Long range-based low-power wireless sensor node

Sensor nodes are the most significant part of a wireless sensor network that offers a powerful combination of sensing, processing, and communication. One major challenge while designing a sensor node is power consumption, as sensor nodes are generally battery-operated. In this study, we proposed the design of a low-power, long range-based wireless sensor node with flexibility, a compact size, and energy efficiency. Furthermore, we improved power performance by adopting an efficient hardware design and proper component selection. The Nano Power Timer Integrated Circuit is used for power management, as it consumes nanoamps of current, resulting in improved battery life. The proposed design achieves an off-time current of 38.17309 nA, which is tiny compared with the design discussed in the existing literature. Battery life is estimated for spreading factors (SFs), ranging from SF7 to SF12. The achieved battery life is 2.54 years for SF12 and 3.94 years for SF7. We present the analysis of current consumption and battery life. Sensor data, received signal strength indicator, and signal-to-noise ratio are visualized using the ThingSpeak network.     

Implementation and Performance Evaluation of Ferroelectric Negative Capacitance FET

Silicon - With the constant increase in power dissipation of nanoscale transistors, the almost four-decade-old cycle of performance advancement in complementary...     

Direct-Patterning ZnO Deposition by Atomic-Layer Additive Manufacturing Using a Safe and Economical Precursor

Area-selective atomic layer deposition (AS-ALD) is a bottom-up nanofabrication method delivering single atoms from a molecular precursor. AS-ALD enables self-aligned fabrication and outperforms lithography in terms of cost, resistance, and equipment prerequisites, but it requires pre-patterned substrates and is limited by insufficient selectivity and finite choice of substrates. These challenges are circumvented by direct patterning with atomic-layer additive manufacturing (ALAM) — a transfer of 3D-printing principles to atomic-layer manufacturing where a precursor supply nozzle enables direct patterning instead of blanket coating. The reduced precursor vapor consumption in ALAM as compared with ALD calls for the use of less volatile precursors by replacing diethylzinc used traditionally in ALD with bis(dimethylaminopropyl)zinc, Zn(DMP)₂. The behavior of this novel ZnO ALAM process follows that of the corresponding ALD in terms of deposit quality and growth characteristics. The temperature window for self-limiting growth of stoichiometric, crystalline material is 200–250 °C. The growth rates are 0.9 Å per cycle in ALD (determined by spectroscopic ellipsometry) and 1.1 Å per pass in ALAM (imaging ellipsometry). The preferential crystal orientation increases with temperature, while energy-dispersive X-ray spectroscopic and XPS show that only intermediate temperatures deliver stoichiometric ZnO. A functional thin-film transistor is created from an ALAM-deposited ZnO line and characterized.     

Electrochemical biosensor based on gold coated iron nanoparticles/chitosan composite bound xanthine oxidase for detection of xanthine in fish meat

A xanthine oxidase was immobilized covalently onto chitosan bound gold coated iron nanoparticles (CHIT/Fe-NPs@Au) electrodeposited on the surface of pencil graphite electrode (PGE). A xanthine biosensor was fabricated using XOD/CHIT/Fe-NPs@Au/PGE as working, Ag/AgCl as reference and Pt as auxiliary electrode connected through potentiostat. The enzyme electrode was characterized by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy and electrochemical impedance spectroscopy (EIS). The biosensor exhibited optimum current response within 3 s at pH 7.4, 35 °C and working range 0.1–300 μM, when polarized at 0.5 V vs Ag/AgCl. The sensitivity of the biosensor was 0.001169 mAμ M^–1 cm^–2 with detection limit of 0.1 μM (S/N = 3). The biosensor showed only 25% loss in its initial activity after its 100 uses over 100 days, when stored at 4 °C.     

Automatic speaker recognition from speech signal using bidirectional long-short-term memory recurrent neural network

Speaker recognition is a major challenge in various languages for researchers. For programmed speaker recognition structure prepared by utilizing ordinary speech, shouting creates a confusion between the enlistment and test, henceforth minimizing the identification execution as extreme vocal exertion is required during shouting. Speaker recognition requires more time for classification of data, accuracy is optimized, and the low root-mean-square error rate is the major problem. The objective of this work is to develop an efficient system of speaker recognition. In this work, an improved method of Wiener filter algorithm is applied for better noise reduction. To obtain the essential feature vector values, Mel-frequency cepstral coefficient feature extraction method is used on the noise-removed signals. Furthermore, input samples are created by using these extracted features after the dimensions have been reduced using probabilistic principal component analysis. Finally, recurrent neural network-bidirectional long-short-term memory is used for the classification to improve the prediction accuracy. For checking the effectiveness, the proposed work is compared with the existing methods based on accuracy, sensitivity, and error rate. The results obtained with the proposed method demonstrate an accuracy of 95.77%.     

Size and Shape Exclusion in 2D Silicon Dioxide Membranes

2D membranes such as artificially perforated graphene are deemed to bring great advantages for molecular separation. However, there is a lack of structure-property correlations in graphene membranes as neither the atomic configurations nor the number of introduced sub-nanometer defects are known precisely. Recently, bilayer silica has emerged as an inherent 2D membrane with an unprecedentedly high areal density of well-defined pores. Mass transfer experiments with free-standing SiO₂ bilayers demonstrated a strong preference for condensable fluids over inert species, and the measured membrane selectivity revealed a key role of intermolecular forces in ångstrom-scale openings. In this study, vapor permeation measurements are combined with quantitative adsorption experiments and density functional theory (DFT) calculations to get insights into the mechanism of surface-mediated transport in vitreous 2D silicon dioxide. The membranes are shown to exhibit molecular sieving performance when exposed to vaporous methanol, ethanol, isopropanol, and tert-butanol. The results are normalized to the coverage of physisorbed molecules and agree well with the calculated energy barriers.     

Smart porous microparticles based on gelatin/sodium alginate polyelectrolyte complex

Porous microparticles of different sizes were prepared by polyelectrolyte complexation of biopolymers gelatine A and sodium alginate for microencapsulation of food bioactives. The optimum pH and ratio between the polymers sodium alginate and gelatine for maximum complexation was found as 3.7 and 1:3.5 respectively. Effect of various factors like amount of surfactant, concentration of polymer and crosslinker on the formation, size and porous/nonporous nature of the microparticles were investigated. The particles’ diameter on swelling at pH = 7.4 was twice that at pH = 1.2 indicating the pH responsiveness. These microparticles were used as carrier for ascorbic acid. The surface morphology and sizes of the microparticles were investigated by scanning electron microscope (SEM). Fourier transform infrared spectroscopy (FTIR) study indicated the formation of polyelectrolyte complex between gelatine and sodium alginate and successful encapsulation of ascorbic acid into the microparticles. The microparticles were further characterized by thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and X-ray diffraction (XRD) study.