By offering mechanical compliance similar to biological tissue, elastic electronics show great potential in wearable and implanted electronics, interactive robots, and neural interfaces. Miniaturization of elastic electronics through advanced microfabrication is essential to increase device density for high-quality and comprehensive information processing. Cleanroom photolithography is conventionally used for micropatterning photoresists, whose patterns are then transferred to rigid metal or semiconductor materials through lift-off or etching processes. However, such delicate processes are exclusive and cannot be directly translated to fabricate elastic electronics, which are usually based on unconventional materials. Here, we developed a metallic photoresist, based on ligand-encapsulated eutectic gallium-indium liquid metal nanoparticles, and an associated microfabrication process that enables direct, single-step liquid metal microlithography across wafer-scale areas. By leveraging tunable covalent and noncovalent interactions at liquid metal nanoparticles interfaces, this method achieves 2µm resolution, bulk-level conductivity, and 3D topology matching of liquid metal patterns, while maintaining over 750% stretchability. We demonstrate the versatility of this approach by fabricating multi-scale elastic electronics, from high-resolution liquid metal grid transparent electrodes and ECoG neural electrodes to large-area flexible printed circuit boards.

Considering the rapid iteration of you-only-look-once (YOLO)-series algorithms, this paper aims to provide a data-driven performance spectrum and selection guide for the latest YOLO series algorithm (YOLOv8 to YOLOv13) in printed circuit board (PCB) automatic optical inspection (AOI) through systematic benchmarking. A comprehensive evaluation of the six state-of-the-art YOLO series algorithms is conducted on a standardized dataset containing six typical PCB defects: missing hole, mouse bite, open circuit, short circuit, spur, and spurious copper. An innovative dual-cycle comparative experiment (100 rounds and 500 rounds) is designed, and a systematic assessment is performed across multiple dimensions, including accuracy, efficiency, and inference speed. The experimental results have revealed significant variations in algorithm performance with training cycles: under short-term training (100 rounds), YOLOv13 achieves leading detection performance (mAP50 = 0.924, mAP50-95 = 0.484) with the fewest parameters (2.45 million); after full training (500 rounds), YOLOv10 achieves the highest overall accuracy (mAP50 = 0.946, mAP50-95 = 0.526); additionally, YOLOv11 shows the optimal speed-accuracy balance after long-term training, while YOLOv12 excels in short-term training; moreover, “open circuit” and “spur” are evaluated as the most challenging defect categories to detect. The findings given in this paper indicate the absence of a universally applicable “all-in-one” algorithm and propose a clear algorithm selection roadmap: YOLOv10 is recommended for offline analysis scenarios prioritizing extreme accuracy; YOLOv13 is the top choice for applications requiring rapid iteration with tight training time constraints; and YOLOv11 is the best option for high-throughput online inspection PCB production lines.

Abstract Deep learning models have been developed and applied to the field of printed circuit boards (PCBs) surface defect detection. However, there are still limitations that prevent the model from effectively balancing detection accuracy and model com-plexity. To address this issue, a lightweight defect inspection network (LDINet) is proposed to achieve accurate and efficient detection with reduced model parameters. Firstly, to improve the ability to express tiny defects on the PCB surface, an im-proved MobileNetV3, based on a lightweight and efficient channel attention network (ECANet), is adopted as the model backbone to achieve more efficient feature extraction with fewer parameters. Subsequently, to utilize both the deep semantic feature and the shallow fine-grained feature that the backbone component extracted and enhance the detection capability with notable scale changes, the novel cross-scale feature fusion module (ICCFM) based on combining reparameterization and ECANet attention mechanism is designed, which can effectively integrate context information and detail features, and integrate more abundant small defect features. Finally, comparative experiments on public and industrial datasets demonstrate that the proposed model is better suited for deployment in PCB production line inspection devices with limited computing resources.

This article explores the high-frequency characteristics of gallium nitride (GaN) power-switching devices and evaluates their application performance using a double-pulse test (DPT) circuit model. With the increasing adoption of GaN power-switching devices in high-performance and miniaturized electronic products, their low junction capacitance makes them highly suitable for high-frequency applications. However, parasitic inductance in the power loop can introduce resonance phenomena, impacting system stability and switching performance. To address this, this study integrates the parasitic parameters of printed circuit boards (PCBs) with the nonlinear junction capacitance characteristics of GaN devices. Finite element analysis (FEA) is employed to extract PCB parasitic inductance values and analyze their effects on GaN power-switching behavior. The findings indicate that precise extraction and analysis of parasitic inductance are critical for optimizing the performance of GaN switching devices. Additionally, this study investigates mitigation strategies to minimize parasitic inductance, ultimately enhancing GaN device design and reliability. The insights from this research provide valuable guidance for the development of GaN power devices in high-frequency applications.

