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Lead oxide (PbO) bulk and nanoparticles of two different sizes (A = 78 nm and B = 54 nm) are incorporated separately into the polystyrene matrix at various concentrations (0, 10, 15, 25, and 35 %) using roll mill mixing and compressing molding techniques. The X-ray narrow-spectrum series (N-series / ISO 4037-1) is then used to investigate the radiation attenuation capability of the novel polymer composite PS/PbO, as well as the effect of varying PbO particle sizes on shielding performance. The filler dispersion and chemical elemental analysis of the synthesized composite are investigated using scanning electron microscopy and energy-dispersive X-ray spectroscopy. To determine the mass attenuation coefficients ?m, samples with various thicknesses of the synthesized composite are examined using a range of X-ray energies, and the experimental data are compared to theoretical values from NIST databases (XCOM and FFAST). The results indicate that either increasing the filler weight percentage or, decreasing the filler particle size, enhanced the attenuation parameters throughout all energies. The composite containing the smallest nanosize of PbO exhibited the maximum radiation shielding efficacy among all combinations and therefore, might be used to develop low-cost and lightweight X-ray shields.

Single-cell RNA sequencing is now a standard method used to reveal the molecular details of cellular heterogeneity, but current approaches have limitations on speed, scale, and ease of use that stem from the complex microfluidic devices or fluid handling steps required for sample processing. We, therefore, developed a method that does not require specialized microfluidic devices, expertise, or hardware. Our approach is based on particle-templated emulsification, which allows single-cell encapsulation and barcoding of cDNA in uniform droplet emulsions with only a vortexer. PIP-seq accommodates a wide range of emulsification formats, including microwell plates and large-volume conical tubes, enabling thousands of samples or millions of cells to be processed in minutes. We demonstrate that PIP-seq produces high-purity transcriptomes in mouse-human mixing studies, is compatible with multi-omics measurements, and can accurately characterize cell types in human breast tissue when compared to a commercial microfluidic platform. Single-cell transcriptional profiling of mixed phenotype acute leukemia using PIP-seq revealed the emergence of heterogeneity within chemotherapy-resistant cell subsets that were hidden by standard immunophenotyping. PIP-seq is a simple, flexible, and scalable next-generation workflow that extends single-cell sequencing to new applications, including screening, diagnostics, and disease monitoring.

Due to their remarkable electrical and light absorption characteristics, hybrid organic-inorganic perovskites have recently gained popularity in several applications such as optoelectronics, lasers, and light-emitting diodes. Through this, there has recently been an increase in the use of halide perovskites (HPs) in resistive switching (RS) devices. However, lead-based (Pb-based) perovskites are notorious for being unstable and harmful to the environment. As a result, lead-free (Pb-free) perovskite alternatives are being investigated in achieving the long-term and sustainable use of RS devices. This work describes the characteristics of Pb-based and Pb-free perovskite RS devices. It also presents the recent advancements of HP RS devices, including the selection strategies of perovskite structures. In terms of resistive qualities, the directions of both HPs appear to be identical. Following that, the possible impact of switching from Pb-based to Pb-free HPs is examined to determine the requirement in RS devices. Finally, this work discusses the opportunities and challenges of HP RS devices in creating a stable, efficient, and sustainable memory storage technology.

Dielectric elastomer (DE) technology are used in several applications for example generator, sensor and actuator. One of the major factors that limits the DE performance is premature electrical breakdown. Compositing is the example that have been reported to increase the breakdown strength. In this study polydimethylsiloxane (PDMS) film will be incorporated with two different fillers which are titanium dioxide (TiO2) and zinc oxide (ZnO). Both metal oxides will be calcined up to 300°C before they are added to the PDMS elastomer as fillers. The results show that the calcined TiO2 and ZnO that incorporated in PDMS films show significant increase of breakdown strengths. Meanwhile, the calcined TiO2 PDMS film give higher breakdown strength as comparison to the calcined ZnO counterpart.

