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While the printed circuit board (PCB) has been widely considered as the building block of integrated electronics, the world is switching to pursue new ways of merging integrated electronic circuits with textiles to create flexible and wearable devices. Herein, as an alternative for PCB, we described a non-printed integrated-circuit textile (NIT) for biomedical and theranostic application via a weaving method. All the devices are built as fibers or interlaced nodes and woven into a deformable textile integrated circuit. Built on an electrochemical gating principle, the fiber-woven-type transistors exhibit superior bending or stretching robustness, and were woven as a textile logical computing module to distinguish different emergencies. A fiber-type sweat sensor was woven with strain and light sensors fibers for simultaneously monitoring body health and the environment. With a photo-rechargeable energy textile based on a detailed power consumption analysis, the woven circuit textile is completely self-powered and capable of both wireless biomedical monitoring and early warning. The NIT could be used as a 24/7 private AI “nurse” for routine healthcare, diabetes monitoring, or emergencies such as hypoglycemia, metabolic alkalosis, and even COVID-19 patient care, a potential future on-body AI hardware and possibly a forerunner to fabric-like computers. The typical approach to electronics is to integrate sensors, power units, and controlling components on a printed circuit board (PCB). Here, the authors demonstrate a self-powered and fully integrated combination of sensors and controlling components that is woven, rather than integrated onto a PCB, allowing for wearable health monitoring.”

The rising demand for radiation detection materials in many applications has led to extensive research on scintillators 1 – 3 . The ability of a scintillator to absorb high-energy (kiloelectronvolt-scale) X-ray photons and convert the absorbed energy into low-energy visible photons is critical for applications in radiation exposure monitoring, security inspection, X-ray astronomy and medical radiography 4 , 5 . However, conventional scintillators are generally synthesized by crystallization at a high temperature and their radioluminescence is difficult to tune across the visible spectrum. Here we describe experimental investigations of a series of all-inorganic perovskite nanocrystals comprising caesium and lead atoms and their response to X-ray irradiation. These nanocrystal scintillators exhibit strong X-ray absorption and intense radioluminescence at visible wavelengths. Unlike bulk inorganic scintillators, these perovskite nanomaterials are solution-processable at a relatively low temperature and can generate X-ray-induced emissions that are easily tunable across the visible spectrum by tailoring the anionic component of colloidal precursors during their synthesis. These features allow the fabrication of flexible and highly sensitive X-ray detectors with a detection limit of 13 nanograys per second, which is about 400 times lower than typical medical imaging doses. We show that these colour-tunable perovskite nanocrystal scintillators can provide a convenient visualization tool for X-ray radiography, as the associated image can be directly recorded by standard digital cameras. We also demonstrate their direct integration with commercial flat-panel imagers and their utility in examining electronic circuit boards under low-dose X-ray illumination. All-inorganic perovskite nanocrystals containing caesium and lead provide low-cost, flexible and solution-processable scintillators that are highly sensitive to X-ray irradiation and emit radioluminescence that is colour-tunable across the visible spectrum.

In this study, a deep learning algorithm based on the you-only-look-once (YOLO) approach is proposed for the quality inspection of printed circuit boards (PCBs). The high accuracy and efficiency of deep learning algorithms has resulted in their increased adoption in every field. Similarly, accurate detection of defects in PCBs by using deep learning algorithms, such as convolutional neural networks (CNNs), has garnered considerable attention. In the proposed method, highly skilled quality inspection engineers first use an interface to record and label defective PCBs. The data are then used to train a YOLO/CNN model to detect defects in PCBs. In this study, 11,000 images and a network of 24 convolutional layers and 2 fully connected layers were used. The proposed model achieved a defect detection accuracy of 98.79% in PCBs with a batch size of 32.

The high-frequency and high-speed printed circuit board (PCB) with lower transmission loss, higher heat resistance, and better processability play increasing significant roles in mobile communication technology. However, because the materials and micro drilling process of high-frequency and high-speed PCB are very different from the traditional printed board, there are still many of key techniques to be explored in the future study. In this paper, the characteristics of high-frequency and high-speed PCB were presented. Researches concerning the design and wear ability of micro drill, the analysis of micro drilling force and temperature, and the quality of micro holes were reviewed. Finally, several key techniques and challenges regarding materials and micro drilling were suggested.

The increasing use of electrical and electronic equipment leads to a huge generation of electronic waste (e-waste). It is the fastest growing waste stream in the world. Almost all electrical and electronic equipment contain printed circuit boards as an essential part. Improper handling of these electronic wastes could bring serious risk to human health and the environment. On the other hand, proper handling of this waste requires a sound management strategy for awareness, collection, recycling, and reuse. Nowadays, the effective recycling of this type of waste has been considered as a main challenge for any society. Printed circuit boards (PCBs), which are the base of many electronic industries, are rich in valuable heavy metals and toxic halogenated organic substances. In this review, the composition of different PCBs and their harmful effects are discussed. Various techniques in common use for recycling the most important metals from the metallic fractions of e-waste are illustrated. The recovery of metals from e-waste material after physical separation through pyrometallurgical, hydrometallurgical, or biohydrometallurgical routes is also discussed, along with alternative uses of non-metallic fraction. The data are explained and compared with the current e-waste management efforts done in Egypt. Future perspectives and challenges facing Egypt for proper e-waste recycling are also discussed.

