physics. This paper reviews the ASICs themselves and details some of the many applications. [ABSTRACT FROM AUTHOR]

In an inductive sensor, relative movement occurs between a fixed coil and a metal target or another coil, and inductive sensors are often used to measure moving, rotating and reciprocating targets. However, the effect of this relative movement on the detection capabilities of inductive sensors has not previously been investigated. Therefore, the influence of velocity on the sensitivity of inductive sensors for metal particle detection should be addressed to improve the performance of inductive sensors in terms of their sensitivity for metal particle detection and in other aspects of their operation. This paper presents a theoretical analysis and experimental validation of the dependence of the sensitivity of an inductive sensor on the velocity of the particles it is measuring. The results indicate that the sensitivity to particles can be significantly improved by decreasing their velocity. When the velocity of the particles is decreased from 2.9×10-2 m/s to 5.8×10-3 m/s, the inductance variation of the coil increases from 0.43 nH to 0.81 nH, corresponding to a sensitivity enhancement of 87.5%. [ABSTRACT FROM AUTHOR]

A new generation of Monolithic Active Pixel Sensors (MAPS), produced in a 65 nm CMOS imaging process, promises higher densities of on-chip circuits and, for a given pixel size, more sophisticated in-pixel logic compared to larger feature size processes. MAPS are a cost-effective alternative to hybrid pixel sensors since flip-chip bonding is not required. In addition, they allow for significant reductions of the material budget of detector systems, due to the smaller physical thicknesses of the active sensor and the absence of a separate readout chip. The TANGERINE project develops a sensor suitable for future Higgs factories as well as for a beam telescope to be used at beam-test facilities. The sensors will have small collection electrodes (order of µm) to maximize the signal-to-noise ratio, which makes it possible to minimize power dissipation in the circuitry. The first batch of test chips, featuring full front-end amplifiers with Krummenacher feedback, was produced and tested at the Mainzer Mikrotron (MAMI) at the end of 2021. MAMI provides an electron beam with currents up to 100 µA and an energy of 855 MeV. The analog output signal of the test chips was recorded with a high bandwidth oscilloscope and used to study the charge-sensitive amplifier of the chips in terms of waveform analysis. A beam telescope was used as a reference system to allow for track-based analysis of the recorded data. [ABSTRACT FROM AUTHOR]

This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art. [ABSTRACT FROM AUTHOR]

Colloidal Fe-zeolites such as Fe-BEA-35 are currently under study as new adsorbent and catalyst materials for in-situ chemical oxidation with H 2 O 2 . As for nanoremediation in general, the availability of suitable particle detection methods is a requirement for successful process development and particle tracing. Detection and distinguishing between natural colloids and introduced particles with a similar composition are a challenge. By means of fluorescence labelling, a highly specific detection option for Fe-BEA-35 was developed. ‘Ship-in-a-bottle’ synthesis of fluorescein within the zeolite pores, which was applied for the first time for a BEA type zeolite, provides a product with stable and non-extractable fluorescence. When the fluorescent labelled zeolite is added at a concentration of 1 wt.% referring to the total zeolite mass, a very low detection limit of 1 mg/L of total zeolite is obtained. Compared to commonly applied turbidity measurements, detection via fluorescence labelling is much more specific and sensitive. Fluorescence is only marginally affected by carboxymethyl cellulose, which is frequently applied as stabilizer in application suspensions but will be depleted upon contact with H 2 O 2 . Transport properties of fluorescent labelled and non-labelled Fe-zeolite particles are in agreement as determined in a column study with quartz sand and synthetic groundwater (classified as very hard). [ABSTRACT FROM AUTHOR]

Oil particle sensors can provide valuable information about the conditions of mechanical devices by detecting the metallic particles falling from machine components into lubricating oil due to fatigue, corrosion and pitting. However, the vibration interferences and background noise in the signals from a particle sensor make it difficult to precisely detect the particle quantity and size, and thus lead to misleading detection results. To enhance the capabilities of oil particle sensors, resonance-based signal decomposition is employed in this study to separate the output signal of the oil particle sensor into two components, i.e., the high-resonance and the low-resonance components. Since the high- and the low-resonance components reflect the harmonic signal components and the impulse signal components contained in the original signal respectively, only the low-resonance component contains the particle signal contents. Then the fractional calculus technique is applied to extract the oil particle signature from the low-resonance component. In this way, the interferences can be eliminated and the particle signature can be reliably extracted. The performance of the proposed approach has been validated by both simulated and experimental data. [ABSTRACT FROM AUTHOR]

Detecting particles in intracellular images and estimating their radii is important for investigating the causes of clinical conditions. Presently, particles are manually counted by observers, which is time consuming and subjective. Thus, we propose an automatic particle counting and radius estimation method based on pattern recognition techniques. Particles are analyzed by a computer in two processes. During the first process, particles are detected using face recognition via a support vector machine. In the second process, the edge points of a detected particle are distinguished by the intensity difference between the particle and the background. The edge points are extracted and fitted by a circle by a voting technique. The proposed method accurately located 96.04% of the particles detected by manual counting. ImageJ, the particle detection software generally used in cell biology, detected 72.32% of the particles. Therefore, our method yields higher accuracy than ImageJ. Moreover, the effectiveness of radius estimation was also experimentally verified. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Neutron flux measure systems perform an important role in both nuclear power plants and thermonuclear reactors. However, strong neutron sources are necessary to test these modern neutron flux monitoring systems. Due to the dangerous nature of neutron radiation, developers opt for simulated neutron signals instead. This paper details the design of a random pulse signal generator capable of perfectly reproducing high count rate neutron signals (up to 109 counts per-second) for the purpose of testing flux monitoring systems in-lab without any radiation source involved. Fully digitalized waveforms were created within FPGA to assure that important elements of neutron signal, including pulse shape, pulse amplitude spectrum, count rate, and add-on white noise intensity were all able to be accessed and changed separately over the PCI&PCIe bus. A Labview-based Human Machine Interface (HMI) was also built to show all of these aspects and allowed real-time modifications. When compared to other signal generators, our signal generator has a significantly longer non-repetitive waveform and higher performance capability. This made it a reliable way to diagnose flux measurement systems by comparing count rates set on the signal generator to record results. [ABSTRACT FROM AUTHOR]

The Tangerine project aims to develop new state-of-the-art high-precision silicon detectors. Part of the project has the goal of developing a monolithic active pixel sensor using a novel 65 nm CMOS imaging process, with a small collection electrode. This is the first application of this process in particle physics, and it is of great interest as it allows for an increased logic density and reduced power consumption and material budget compared to other processes. The process is envisioned to be used in for example the next ALICE inner tracker upgrade, and in experiments at the electron-ion collider. The initial goal of the three-year Tangerine project is to develop and test a sensor in a 65 nm CMOS imaging process that can be used in test beam telescopes at DESY, providing excellent spatial resolution and high time resolution, and thus demonstrating the capabilities of the process. The project covers all aspects of sensor R&D, from electronics and sensor design using simulations, to prototype test chip characterisation in labs and at test beams. The sensor design simulations are performed by using a powerful combination of detailed electric field simulations using technology computer-aided design and high-statistics Monte Carlo simulations using the Allpix 2 framework. A first prototype test chip in the process has been designed and produced, and successfully operated and tested both in labs and at test beams. [ABSTRACT FROM AUTHOR]

Abstract: Hypervelocity impacts of micrometeoroid and space debris particles can produce a highly transient plasma cloud that shows a spectrum of distinct electrical phenomena ranging from charge production to electrostatic field and electromagnetic wave generation. The coupling of these effects to electrical probes can be used as a means of in-situ debris detection to monitor the polluted orbits around the Earth. In the past, some detectors were built mainly for the detection of natural dust populations in space, such as a long heritage of charge collection detectors. In addition, several radio astronomy and ambient plasma instruments that were not specifically dedicated to particle detection revealed impact-induced anomalies during interplanetary missions. Most of them were explained by the interaction of electrically sensitive probes with free charges produced upon impact. For the application in low Earth orbits, one needs to take into account, that the man-made debris population differs from natural populations in many regards, as does the plasma environment between interplanetary space and in orbits close to Earth. The paper at hand gives a summary of detectors with flight heritage and devises a first concept for in situ space debris detectors in low Earth orbit by exploiting past experience with dust detectors in deep space. [Copyright &y& Elsevier]

Highlights: [•] Carbon coated copper foils were intentionally contaminated. [•] Specimen were excited by optical and near infrared radiation. [•] The specimen’s responses in the optical and mid wave infrared range were analyzed. [•] Infrared detection was found to provide improved detectability of particles. [Copyright &y& Elsevier]