The conservation of artworks is playing an increasingly important role in society today. Non-destructive investigation can provide the potential to identify deterioration as early as possible. In this research, transmission mid-wave infrared (MWIR) vision and air-coupled ultrasound (ACU) were used to investigate two paintings on canvas made from different textile support materials. An X-ray technique was used in the work for validation. It was found that the transmission mode can probe deeper and the differences in absorption due to the different textile support materials can be distinguished. This paper summarises advantages of the transmission inspection mode and compares and analyses images from the two techniques from a physical point of view.

The non-uniformity of non-planar object inspection data makes their analysis challenging. This paper reports a study of the use of recurrent neural network and artificial feed-forward neural network in pulsed thermography during the automated inspection of non-planar carbon fiber reinforced plastic samples. The time series, including the raw temperature-time series and sequenced signals obtained from the first derivative after thermographic signal reconstruction was used to train and test the models respectively. Quantitative comparisons of testing results showed that the long short-term memory recurrent neural network model was more accurate in handling time dependent information compared to the artificial feed-forward neural network model.

Cognitive sensor systems (CSS) determine the future of inspection and monitoring systems for the nondestructive evaluation (NDE) of material states and their properties and key enabler of NDE 4.0 activities. CSS generate a complete NDE 4.0 data and information ecosystem, i. e. they are part of the materials data space and they are integrated in the concepts of Industry 4.0 (I4.0). Thus, they are elements of the Industrial Internet of Things (IIoT) and of the required interfaces. Applied Artificial Intelligence (AI) is a key element for the development of cognitive NDE 4.0 sensor systems. On the one side, AI can be embedded in the sensor’s microelectronics (e. g. neuromorphic hardware architectures) and on the other side, applied AI is essential for software modules in order to produce end-user-information by fusing multi-mode sensor data and measurements. Besides of applied AI, trusted AI also plays an important role in CSS, as it is able to provide reliable and trustworthy data evaluation decisions for the end user. For this recently rapidly growing demand of performant and reliable CSS, specific requirements have to be fulfilled for validation and qualification of their correct function. The concept for quality assurance of NDE 4.0 sensor and inspection systems has to cover all of the functional sub-systems, i. e. data acquisition, data processing, data evaluation and data transfer, etc. Approaches to these objectives are presented in this paper after giving an overview on the most important elements of CSS for NDE 4.0 applications. Reliable and safe microelectronics is a further issue in the qualification process for CSS.

Pulsed thermography is one of the most popular thermography inspection methods. During an experiment of pulsed thermography, a specimen is quickly heated, and infrared images are captured to provide information about the specimen’s surface and subsurface conditions. Adequate transformations are usually performed to enhance the contrast of the thermal images and to highlight the abnormal regions before these thermal images are visually inspected. Given that deep neural networks have been a success in computer vision in the past few years, a data contrast enhancement approach with stacked denoising autoencoder (DAE) is proposed in this paper to enhance the abnormal regions in the thermal frames gathered by pulsed thermography. Compared to the direct principal component thermography, the proposed method can enhance the abnormalities evidently without weakening important details.

In ultrasonic non-destructive testing, array and matrix transducers are being employed for applications that require in-field steerability or which benefit from a higher number of insonification angles. Having many transmit channels, on the other hand, increases the measurement time and renders the use of array transducers unfeasible for many applications. In the literature, methods for reducing the number of required channels compared to the full matrix capture scheme have been proposed. Conventionally, these are based on choosing the aperture that is as wide as possible. In this publication, we investigate a scenario from the field of pipe inspection, where cracks have to be detected in specific areas near the weld. Consequently, the width of the aperture has to be chosen according to the region of interest at hand. On the basis of ray-tracing simulations which incorporate a model of the transducer directivity and beam spread at the interface, we derive application specific measures of the energy distribution over the array configuration for given regions of interest. These are used to determine feasible subsampling schemes. For the given scenario, the validity/quality of the derived subsampling schemes are compared on the basis of reconstructions using the conventional total focusing method as well as sparsity driven-reconstructions using the Fast Iterative Shrinkage-Thresholding Algorithm. The results can be used to effectively improve the measurement time for the given application without notable loss in defect detectability.