Printed circuit board (PCB) manufacturing processes are becoming increasingly complex, where even minor defects can impair product performance and yield rates. Precisely identifying PCB defects is critical but remains challenging. Traditional PCB defect detection methods, such as visual inspection and automated technologies, have limitations. While defects can be readily identified based on symmetry, the operational aspect proves to be quite challenging. Deep learning has shown promise in defect detection; however, current deep learning models for PCB defect detection still face issues like large model size, slow detection speed, and suboptimal accuracy. This paper proposes a lightweight YOLOv8 (You Only Look Once version 8)-based model called LW-YOLO (Lightweight You Only Look Once) to address these limitations. Specifically, LW-YOLO incorporates a bidirectional feature pyramid network for multiscale feature fusion, a Partial Convolution module to reduce redundant calculations, and a Minimum Point Distance Intersection over Union loss function to simplify optimization and improve accuracy. Based on the experimental data, LW-YOLO achieved an mAP0.5 of 96.4%, which is 2.2 percentage points higher than YOLOv8; the precision reached 97.1%, surpassing YOLOv8 by 1.7 percentage points; and at the same time, LW-YOLO achieved an FPS of 141.5. The proposed strategies effectively enhance efficiency and accuracy for deep-learning-based PCB defect detection.

Confining photons in a finite volume is highly desirable in modern photonic devices, such as waveguides, lasers and cavities. Decades ago, this motivated the study and application of photonic crystals, which have a photonic bandgap that forbids light propagation in all directions 1 – 3 . Recently, inspired by the discoveries of topological insulators 4 , 5 , the confinement of photons with topological protection has been demonstrated in two-dimensional (2D) photonic structures known as photonic topological insulators 6 – 8 , with promising applications in topological lasers 9 , 10 and robust optical delay lines 11 . However, a fully three-dimensional (3D) topological photonic bandgap has not been achieved. Here we experimentally demonstrate a 3D photonic topological insulator with an extremely wide (more than 25 per cent bandwidth) 3D topological bandgap. The composite material (metallic patterns on printed circuit boards) consists of split-ring resonators (classical electromagnetic artificial atoms) with strong magneto-electric coupling and behaves like a ‘weak’ topological insulator (that is, with an even number of surface Dirac cones), or a stack of 2D quantum spin Hall insulators. Using direct field measurements, we map out both the gapped bulk band structure and the Dirac-like dispersion of the photonic surface states, and demonstrate robust photonic propagation along a non-planar surface. Our work extends the family of 3D topological insulators from fermions to bosons and paves the way for applications in topological photonic cavities, circuits and lasers in 3D geometries. A three-dimensional photonic topological insulator is presented, made of split-ring resonators with strong magneto-electric coupling, which has an extremely wide topological bandgap, forbidding light propagation.

Harvesting mechanical energy from our surroundings to acquire a steady and high power output has attracted intensive interest due to the fast development of portable electronics. In this work, the disk-structured triboelectric nanogenerator (TENG) was prepared based on the mature printed circuit board (PCB) technology and the composite structure for effectively improving the utilization in space. A narrow grating of 1° was designed to produce high output. Operated at a rotation rate of 1,000 rpm, the TENG produces a high output power density of 267 mW/cm 2 (total power output of 25.7 W) at a matched load of 0.93 MΩ. After introducing a transformer, the output power can be managed so that it can be directly used to charge a battery for a smart phone. With the PCB production technology, fabrication of high performance TENG at low cost and large-scale becomes feasible.

The introduction of 3D printing technology has revolutionised the manufacturing and electronic product design in the past few years, where it is used to even produce printed circuit boards. Printed electronics is one of the fastest-growing additive manufacturing technologies and is becoming invaluable to various industries. The evolution of several contact and non-contact types of fabrication techniques have been reported in the recent past. Leveraging these technologies, various types of printed electronic components have been realized. One method is inkjet printing technology, which has been widely accepted for printed electronics manufacturing. As 3D printing uses only those materials which are essential to create the product, it eliminates waste production, with a smaller equipment cost and minimizes the number of process steps, resulting in lower manufacturing costs with reduced turnaround time. Various kinds of conductive and non-conductive materials have emerged in the recent past in conjunction with many manufacturing techniques for printed electronics. Herein, we review the most commonly used substrates, electronic printing materials, and the widespread printing techniques employed at the industrial level, giving an overall vision for a better understanding and evaluation of their different features. The technical challenges of several contact and non-contact techniques with corresponding solutions are also presented. Finally, status on advances in the production of various kinds of materials employed in 3D printed electronics and the methods for producing them, shortcomings, technical challenges, applications, benefits, and the future opportunities pertaining to printed electronics are discussed in detail.

The development of wearable and on-skin electronics requires high-density stretchable electronic systems that can conform to soft tissue, operate continuously and provide long-term biocompatibility. Most stretchable electronic systems have low-density integration and are wired with external printed circuit boards, which limits functionality, deteriorates user experience and impedes long-term usability. Here we report an intrinsically permeable, three-dimensional integrated electronic skin. The system combines high-density inorganic electronic components with organic stretchable fibrous substrates using three-dimensional patterned, multilayered liquid metal circuits and stretchable hybrid liquid metal solder. The electronic skin exhibits high softness, durability, fabric-like permeability to air and moisture and sufficient biocompatibility for on-skin attachment for a week. We use the platform to create wireless, battery-powered and battery-free skin-attached bioelectronic systems that offer complex system-level functions, including the stable sensing of biosignals, signal processing and analysis, electrostimulation and wireless communication. An electronic skin that connects rigid inorganic electronic components with a multilayered stretchy liquid metal fibre mat using a hybrid liquid metal solder can offer high integration density while remaining soft, permeable and biocompatible.