Recently, Clifton and Halvorson have tried to salvage a particle phenomenology in the absence of particle ontology within algebraic relativistic quantum field theory. Their idea is that the detection of a particle is the measurement of a local observable which simulates the measurement of an almost local observable that annihilates the vacuum. In this note, we argue that the measurements local particle detections are supposed to simulate probe radically holistic aspects of relativistic quantum fields. We prove that in an axiomatic (Haag-Araki) quantum field theory on Minkowski spacetime, formulated in a Hilbert space $\mathcal{H}$, there is no positive observable C, with norm less than or equal to 1, satisfying the conditions: (1) the expectation value of C in the vacuum state Ω is zero, (2) there is at least one vector state Ψ in $\mathcal{H}$ in which the expectation value of C is different from zero, and (3) there exists at least one spacetime region $\mathcal{O}$ such that the non-selective measurement of C leaves the expectation values of all observables in the local algebra of that region unaltered regardless of the state the system is in. The result reveals a tension between intuitions regarding localization and intuitions regarding causality: to save 'particle phenomena' in the absence of particle ontology, one has to feign 'particle' detectors with 'good' properties as to locality but 'bad' behavior as to causality. [ABSTRACT FROM AUTHOR]

Image denoising and signal enhancement are two common steps to improve particle contrast for detection in low-signal-to-noise ratio (SNR) fluorescence live-cell images. However, denoising may oversmooth features of interest, particularly weak features, leading to false negative detection. Here, we propose a robust framework for particle detection in which image denoising in the grayscale image is not needed, so avoiding image oversmoothing. A key to our approach is the new development of a particle enhancement filter based on the recently proposed particle probability image to obtain significantly enhanced particle features and greatly suppressed background in low-SNR and low-contrast environments. The new detection method is formed by combining foreground and background markers with watershed transform operating in both particle probability and grayscale spaces; dynamical switchings between the two spaces can optimally make use the information in images for accurate determination of particle position, size, and intensity. We further develop the interacting multiple mode filter for particle motion modeling and data association by incorporating the extra information obtained from our particle detector to enhance the efficiency of multiple particle tracking. We find that our methods lead to significant improvements in particle detection and tracking efficiency in fluorescence live-cell applications. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents the results of the characterisation of a thin fully depleted pixel sensor manufactured in SOI technology on high-resistivity substrate with high momentum charged particles. The sensor is thinned to and a thin phosphor layer contact is implanted on the back-plane. Its response is compared to that of thick sensors of same design in terms of signal and noise, detection efficiency and single point resolution based on data collected with 300GeV pions at the CERN SPS. We observe that the charge collected and the signal-to-noise ratio scale according to the estimated thickness of the sensitive volume and the efficiency and single point resolution of the thinned chip are comparable to those measured for the thick sensors. [Copyright &y& Elsevier]

Abstract: A new algorithm of particle identification suitable for particle tracking technique in fluid mechanics is proposed and tested with synthetic images specifically developed with different particle parameters. The new approach is based on the solution of the optical flow equation via a sum-of-squared-difference method. Particles are detected through the identification of corner features, where image intensity gradients are not null in two orthogonal directions. It is thus possible to identify low intensity and overlapped particles. Furthermore, the feature selection criterion is optimal by construction because it is based on the optical flow solution and therefore a good feature is the one that can be tracked well. This leads to the second advantage of the method, which is the possibility to obtain the local velocity, given by the approximate solution of the optical flow equation, that can be used as a predictor for the subsequent particle pairing step. The proposed algorithm is tested using synthetically generated and experimental images and demonstrates its ability to detect a great number of particles with high reliability in different cases analysed. [Copyright &y& Elsevier]

Dynamically tracking hundreds of individual pits is essential to determine whether there exist 'hot spots' for the formation of clathrin-coated pits or if the pits formed randomly on the plasma membrane. We propose an automated approach to detect these particles based on an improved á trous wavelet transform decomposition with automatic threshold selection and post processing solution, and to track the dynamic process with a greedy algorithm. The results indicate that the detection method can successfully detect most particles in an image with accuracy of 98.61% and 97.65% for adaptor and clathrin images, respectively, and that the tracking algorithm can resolve merging and splitting issues encountered when analyzing dynamic, live-cell images of clathrin assemblies. [ABSTRACT FROM AUTHOR]