Development of ZFO family nanophotocatalysts: Clean hydrogen production by a new high performance photoreactor.

In this study, ZnFeO (ZFO), PdZnFeO (PZFO), BaCaMgSrZnFeO (BCMSZFO), CaCoCuZnFeO (CCCZFO), MgMnZnFeO (MMZFO), and CoCuMgMnNiZnFeO (CCMMNZFO) nanospinels were synthesized via a hydrothermal method assisted by succinic acid. This material was evaluated for hydrogen production through photocatalytic water splitting under separate irradiation wavelengths of visible LED light. The chemical, physical, and photophysical properties of the ZFO family nanophotocatalysts were characterized using FT-IR, XRD, FESEM-EDX-MAP, UV-Vis DRS, and VSM.

A Closed-Loop Ear-Worn Wearable EEG System with Real-Time Passive Electrode Skin Impedance Measurement for Early Autism Detection.

Autism spectrum disorder (ASD) is a chronic neurological disorder with the severity directly linked to the diagnosis age. The severity can be reduced if diagnosis and intervention are early (age < 2 years). This work presents a novel ear-worn wearable EEG system designed to aid in the early detection of ASD.

Motion artifact variability in biomagnetic wearable devices.

Motion artifacts can be a significant noise source in biomagnetic measurements when magnetic sensors are not separated from the signal source. In ambient environments, motion artifacts can be up to ten times stronger than the desired signals, varying with environmental conditions. This study evaluates the variability of these artifacts and the effectiveness of a gradiometer in reducing them in such settings.

Microfabrication Technologies for Nanoinvasive and High-Resolution Magnetic Neuromodulation.

The increasing demand for precise neuromodulation necessitates advancements in techniques to achieve higher spatial resolution. Magnetic stimulation, offering low signal attenuation and minimal tissue damage, plays a significant role in neuromodulation. Conventional transcranial magnetic stimulation (TMS), though noninvasive, lacks the spatial resolution and neuron selectivity required for spatially precise neuromodulation.

Alignment of magnetic sensing and clinical magnetomyography.

Neuromuscular diseases are a prevalent cause of prolonged and severe suffering for patients, and with the global population aging, it is increasingly becoming a pressing concern. To assess muscle activity in NMDs, clinicians and researchers typically use electromyography (EMG), which can be either non-invasive using surface EMG, or invasive through needle EMG. Surface EMG signals have a low spatial resolution, and while the needle EMG provides a higher resolution, it can be painful for the patients, with an additional risk of infection.

Wearable super-resolution muscle-machine interfacing.

Muscles are the actuators of all human actions, from daily work and life to communication and expression of emotions. Myography records the signals from muscle activities as an interface between machine hardware and human wetware, granting direct and natural control of our electronic peripherals. Regardless of the significant progression as of late, the conventional myographic sensors are still incapable of achieving the desired high-resolution and non-invasive recording.

Channels and Features Identification: A Review and a Machine-Learning Based Model With Large Scale Feature Extraction for Emotions and ASD Classification.

Autism Spectrum Disorder (ASD) is characterized by impairments in social and cognitive skills, emotional disorders, anxiety, and depression. The prolonged conventional ASD diagnosis raises the sheer need for early meaningful intervention. Recently different works have proposed potential for ASD diagnosis and intervention through emotions prediction using deep neural networks (DNN) and machine learning algorithms.

Spintronic Eyeblink Gesture Sensor With Wearable Interface System.

This work presents an eyeblink system that detects magnets placed on the eyelid via integrated magnetic sensors and an analogue circuit on an eyewear frame (without a glass lens). The eyelid magnets were detected using tunnelling magnetoresistance (TMR) bridge sensors with a sensitivity of 14 mV/V/Oe and were positioned centre-right and centre-left of the eyewear frame. Each eye side has a single TMR sensor wired to a single circuit, where the signal was filtered (<0.

Using the Intelligent System to Improve the Delivered Adequacy of Dialysis by Preventing Intradialytic Complications.

Acute kidney failure patients while detoxificated by hemodialysis (HD) mostly or continuously faced regular problems such as low blood pressure (hypotension), muscle cramps, nausea, or vomiting. Higher intradialytic symptom leads to low-quality HD treatment. Although more known therapeutic interventions are used to relieve the HD side effects, this study was designed to investigate how intelligent systems can make highly beneficial alterations in dialysis facilities and equipment to ease intradialytic complications and help the staff deliver high-quality treatment.

Cleanroom strategies for micro- and nano-fabricating flexible implantable neural electronics.