In this paper, terahertz time-domain spectroscopy (THz-TDS) is used for the first time to detect fabricated defects in a glass fiber-skinned lightweight honeycomb composite panel. A novel amplitude polynomial regression (APR) algorithm is proposed as a preprocessing method. This method segments the amplitude–frequency curves to simulate the heating and the cooling monotonic behavior as in infrared thermography. Then, the method of empirical orthogonal function (EOF) imaging is applied on the APR preprocessed data as a postprocessing algorithm. Signal-to-noise ratio analysis is performed to verify the image improvement of the proposed APR-EOF modality from a quantitative point of view. Finally, the experimental results and the physical analysis show that THz is more suitable with respect to the detection of defects in glass fiber lightweight honeycomb composites.

Advanced materials such as continuous carbon fiber-reinforced thermoplastic (CFRP) laminates are commonly used in many industries, mainly because of their strength, stiffness to weight ratio, toughness, weldability, and repairability. Structural components working in harsh environments such as satellites are permanently exposed to some sort of damage during their lifetimes. To detect and characterize these damages, non-destructive testing and evaluation techniques are essential tools, especially for composite materials. In this study, artificial intelligence was applied in combination with infrared thermography to detected and segment impact damage on curved laminates that were previously submitted to a severe thermal stress cycles and subsequent ballistic impacts. Segmentation was performed on both mid-wave and long-wave infrared sequences obtained simultaneously during pulsed thermography experiments by means of a deep neural network. A deep neural network was trained for each wavelength. Both networks generated satisfactory results. The model trained with mid-wave images achieved an F1-score of 92.74% and the model trained with long-wave images achieved an F1-score of 87.39%.

Industry 4.0 (I4.0) describes the current revolution of the industrial world with a strong impact on the complete production sector. Data about production processes and the corresponding material and product status are the key elements. All over the world, the protagonists of I4.0 are facing the challenges to define appropriate concepts for I4.0 infrastructure, data exchange, communication interfaces and efficient procedures for the interaction of I4.0 elements. The role of future Nondestructive Evaluation (NDE4.0) and corresponding workflows (i.e. data generation and evaluation) will change accordingly. Thus, NDE4.0 systems will be elements of the Industrial Internet of Things (IIoT) that communicate with the production machines and devices. They become an integral part of the digital production world and the industrial data space. This paper is a summarized overview of our current developments as well as of general key technologies and future challenges to enable the paradigm change from classical NDT toward NDE4.0, starting with approaches on signal processing, artificial intelligence-based information generation and decision making, generic data formats and communication protocols. For illustration purposes, prototypical implementations of our work are presented. This includes a pilot development of a modern human-machine-interaction by the use of assistance technologies for manual inspection.

Recent years have seen a notable advance in the quality of produced industrial ultrasonic data. This is due to two main factors. On the one hand, advances on hardware level permitted to design and trigger ultrasound sensing arrays and matrices that led to new acquisition strategies such as phased array method and full matrix capture technique. On the other hand, the development of algorithms and software components to reconstruct and process the measured data allowed a major improvement of the signal and image quality. Within this aspect, modern signal processing algorithms improved the defect resolution and thus their detection in ultrasound data. Mostly, methods based on time–frequency analysis are used. The measure of the improvement resulting from the signal processing methodology can be confirmed, for instance, by evaluating A-scans containing defects near the front and the back wall of inspected specimens. In this work, we describe a novel algorithm for processing one-, two- or three-dimensional ultrasonic data, in order to increase their defect resolution. The algorithm is demonstrated using simulation phantom as well as on a real specimen both including defects at different depths. The proposed enhancement method is based on the Stockwell transform and normalized Hilbert envelope. Proposed method can effectively improve the quality of the ultrasound data.

Active thermography is considered in this paper as a non-destructive testing technology to ensure the quality of solid oxide fuel cells. The acquired infrared images are automated processed by artificial intelligence methods in order to detect defects within the glass solder layer of solid oxide fuel cells. For this purpose, three supervised machine learning methods are investigated: (1) Support Vector Machine, (2) Adaptive Boosting, (3) U-Net. Among those methods, the U-Net method outperforms the other considered methods with higher accuracy and F-measure.