Alefacept provides sustained clinical and immunological effects in new-onset type 1 diabetes patients

BACKGROUND. Type 1 diabetes (T1D) results from destruction of pancreatic β cells by autoreactive effector T cells. We hypothesized that the immunomodulatory drug alefacept would result in targeted quantitative and qualitative changes in effector T cells and prolonged preservation of endogenous insulin secretion by the remaining β cells in patients with newly diagnosed T1D

Eco-friendly all-carbon paper electronics fabricated by a solvent-free drawing method

Here we report the fabrication of high-performance all-carbon temperature and infrared (IR) sensors with a solvent-free multiwalled carbon nanotube (MWCNT) trace as the sensing element and commercial graphite pencil trace as the electrical contact on recyclable and biodegradable cellulose filter paper without using any toxic materials or complex procedures. The temperature sensor shows a large negative temperature coefficient of resistance (TCR) in the range of −3100 ppm K−1 to −4900 ppm K−1, which is comparable to available commercial temperature sensors, and an activation energy of 34.85 meV. The IR sensor shows a high responsivity of 58.5 V W−1, which is greater than reported IR sensors with similar dimensions. A detailed study of the conduction mechanism in MWCNTs with temperature and the photo response with IR illumination was done and it was found that the conduction is due to thermally assisted hopping in band tails and the photo response is bolometric in nature. The successful fabrication of these sensors on cellulose filter paper with a comparable performance to existing components indicates that it is possible to fabricate high-performance electronics using low-cost, eco-friendly materials without the need for expensive clean-room processing techniques or harmful chemicals

Discrimination of gases with a single chemiresistive multi-gas sensor using temperature sweeping and machine learning

Selective gas leakage detection is crucial due to the adverse effects of harmful gases present in the air on human health. However, existing high-temperature metal oxide gas sensors, which consume more power, are nonspecific and more sensors are required to discriminate different gases. On the other hand, room temperature gas sensors suffer from slow response and poor reliability despite their low power consumption. We proposed a method to discriminate three gases at relatively low power consumption with a single metal oxide gas sensor using temperature sweeping to address these issues. The machine learning classification algorithms in conjunction with the ternary logic of the sensor response at different temperatures were utilized to discriminate the gases. As the proposed approach requires just one metal oxide gas sensor instead of an array consisting of at least three nonspecific sensors to discriminate the multiple gases, the power consumption can be reduced significantly. As a feasible solution to the existing issues of conventional sensors, this novel methodology paves the way for the widespread use of the sensors in practical applications where reduction of power consumption is necessary

Solvent-free fabrication of paper based all-carbon disposable multifunctional sensors and passive electronic circuits

In light of recent interest in the green fabrication of electronics, we report eco-friendly engineered temperature sensors, pH sensors, humidity sensors and passive resistor-capacitor (RC) filters by solventfree processing of graphite on cellulose paper. This was achieved via direct writing of graphite pencil on cellulose paper which involves the deposition of few layers to multi layers of conductive graphene flakes intercalated by clay and wax. The temperature sensor exhibits a negative temperature coefficient of resistance of -4232 ppm K-1, which is comparable to that of conventional temperature sensors (platinum, nickel, and copper) that are fabricated by capital intensive and complex procedures. The dynamic response of the temperature sensor shows its repeatability with excellent response time of 13.5 s. The all-carbon pH sensor could efficiently distinguish acidic, alkaline and neutral solutions with significant sensitivity from 1.77 k Omega pH(-1) to 2.21 k Omega pH(-1). The higher sensitivity of the pH sensor is attributed to the oxygen functional groups present in pencil graphite which undergoes protonation and deprotonation in presence of H+ and OH- ions to alter the conductivity of graphite trace. The interdigitated capacitive humidity sensor shows a linear response to humidity with fast response (1.5-2 s) and recovery times (6-7 s). These fast response and recovery times are due to fast adsorption and evaporation kinetics of water molecules on polar OH groups of cellulose fibers in paper, which depends on porosity of the paper. The versatility of the pencil-on-paper approach was further explored by drawing resistor and interdigitated capacitor in series to fabricate in-plane all-carbon RC filter which demonstrate anticipated functionality with a cut-off frequency of 6 kHz. To the best of our knowledge, no studies have been reported on direct-write graphite on paper based in-plane all carbon passive electronic circuits, pH sensor and humidity sensor. This cleanroom-free approach further expands the scope of graphite on paper as a functional material in developing sensors and circuits for greener consumer electronics thereby causing no environmental contamination either in their production or disposal

Low cost, flexible and biodegradable touch sensor fabricated by solvent-free processing of graphite on cellulose paper