Implantable electronic neural interfaces typically take the form of probes and are made with rigid materials such as silicon and metals. These have advantages such as compatibility with integrated microchips, simple implantation and high-density nanofabrication but tend to be lacking in terms of biointegration, biocompatibility and durability due to their mechanical rigidity. This leads to damage to the device or, more importantly, the brain tissue surrounding the implant.

Neural microprobe modelling and microfabrication for improved implantation and mechanical failure mitigation.

Careful design and material selection are the most beneficial strategies to ensure successful implantation and mitigate the failure of a neural probe in the long term. In order to realize a fully flexible implantable system, the probe should be easily manipulated by neuroscientists, with the potential to bend up to 90°. This paper investigates the impact of material choice, probe geometry, and crucially, implantation angle on implantation success through finite-element method simulations in followed by cleanroom microfabrication.

Rotating neurons for all-analog implementation of cyclic reservoir computing.

Hardware implementation in resource-efficient reservoir computing is of great interest for neuromorphic engineering. Recently, various devices have been explored to implement hardware-based reservoirs. However, most studies were mainly focused on the reservoir layer, whereas an end-to-end reservoir architecture has yet to be developed.

Schemes for Single Electron Transistor Based on Double Quantum Dot Islands Utilizing a Graphene Nanoscroll, Carbon Nanotube and Fullerene.

The single electron transistor (SET) is a nanoscale switching device with a simple equivalent circuit. It can work very fast as it is based on the tunneling of single electrons. Its nanostructure contains a quantum dot island whose material impacts on the device operation.

Flexible Piezoelectric Sensors for Miniaturized Sonomyography.

Sonomyography refers to the measurement of muscle activity with an ultrasonic transducer. It is a candidate modality for applications in diagnosis of muscle conditions, rehabilitation engineering and prosthesis control as an alternative to electromyography. We propose a mechanically-flexible piezoelectric sonomyography transducer.

A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection.

There is a growing interest in neuromorphic hardware since it offers a more intuitive way to achieve bio-inspired algorithms. This paper presents a neuromorphic model for intelligently processing continuous electrocardiogram (ECG) signal. This model aims to develop a hardware-based signal processing model and avoid employing digitally intensive operations, such as signal segmentation and feature extraction, which are not desired in an analogue neuromorphic system.

Nanotechnology Enables Novel Modalities for Neuromodulation.

Neuromodulation is of great importance both as a fundamental neuroscience research tool for analyzing and understanding the brain function, and as a therapeutic avenue for treating brain disorders. Here, an overview of conceptual and technical progress in developing neuromodulation strategies is provided, and it is suggested that recent advances in nanotechnology are enabling novel neuromodulation modalities with less invasiveness, improved biointerfaces, deeper penetration, and higher spatiotemporal precision. The use of nanotechnology and the employment of versatile nanomaterials and nanoscale devices with tailored physical properties have led to considerable research progress.

Challenges to the Development of the Next Generation of Self-Reporting Cardiovascular Implantable Medical Devices.

Cardiovascular disease (CVD) is a group of heart and vasculature conditions which are the leading form of mortality worldwide. Blood vessels can become narrowed, restricting blood flow, and drive the majority of hearts attacks and strokes. Reactive surgical interventions are frequently required; including percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG).

Adaptive Extreme Edge Computing for Wearable Devices.

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing.

The Future of Neuroscience: Flexible and Wireless Implantable Neural Electronics.

Neurological diseases are a prevalent cause of global mortality and are of growing concern when considering an ageing global population. Traditional treatments are accompanied by serious side effects including repeated treatment sessions, invasive surgeries, or infections. For example, in the case of deep brain stimulation, large, stiff, and battery powered neural probes recruit thousands of neurons with each pulse, and can invoke a vigorous immune response.

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World.

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic.

A CMOS Hall sensor modeling with readout circuitry and microcontroller processing for magnetic detection.

A Hall sensor array system for magnetic field detection and analysis is realized in X-FAB 0.18 μm CMOS technology. Magnetic field detection is attributed to the magnetization of metal coils to metal particles and the sensing characteristics of the Hall sensor array.

Detection techniques of biological and chemical Hall sensors.

Integrated magnetic Hall effect sensors have been widely used in people's daily life over the past decades, and still are gaining enormous attention from researchers to establish novel applications, especially in biochemistry and biomedical healthcare. This paper reviews, classifies, compares and concludes state-of-the-art integrated Hall magnetic sensors in terms of cost, power, area, performance and application. Current applications of the Hall sensors such as detecting magnetic nanoparticles (MNPs) labeled on biomolecule, monitoring blood pulse wave velocity, characterizing soft biological materials, controlling syringe injection rate and eye surgery by training systems, and assisting magnetic resonance imaging (MRI) will be discussed comprehensively and future applications and trends will be highlighted.

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection.

With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems.