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This paper presents a back-gated ZnO nanowires (NWs) field-effect transistor (FET) realized through the contact printing process. A low-stress $\mathbf{SiN}_{\mathbf{x}}$ deposited at room temperature by inductively coupled plasma (ICP) process was employed as the back-gate dielectric. Importantly, the adopted fabrication process evades any high-temperature process including for the deposition of the high-quality gate dielectric. The temperature fabrication is critical for development of electronic devices on flexible substrates. The ZnO NW FET shows excellent performance, which is reflected through transconductance (477 nS) and $\mathbf{I}_{\mathbf{on}}/\mathbf{I}_{\mathbf{off}}$ ratio $(\sim\mathbf{10}^{\mathbf{6})}$ . Finally, the developed ZnO NW FET was investigated for UV detection and it exhibited a gate tunable photoresponsivity.

Ultra-thin chips (UTC) of silicon are a promising approach to address the immediate demand for the development of high performance flexible electronic systems. However, Si is brittle and rigid in nature, thereby limiting its application in flexible electronics. The mechanical flexibility could be induced to Si, by thinning it down to < 50 µm. This paper presents an overview of post-processing steps adopted for realisation of UTC. In particular, we have focussed on wet-etching and two protective layers, namely ProTEK© B3 and polydimethylsiloxane (PDMS), which could be used as a low-cost method for protecting the front side of the wafer comprising of electronics devices.

Patients suffering from chronic non-healing wounds must deal with long-term physical pain, emotional distress, and financial burden. Monitoring of wound-exudate from chronic wounds could improve the efficacy of patient care, leading to improved treatment outcomes. We present a flexible bandage sized multi-site wound monitoring system with an on-board wireless embedded system consisting of four potentiostat analog front ends and a temperature sensor. This embedded system interfaces with four flexible electrochemical sensors screen-printed onto wound dressings for uric acid sensing; a biomarker for monitoring wound healing. This system measures uric acid levels at multiple sites of a single wound, allowing for spatial resolution when evaluating healing of large wounds. This work demonstrates the ability of this system to measure uric acid in-vitro. The system transmits potentiostat data to a remote data aggregator such as a tablet or smartphone, and can operate for up to 10 days powered by the on-board 350 mAh lithium polymer battery.

Sensory feedback from touch is critical for many tasks carried out by robots and humans, such as grasping objects or identifying materials. Electronic skin (e-skin) is a crucial technology for these purposes. Artificial tactile skin that can play the roles of human skin remains a distant possibility because of hard issues in resilience, manufacturing, mechanics, sensorics, electronics, energetics, information processing, and transport. Taken together, these issues make it difficult to bestow robots, or prosthetic devices, with effective tactile skins. Nonetheless, progress over the past few years in relation with the above issues has been encouraging, and we have achieved close to providing some of the abilities of biological skin with the advent of deformable sensors and flexible electronics. The naive imitation of skin morphology and sensing an impoverished set of mechanical and thermal quantities are not sufficient. There is a need to find more efficient ways to extract tactile information from mechanical contact than those previously available. Renewed interest in neuromorphic tactile skin is expected to bring some fresh ideas in this field. This article reviews these new developments, particularly related to the handling of tactile data, energy autonomy, and large-area manufacturing. The challenges in relation with these advances for tactile sensing and haptics in robotics and prosthetics are discussed along with potential solutions.

Circular economy focussing on the reuse and recycling of materials is gaining significant interest these days as the concern for environment sustainability is increasing [1] - [3] . In this regard, printed electronics or green electronics is being promoted as alternative to conventional electronics, which requires several hazardous and toxic materials. However, there is lot to be done to align this emerging field with the requirements of circular economy [4] , [5] and one way is to identify the waste materials and transfer them into a valuable products [6] . In this regard, integration of electronics in textiles is one of the attractive directions [7] and recently flexible devices like solar cells, sensors and electronics have been successfully integrated into textiles [8] - [12] . Here we present, a temperature sensor fabricated on biodegradable cellulose cloth. The fabricated cloth based temperature sensors shows a sensitivity of 30°C/Ω in the temperature range of 25-60°C.

