Emerging Internet of Things driven carbon nanotubes-based devices

Carbon nanotubes (CNTs) have attracted great attentions in the field of electronics, sensors, healthcare, and energy conversion. Such emerging applications have driven the carbon nanotube research in a rapid fashion. Indeed, the structure control over CNTs has inspired an intensive research vortex due to the high promises in electronic and optical device applications. Here, this in-depth review is anticipated to provide insights into the controllable synthesis and applications of high-quality CNTs. First, the general synthesis and post-purification of CNTs are briefly discussed. Then, the state-of-the-art electronic device applications are discussed, including field-effect transistors, gas sensors, DNA biosensors, and pressure gauges. Besides, the optical sensors are delivered based on the photoluminescence. In addition, energy applications of CNTs are discussed such as thermoelectric energy generators. Eventually, future opportunities are proposed for the Internet of Things (IoT) oriented sensors, data processing, and artificial intelligence.

Recent Progress and Perspectives on Non-Invasive Glucose Sensors

Self-monitoring of blood glucose forms an important part of the management of diabetes and the prevention of hyperglycaemia and hypoglycaemia. Current glucose monitoring methods either use needle-prick enzymatic glucose-meters or subcutaneous continuous glucose sensors (CGM) and thus, non-invasive glucose measurements could greatly improve the self-management of diabetes. A wide range of non-invasive sensing techniques have been reported, though achieving a level of precision comparable to invasive meters remains a challenge. Optical sensors, which utilise the interactions between glucose and light, offer the potential for non-invasive continuous sensing, allowing real-time monitoring of glucose levels, and a range of different optical sensing technologies have been proposed. These are primarily based upon optical absorption and scattering effects and include infrared spectroscopy, Raman spectroscopy and optical coherence tomography (OCT), with other optical techniques such as photoacoustic spectroscopy (PAS) and polarimetry also reported. This review aims to discuss the current progress behind the most reported optical glucose sensing methods, theory and current limitations of optical sensing methods and the future technology development required to achieve an accurate optical-based glucose monitoring device.

Recent Advances in Wearable Optical Sensors Automation Powered by Battery Versus Skin-Like Battery-Free Devices for Personal Healthcare - A Review

Currently, old-style personal medicare techniques rely mostly on traditional methods, such as cumbersome tools and complicated processes, which can be time-consuming and inconvenient in some circumstances. Furthermore, such old methods need the use of heavy equipment, blood draws, and traditional bench-top testing procedures. Invasive ways of acquiring test samples can potentially cause patients discomfort and anguish. Wearable sensors, on the other hand, may be attached to numerous body areas to capture diverse biochemical and physiological characteristics as a developing analytical tool. Physical, chemical, and biological data transferred via the skin is used to monitor health in various circumstances. Wearable sensors can assess the aberrant conditions of the physical or chemical components of the human body in real-time, exposing the body state in time, thanks to unintrusive sampling and high accuracy. Most commercially available wearable gadgets are mechanically hard components attached to bands and worn on the wrist, with form factors ultimately constrained by the size and weight of the batteries required for the power supply. Wearable gadgets with “skin-like” qualities are a new type of automation that is only starting to make its way out of research labs and into pre-commercial prototypes. In this paper, we studied the recent advancement in battery-powered wearable sensors established on optical phenomena and skin-like battery-free sensors which brings a breakthrough in wearable sensing automation.

Bio-GO-SHIP: The Time Is Right to Establish Global Repeat Sections of Ocean Biology

In this article, we present Bio-GO-SHIP, a new ocean observing program that will incorporate sustained and consistent global biological ocean observations into the Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP). The goal of Bio-GO-SHIP is to produce systematic and consistent biological observations during global ocean repeat hydrographic surveys, with a particular focus on the planktonic ecosystem. Ocean plankton are an essential component of the earth climate system, form the base of the oceanic food web and thereby play an important role in influencing food security and contributing to the Blue Economy. Despite its importance, ocean biology is largely under-sampled in time and space compared to physical and chemical properties. This lack of information hampers our ability to understand the role of plankton in regulating biogeochemical processes and fueling higher trophic levels, now and in future ocean conditions. Traditionally, many of the methods used to quantify biological and ecosystem essential ocean variables (EOVs), measures that provide valuable information on the ecosystem, have been expensive and labor- and time-intensive, limiting their large-scale deployment. In the last two decades, new technologies have been developed and matured, making it possible to greatly expand our biological ocean observing capacity. These technologies, including cell imaging, bio-optical sensors and 'omic tools, can be combined to provide overlapping measurements of key biological and ecosystem EOVs. New developments in data management and open sharing can facilitate meaningful synthesis and integration with concurrent physical and chemical data. Here we outline how Bio-GO-SHIP leverages these technological advances to greatly expand our knowledge and understanding of the constituents and function of the global ocean plankton ecosystem.

