Hardware, Volume 4, Issue 1 (March 2026) – 6 articles

Wide bandgap (WBG) semiconductors such as gallium nitride (GaN) and silicon carbide (SiC) have revolutionized modern power electronics by enabling devices that operate at higher voltages, temperatures, and switching frequencies than their silicon counterparts. This paper reviews the material properties, device architectures, fabrication techniques, and thermal management strategies that underpin the performance of GaN and SiC technologies. We highlight key trade-offs between GaN and SiC in terms of voltage blocking capability, switching efficiency, and thermal robustness and discussed their application in electric vehicles, renewable energy systems, and power converters. Market adoption trends and manufacturing challenges are also analyzed, with attention to cost-performance dynamics and packaging innovations. Finally, we address the critical role of thermal boundary resistance and emerging reliability solutions, providing a perspective on the future trajectory of WBG device research and commercialization. Full article

Long-read sequencing technologies, particularly those developed by Oxford Nanopore Technologies (ONT), have transformed genome sequencing by enabling high-resolution analysis of complex microbial communities. Among ONT devices, the MinION remains affordable and scalable for low-resource settings. However, its limited onboard computing power constrains high-accuracy basecalling and limits its ability to address inherent sequencing errors effectively. To overcome these constraints, we assembled a streamlined in-house workflow that integrates at least five MinION devices with a GPU-powered workstation running Ubuntu 20 and MinKNOW. Rather than a new sequencing platform, this “home-made GridION” represents a practical integration of existing ONT devices with dedicated computing resources. At its core is a live basecalling pipeline capable of handling both FAST5 and POD5 file formats. The system supports high-throughput basecalling using Guppy on FAST5 files as well as Dorado on POD5 files, ensuring compatibility with both legacy and current ONT data standards. File monitoring is automated via inotifywait, enabling immediate detection of new files, real-time basecalling, and organized output of FASTQ batches. Beyond basecalling, we implemented an automated downstream pipeline for metagenomic analysis, enabling taxonomic profiling and detection of antimicrobial resistance genes (ARG). Tested on 10 hospital wastewater samples, the workflow generated at least 500,000 reads per sample within six hours, which were analysed for antimicrobial resistance gene abundance. This demonstrates its potential as an open, scalable hardware/software platform that extends the utility of MinION sequencing for microbial genomics in resource-limited environments. The setup can channel as many MinIONs as available USB ports, with a ratio of 1 MK1D for 1 TB of storage capacity on the associated computer. Full article

Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) systems are widely used for the real-time analysis of mass changes and viscoelastic properties in biological samples, enabling applications such as biomolecular interaction studies, biosensing, and fluid characterization. However, their accessibility has been limited by high acquisition costs. To address this limitation, a low-cost, open-source QCM-D system was developed. Unlike other affordable, open-hardware alternatives, this system is specifically optimized for potential biomedical applications by integrating active thermal control to preserve the physical properties of the samples and dissipation monitoring to characterize their viscoelastic behavior. A 10 MHz quartz crystal with a sensor module and a control and acquisition unit were integrated. The full system was built at a total cost below USD 500. Performance validation showed a temperature stability of ±0.13 °C, a frequency stability of ±2 Hz in air, and a limit of detection (LOD) of 0.46% polyethylene glycol (PEG), thereby enabling stable, reproducible measurements and the sensitive detection of small mass and interfacial changes in low-concentration samples. These results demonstrate that key QCM-D sensing capabilities can be achieved at a fraction of the cost, providing an accessible and reliable platform for potential biomedical research. Full article

Dynamic demand response (DDR) is the process of shifting power consumption towards periods of lower demand based on real-time energy pricing data. It is a flexibility measure utilised in the decarbonisation of the UK’s power system to reduce peak demand. Dynamic time-of-use (dTOU) tariffs, such as Agile Octopus, incentivise DDR by providing half-hourly electricity prices for each day. Through this incentive, households are offered the opportunity to reduce their energy costs by applying DDR to energy-intensive, deferrable loads. This paper presents an open-source, Internet of Things (IoT)-based system designed to automate DDR and streamline its implementation. The system identifies the period of lowest electricity prices and activates a relay during this period each day. For validation, the system was tested over a one-month experiment, which showed that, in a favourable scenario, it could reduce an appliance’s electricity costs by up to 44%. These results highlighted the system’s potential to deliver substantial energy cost savings, while also encouraging households to participate in flexibility measures that alleviate pressure on the National Grid. Full article

This work presents the development of a differential-drive wheeled mobile robot educational prototype, manufactured using 3D additive techniques. The robot is powered by an embedded ARM-based computing system and uses open-source software. To validate the prototype, a trajectory-tracking task was successfully implemented. The aim of this contribution is to provide an easily replicable prototype for teaching automatic control and related engineering topics in academic settings. Full article

High-flux neutron beams and high-efficiency detectors enable rapid neutron diffraction measurements at the Engineering Materials Diffractometer (VULCAN) at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory (ORNL). To optimize beam time utilization, efficient sample exchange, alignment, and automated measurements are essential. Recent advances in artificial intelligence (AI) have expanded the capabilities of robotic systems. Here, we report the development of a Robotic Interactive Control and Handling (RICH) system for sample handling at VULCAN, designed to support high-throughput experiments and reduce overhead time. The RICH system employs a six-axis desktop robot integrated with AI-based computer vision models capable of recognizing and localizing samples in real time from instrument and depth-resolving cameras. Vision algorithms combine these detections to align samples with designated measurement positions or place them within complex sample environments such as furnaces. This integration of machine learning-assisted vision with robotic handling demonstrates the feasibility of autonomous sample detection and preparation, offering a pathway toward fully unmanned neutron scattering experiments. Full article