In continuous culture, a population of microorganisms is propagated in a stable environment over many generations. This is particularly relevant for experimental evolution and metabolic studies. However, continuous culture protocols are difficult to implement, so they are not commonly used in microbiology laboratories. Here, we present the ModuloStat, a modular, open-source framework that facilitates continuous culture in mini-bioreactors. The ModuloStat system is grounded on digital fabrication tools easily accessible in FabLabs and programmable electronics. Maintaining a culture is divided into tasks assigned to dedicated printed circuit boards with a microcontroller connected to a Wi-Fi network. According to Internet of Things principles, each board operates a set of sensors and actuators autonomously and can receive and send information. The boards are stacked to implement complex behaviors and can be modified to accommodate new features. A thermoregulated box holds the components and can be placed on a laboratory bench or transported under a sterile hood for inoculation. Sterility is ensured by autoclaving, after assembly, all components that will come into contact with the culture medium. In-situ optical density monitoring combined with modularity and computer control enables many cultivation modes. Additionally, we present the construction of the Bacillus subtilis strain ZB designed for bioreactor culture that exhibits a zero-biofilm phenotype. To demonstrate the system's versatility, we performed several experimental cultures with this model organism, including chemostat, turbidostat, medium swap, and a cascade of bioreactors.

In mass spectrometry (MS), the radio frequency (RF)-only multipole ion guide commonly used in a medium pressure range (0.1-10 Pa) is susceptible to a collision-damping-induced loss of instrumentation sensitivity, resulting in operation within a limited m/z bandwidth. In this study, a novel direct current (DC)-free gradient-threaded discrete twisted dipole ion guide (GTDIG) is proposed for high-efficiency ion propulsion and an expanded m/z bandwidth in a medium vacuum (0.1-10 Pa). An overview of the GTDIG working principles is provided with systematic optimization of the critical parameters. The GTDIG has been applied by using a series of printed circuit boards, utilizing an innovative discrete helical electrode design. The performance of the GTDIG prototype is evaluated using multiphysical numerical modeling and tested using a custom-built nanoelectrospray ionization (ESI)-MS platform, including a direct comparison with a quadrupole ion guide. The results indicate that the RF-only GTDIG offers a significantly larger m/z window while effectively maintaining an axial field, demonstrating that this concept can be utilized in the development of instruments with an enhanced performance.

Defects in printed circuit boards (PCBs), if not detected promptly, may persist over time until they cause the failure of critical components. Traditional monitoring methods, which are limited to simulations or superficial measurements, obstruct predictive maintenance and real-time fault detection. To address these issues and enhance real-time diagnostics of thermal anomalies in PCBs, this work proposes an integrated system that combines infrared thermography (IRT), artificial intelligence (AI) algorithms, and Taguchi–ANOVA statistical techniques. IR thermography was employed to identify thermal stresses in the devices during normal operation. The IR acquisitions were used to build a dataset for specialized AI model’s training, which combines thermal anomalies segmentation using U-Net with a Multilayer Perceptron (MLP) classifier for heat distribution patterns. The Taguchi method determines the optimal configuration of the selected parameters, while Analysis of Variance (ANOVA) evaluates the effect of each factor on the F1-score response. These techniques statistically validated the AI performance, confirming the optimal set of selected hyperparameters and quantifying their contribution to F1-score. The novelty of the study lies in the integration of real-time infrared thermography with an interpretable AI pipeline and a Taguchi–ANOVA statistical framework, which enables both optimisation and rigorous validation of AI performance under real-time operating conditions.

Extraction of Metals from Waste Printed Circuit Boards of Computers Through Leaching Using Novel Deep Eutectic Solvents of Organic Acids

Abstract Reducing losses in inductor core materials allows further miniaturization and increase of efficiency in power converters. Nanocomposites containing superparamagnetic 11 3 nm ‐ particles in a polyvinyl alcohol polymer matrix were developed as printable and castable inductor core materials for MHz range frequencies. The aqueous synthesis resulted in nanocomposites of well‐dispersed particles with volume fractions ranging from 10% to 45%. The nanocomposite is eddy current free, has high volume susceptibility up to 17, and a constant AC response in the Hz–kHz range. Hysteresis measurements at 100–900 kHz show that power losses scale as ‐field squared and with frequency to the power of 1–1.3, indicating that the only loss mechanism is high‐frequency hysteresis. For an induced ‐field amplitude of 30 mT, commonly used in inductor core materials for power electronics, the losses are on the order of – kW . These losses can be reduced by using more monodisperse particles. The presented nanocomposite is easily integrated into micro‐fabrication methods, demonstrated by depositing nanocomposite cores on printed circuit board inductors. The inductors with nanocomposite core, measured up to 100 MHz, display an increase in inductance compared to air‐core inductors. This showcases superparamagnetic nanocomposites as relevant candidates for high‐frequency applications such as portable electronics.