Ti/TiO2/SiO2 multilayer thin films with enhanced spectral selectivity for optical narrow bandpass filters

Thin film-based optical sensors have been attracting increasing interest for use in developing technologies such as biometrics. Multilayered dielectric thin films with different refractive indices have been utilized to modulate the optical properties in specific wavelength bands for spectral selectivity of Thin Film Narrow Bandpass Filters (TFNBFs). Progress in TFNBF design has been made with the incorporation of metallic thin films. Narrower bandwidths with higher transmittance have been achieved in specific spectral bands. In this work, Ti/TiO2/SiO2 based multilayer thin films were prepared using pulsed-DC reactive sputtering. Computer simulations using the Essential Macleod Program allowed the optimal number of layers and thickness of the multilayer thin films to be determined to efficiently tailor the optical path transmitting specific wavelength bands. The addition of Ti metal layers within dielectric (TiO2/SiO2) multilayer thin films significantly changes the cutoff frequency of transmittance at specific wavelengths. Representative 26 multilayer films consisting of Ti, TiO2, and SiO2 show lower transmittance of 10.29% at 400 nm and 10.48% at 680 nm. High transmittance of 80.42% at 485 nm was observed, which is expected to improve the spectral selectivity of the TFNBF. This work provides a contribution to future simulation based design strategy based on experimental thin film engineering for potential industrial development opportunities such as optical biometrics.

Semi-Supervised Deep Learning in High-Speed Railway Track Detection Based on Distributed Fiber Acoustic Sensing

High deployment costs, safety risks, and time delays restrict traditional track detection methods in high-speed railways. Therefore, approaches based on optical sensors have become the most remarkable strategy in terms of deployment cost and real-time performance. Owing to the large amount of data obtained by sensors, it has been proven that deep learning, as a powerful data-driven approach, can perform effectively in the field of track detection. However, it is difficult and expensive to obtain labeled data from railways during operation. In this study, we used a segment of a high-speed railway track as the experimental object and deployed a distributed optical fiber acoustic system (DAS). We propose a track detection method that innovatively leverages semi-supervised deep learning based on image recognition, with a particular pre-processing for the dataset and a greedy algorithm for the selection of hyper-parameters. The superiority of the method was verified in both experiments and actual applications.

Спектральные оптические свойства керамических нанопористых мембран на основе анодного оксида алюминия и покрытия из серебра в парах аммиака

Spectral optical properties of synthesized ceramic nanoporous membranes based on anodic aluminum oxide coated silver in saturated ammonia gas flow have been experimentally investigated. Based on the measured transmission spectra and detected interference part of the spectra in wavelength range from 550 to 900 nm, temporal and spectral dependencies of the effective optical thickness and its changes in non-equilibrium conditions were obtained due to adsorption of ammonia molecules on silver film surface. According to detected and measured interference maximum shifts up to 14 nm in transmission spectra of Al2O3 + Ag membranes in ammonia gas flow, the possibility of constructing a selective interferometric optical sensors with 10 − 15 min response time is shown.

Design and Simulation of Waveguide Bragg Grating based Temperature Sensor in COMSOL

With the advancements in the domain of photonics and optical sensors, Fibre Bragg Grating (FBG) sensors, owing to their increased advantages, have been researched widely and have proved to be useful in sensing applications. Moreover, the advent of Photonic Integrated Circuits (PICs) demands the incorporation of optical sensing in waveguides, which can be integrated on silicon photonic chips. In this paper, the design of a sub-micron range Waveguide Bragg Grating (WBG) based temperature sensor with high peak reflectivity and thermal sensitivity is proposed. The flexibility of COMSOL Multiphysics software is explored to simulate the sensor and the results are verified with the analytical values calculated using MATLAB. The simulation is carried out for the proposed design having 16000 gratings and a corresponding peak reflectivity of 0.953 is obtained. A thermal sensitivity of 80 pm/K is achieved, which is approximately eight times better than that of FBG based sensor.