This paper presents van der Waals (vdW) contacted graphene field-effect transistors (GFETs) and logic circuits, both on rigid and flexible substrates. These logic gates exhibit stable performances after 100 cycles of bending. Realized with complementary metal oxide semiconductor (CMOS) compatible technology, the vdW contacted GFETs show performance comparable to the top-contacted GFETs and thus present excellent cost-effective alternatives to top- and edge-contacted GFETs for large-area electronic applications.

Inorganic nanostructures on flexible substrates form key components for high performance flexible electronics. 1D (Nanowires) and 2D (graphene) nanostructures are inherently flexible, in addition to having attractive electronic properties. The utility of these nanostructures for high performance flexible electronics can be maximized through an effective transfer processes. In this regard, the hot lamination method is presented here as an effective technique to develop a 3D stack of ZnO nanowires (NWs) and graphene on flexible PVC substrates. The transfer process presented here produces well-adhered ZnO NWs on flexible PVC substrate while maintaining NWs structure intact. The diameter of the NWs, synthesized by using vapour-liquid-solid (VLS) growth mechanism, is in the range of 50–100 nm. The ZnO - monolayered (ML) graphene structure was studied using electron microscopy followed by electrochemical analysis.

This work presents touch sensing device with flexible piezoelectric Aluminium Nitride (AIN) capacitor tightly coupled with a MOSFET device. Unlike conventional high-temperature deposition, the A1N in this work was sputtered on polyimide at room temperature to obtain a flexible piezoelectric touch sensing element. Further, the proposed device has reliable operation as unlike other similar touch sensing devices, where high voltage needed to polarize the piezoelectric layer poses a major risk for the MOSFET operation, the A1N does not require any poling. The touch sensor has been evaluated in a force range of 0.5-3.5N and normalised change in drain current per unit force or the sensitivity is found to be 2.64 N-1.

This work demonstrates a xanthine oxidase (XO) based electrochemical biosensor for detection of xanthine, a metabolic product of purines in wounds. XO immobilized on a multiwalled carbon nanotube (MWNT) and gold (Au) nanoparticle matrix provided an electron transfer pathway to monitor enzymatic xanthine oxidation. This nanostructured matrix acted also as a catalyst, assisting detection of enzymatic byproduct, hydrogen peroxide. The results show the biosensor has the detection limit of 0.1 nM, and an enhanced performance over a broad linear concentration range of 0.1 nM–7.3 mM compared to MWNT or Au individually. Electrochemical results over a broad pH range depicted a correlation model of the sensor signal with healing through its stages as the pH shifts from alkaline to acidic as recovery progresses. This shows that the biosensor can be used to not only track healing progress but also continuously monitor complications with a thorough assessment of biochemical tissue recuperation.

This paper presents a low voltage graphene field effect transistor (GFET) based pressure sensor. The sensor comprises of GFET connected with a piezoelectric Aluminium Nitride (AIN) capacitor in an extended gate configuration. In this configuration, the piezopotential generated across the AlN capacitor, as a result of applied pressure, appears at the gate terminal of GFET and modulates the channel current. The sensor operates at a remarkably low voltage (100 mV) and exhibits a sensitivity of about 7.18×10 -3 Pa -1 for a pressure range of 3.25-9.74 kPa. These values make the developed GFET sensor suitable for tactile skin in robotics and prosthetics and for wearable health monitoring devices.

This work presents an approach towards integrating enzymatic biosensors to monitor wound healing. Levels of uric acid (UA) a metabolic byproduct of purine degradation in dying cells, is investigated as an indicator of wound healing. The biosensor was fabricated by immobilizing uricase (UOx) natural catalyst for UA as the enzyme and, multi-walled carbon nanotubes (MWNT) and gold nanoparticles (Au) as electron exchange matrix. UA is oxidized by UOx with the Au nanoparticle and MWNT matrix providing thermodynamically feasible electron transfer pathways leading to the detection of byproduct H 2 O 2 , through its reduction on the matrix. This matrix was treated with cold plasma treatment which enabled sintering of Au nanoparticles on MWNTs, without damaging the substrate. This sintering induced densification led to higher conductivity of the matrix. The electrochemical results show that the treatment improved the H 2 O 2 reduction peak potential (E pc ) by 27% (from −534 to −392mV) for cold plasma treated matrix compared to untreated matrix. The biosensor has a linear range of 2.9 µM to 0.1 mM of UA with the sensitivity of 0.1 µA µM−1 cm−2 and the lowest detection concentration of 0.9 µM. These results show that this biosensor can be used to monitor UA in physiological concentrations.