Machine learning-based system for online quantitative monitoring of heavy metals across different aqueous matrices using spectroscopy of plasmas in liquids.

This study presents the development and validation of an integrated access control and attendance registration system based on 125 kHz RFID technology, implementing the Wiegand communication protocol for data transmission between readers and the control unit. The system architecture comprises a main control panel equipped with a dual-core microcontroller with differentiated processing capabilities (480 MHz and 240 MHz), designed to facilitate integration with IoT ecosystems in the context of Industry 4.0. The experimental methodology included the implementation and validation of the prototype under controlled laboratory conditions, obtaining results that demonstrate the technical feasibility of the system and its potential applicability in harsh industrial environments. Performance testing demonstrated that the system achieves an average response time of 0.5 seconds for authentication and event logging operations, processed through a PostgreSQL database optimized for real-time applications. The development included the implementation of an administrative web interface using the Apache NetBeans integrated development environment, providing system management and monitoring capabilities. Additionally, the complete schematic design of the control panel was developed, culminating in the fabrication and implementation of a custom PCB (Printed Circuit Board) that integrates all electronic components of the system. As a complement to the technological development, a comparative analysis of commercially available electromagnetic locks was conducted, establishing technical and economic selection criteria to optimize system implementation in corporate office access control applications. Keywords: RFID technology, Access, Control, Wiegand Protocol, Portenta H7,IoT.

Low-profile broadband magnetoelectric dipole antenna based on printed circuit Board

A compact single asymmetric coplanar waveguide feed (ACPW‐fed) dual circularly polarized microstrip antenna that operates at 1.8, 3.9, and 5.2 GHz in the entire operating frequency band 600 MHz–6 GHz for the radar detection of the improvised explosive devices (IEDs) carried by a person is introduced. The proposed novel quasi‐omnidirectional antenna consists of single sided rectangular ring microstrip patch antenna. L‐shaped slots are etched at the two opposite corners of the rectangular ring, introducing new resonance and circular polarization waves at the mid and upper bands, respectively. The achieved dual half‐rectangular ring patch antenna (DHRR‐patch) is loaded with strips of various shapes delicately placed at the center of the radiator, providing new resonance at the upper band and the improvement of the CP features. The matching technique designed based on CPW 50 Ω microstrip transmission line combined with the dual broad band matching techniques through quarter‐wave transformer in conjunction with open stubs and distributed lumped element method constitutes the novelty of the study. Based on quasi‐TEM mn (q‐TEM mn ) mode, ACPW‐fed and CP‐slots are employed to generate CP radiations at the q‐TEM 11 and q‐TEM 21 modes, respectively, while the ground plane width is optimized to enhance axial ratio bandwidth (AR‐BW). Input impedance and radiation pattern calculations of the conventional structure using transmission line and cavity model‐based q‐TEM 01 mode are conducted, respectively. Numerical experiments of the studied monolayer antenna are carried out using Advanced Design System (ADS) Version 2009 environment software employing internal one‐port option to excite the antenna. The prototype of the proposed antenna with a compact dimension (0.27 λ g × 0.38 λ g × 0.02 λ g at 1.8 GHz where λ g is the guided wavelength of the q‐TEM 01 mode) is fabricated on high loss laminate FR4 substrate of volume 43 × 38 × 1.6 (mm 3 ) and relative dielectric constant of 4.4 with simple laboratory‐based traditional printed circuit board (PCB) etching process. Measurement results show a fractional impedance bandwidth (FIBW) of 11.1%, 5.9%, and 7.1%, axial ratio (AR) of 4.6, 2.2, and 0.5 dB, and peak gain of 3.7, 4.7, and 6.1 dBic at 1.8, 4.0, and 5.2 GHz, respectively, demonstrating its suitability for IED detection applications. To verify the efficiency of the proposed model, measured results are compared with the simulated results and good agreement has been established.

Environmental sustainability assessment processes for flat panel displays dismantling.

MICROMAP: A LOW-COST MULTI-CHANNEL ELECTROPHYSIOLOGY ACQUISITION SYSTEM.

MSMD-YOLO: Enhanced Printed Circuit Board Defect Detection with a Multi-Scale Merging and Attention Network

A printed circuit board embedded with copper-based microchannels for radio frequency power electronics

Ultrasonic-induced water-oil interface of microdroplets: A selective and rapid green approach for gold recovery from e-waste.

Effect of pyrolysis and comminution on copper extraction and techno-economic assessment for resource recovery from waste printed circuit board.

Non-destructive Printed Circuit Board layout verification using a deterministic diffusion-guided framework