There is a strong demand for flexible user interface technology with a desired size and shape to make real world objects touch-interactive. In this regard, plastic based touch sensors have been widely used on account of their adaptability. However, their fabrication involves eco-unfriendly procedures like lithography or other cleanroom processes which contribute greenhouse gases (GHG) and hence raises the need for exploring alternative green fabrication methods. In this work, we report the fabrication of an inexpensive, flexible, biocompatible, low power and environmentally benign interdigitated capacitive (IDC) touch sensor based on cleanroom-free and solvent-free processing of graphite on paper (GOP). The performance of touchpad with four touch sensor keys was evaluated by integrating it with Arduino UNO development board and it functioned similar to the conventional touchpads which are fabricated by utilizing complex procedures and expensive equipments on plastic substrates. The influence of the number and the overlap length of electrode digits on touch sensor performance was investigated and optimized based on variation in capacitance with the interaction of finger with the touch sensor for better performance and enhanced user experience. The pressure due to strength of the touch and strain induced due to bending and folding of the touch sensor resulted in insignificant variation in capacitance and no change in functionality. This suggests that the flexible and robust GOP based touchpad can be used in diverse applications such as user interface for medical diagnostics, health, environment and security monitoring devices where low cost, eco-friendly, flexible, durable and stable touch control is required. This approach of developing highly advanced, efficient yet biodegradable paper electronics which doesn’t involve the use of any toxic, flammable or corrosive gases nor chemicals paves the way for next generation green and sustainable eco-friendly electronics

Drift independent discrimination of H2S from other interfering gases with a metal oxide gas sensor using extracted adsorption-desorption noise

Gas detection is attaining significant attention in the healthcare sector due to the health issues involved with the inhalation of toxic gases present in the air. Though the existing sensors can efficiently detect the target gases, they suffer from high power consumption, interference with other gases, and baseline drift. Here, we present a drift-independent technique to achieve selectivity and low power consumption with a single unfunctionalized metal oxide gas sensor. We utilized adsorption-desorption noise to discriminate H2S from NH3 and CO2. As it addresses typical challenges of existing resistive gas sensors, the proposed methodology paves the way for developing future low-power selective gas sensing technology

Deep Learning Methods for Detecting Chilli Pests: A Novel Performance Analysis

Ensuring food security is a top goal for all nations, yet infected plants can negatively impact agricultural production and the country's economic resources. In the past, farmers have depended on conventional techniques to enhance crop yield. In recent times, there has been a significant decline in crop production due to pest infestations on Chilli crops. The progress of deep learning techniques facilitates the categorization of diverse sorts of images in practical applications. Especially, detecting multi-class Chilli crop pests with good accuracy using deep learning algorithms is consistently a significant challenge. The proposed study concentrated in identifying pests on Chilli leaves using deep learning methods such as YOLOv5 and YOLOv7. To improve classification accuracy, a new and unique dataset called the standard balanced custom ‘Chilli pest dataset’ is created with 13,414 pest images. This dataset includes three specific pest classes: Black Thrips, Redmites, and White Fly. We analysed the custom Chilli dataset using YOLOv5 and YOLOv7 to evaluate their effectiveness in detecting pests in Chilli crops and obtained novel detection performance metrics. The resultant parameters mean Average Precision (mAP) for all three pest classes is 98.6% for YOLOv5 and 86.1% for YOLOv7. The YOLOv5s detector demonstrates superior performance compared to the YOLOv7 pest classification, with a 12.5% improvement. The YOLOv7 algorithm achieves its best classification accuracy (86.1%) at a lower epoch (110), while the YOLOv5 algorithm achieves its highest classification accuracy (98.6%) at a higher epoch (350). Nevertheless, despite this distinction, the YOLOv5 algorithm is recommended as the superior detector for accurately identifying pests in well-balanced multi-class pest type datasets, in comparison to YOLOv7, VGG-16 (~92.7%), and VGG-19 (~84.24%) deep learning architectures

Flexible, eco-friendly and highly sensitive paper antenna based electromechanical sensor for wireless human motion detection and structural health monitoring

Flexible antenna sensors have gained significant momentum in recent times due to increase in demand for internet of things (IOT) technology. Conventional flexible antenna-based wireless sensors fabricated on plastic substrates are neither biodegradable nor recyclable, use expensive and sophisticated equipment and eco-unfriendly solution processing to fabricate radiating element thus generating e-waste and causing environment contamination. Here we fabricated a 2.4 GHZ paper-based rectangular patch antenna in which aluminium tape is used as radiating patch and ground pane and cellulose filter paper is used as dielectric substrate that separates radiating patch and ground plane. This aluminium on paper antenna exhibits excellent average sensitivity similar to 3.23 and similar to 3.34 to 1.67% of compressive and tensile bending strain respectively and shows stable performance after hundreds of bending cycles. The performance of antenna sensor was evaluated by subjecting it to microstrains to identify small cracks, folding it with different angles to sense the angle of bending and by interfacing it with hand gloves to detect human motion. Due to versatility in sensing, robustness and flexibility, these antennas can be used as use-and-recycle wireless disposable electromechanical sensors for wearable electronics, human machine interfacing (HMI), crack detection of oils/flammable gas pipelines, and intelligent wireless monitoring of movements