Automatic External Defibrillator is a device that checks the heart rhythm and can send an electric shock to the heart muscle to restore the rhythm. Electric shock will be given in the case of abnormal heart which in termed as Arrhythmia. Cardiac Arrhythmias shows a condition of abnormal electrical activity in the heart which is a threat to humans. There are various algorithms proposed for the detection and classification of arrhythmia such wavelet transforms, Hilbert transform etc. Among these methods time domain analysis is simple with reduced complexity. This paper presents a feature extraction method in time domain for ECG feature extraction using adaptive threshold method for the detection of various peaks and classification of various heart conditions. This is achieved by extracting ECG features such as P, T wave, QRS complex, PR, QT, RR, ST intervals and ST segment deviations for the classification of heart beats according to different arrhythmias. Data were obtained from the records of MIT-BIH arrhythmia database (only one lead). Cardiac arrhythmias which are found are Tachycardia, Supraventricular Tachycardia and Ventricular Fibrillation.

Advances in unsupervised learning have allowed the efficient learning of feature representations directly from large sets of unlabeled data instead of using traditional handcrafted features. However, improving algorithms to increase the quality of these representations in the absence of labeled data is still an area of active research. This paper evaluates visual features learned through unsupervised learning, specifically comparing regularization and preprocessing methods using Gaussian filters on a single-layer network. Using the restricted Boltzmann machine as the unsupervised learning mechanism, features emerging through training on natural videos, with different biasing and preprocessing based on Gaussian filters, are compared by metrics to measure invariance as well as classification performance on standard datasets. When Gaussian filters are convolved with adjacent hidden layer activations from a single example during training, topographies begin to emerge where adjacent features become tuned to slightly varying stimuli. 1D, 2D, and 3D topographies are compared. When a Gaussian low-pass filter is applied to activations from a single hidden node across frames drawn from video, features that are more invariant to transformations are produced. Finally, when Gaussian filters are applied to the visible nodes, images become blurrier; learning from these images also leads to invariant features. The networks are trained using the Hollywood2 video dataset, and tested on image classification of the static CIFAR-10 and STL-10 datasets. To prove that the improvements are independent of the dataset, the networks are shown to produce similar results when trained on the CIFAR-10 dataset. The induction of topography or simple image blurring via Gaussian filters during training produce better discriminative features as evidenced by the consistent and notable increase in classification results that they produce. Also, in the visual domain, invariant features are desirable such that objects can be classified accurately despite transformations. It is found that most of the compared methods produce more invariant features, however, classification accuracy does not correlate to invariance.

Multimodal electrochemical method comprising open circuit potential and amperometric technique has been implemented to improve the specificity of the ethanol detection in a fuel cell sensor system. A miniaturized device with LMP91000 potentiostat and a processing unit has been constructed containing simple auto-calibration algorithm. The developed processing unit consist of a low power microcontroller (MSP430F5529LP). The sensing unit composed of a three electrode proton exchange membrane (PEM) fuel cell sensor, where Nafion is the PEM. In these studies, the signal due to interference has been eliminated with the support of algorithm and multimodal electrochemical method. The results show that the sensor can detect ethanol as low as 5ppm. The constructed device was validated by comparing it with the commercially available potentiostat, and the response was similar in both devices.

This paper presents the new route for fabricating field effect transistors (FETs) on flexible or bendable substrates using a combination of transfer printing and screen printing technologies — the former is used to transfer single crystal silicon (Si) microwires on to flexible substrate and the latter to print the metal lines. A back-gated FET structure has been realized with screen printed silver (Ag) lines as the gate electrode and the transfer-printed arrays of Si microwires are utilized as semiconductor layers of the device. Epoxy based negative resist SU-8 has been used as the adhesive as well as the dielectric for the FET device. An array of 20 wires with variable widths and lengths was developed by using standard photolithography and etching techniques. A custom made micro-spotting technique has been used to obtain the source and drain contacts on top of Si microwires. Good mechanical and electrical responses of the FETs have been observed during the cyclic tests and current-voltage (I-V) measurements respectively.