Department of Electrical Engineering

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IIT Jodhpur

We report the effect of varying the semiconductor:polymer blend ratio on the electrical performance and bias-stress stability of the organic field effect transistors (OFETs) with TIPS-Pentacene and polystyrene as semiconductor and polymer combination. Device performance was found to improve with increasing polymer content in the solution. Devices with 1:3 TIPS-Pentacene:polystyrene blend outperformed the other counterparts with high performance, and demonstrated least performance variation with regular 100 transfer measurement cycles. In addition, 1:3 blend devices have shown the least normalized drain current decay of \sim 8.5 % with the bias stress condition of VDS = VGS = -30 V for 2 h, as compared to the 1:1, 3:1 and neat devices with the decay of 15 %, 37 % and 71 % respectively. Additionally, a better recovery of electrical characteristics was observed in blend devices with larger polymer fractions from the deteriorating effects of gate bias stress. © 2018 IEEE.

World University root node

2018 4th IEEE International Conference on Emerging Electronics, ICEE 2018

Effect of TIPS-Pentacene:Polystyrene Blend Ratio on Electrical Performance and Stability of Solution Processed Organic Field-Effect Transistors

A controllable networked system is steered from an initial state to the desired state with the help of a set of driver nodes. The set of driver nodes used to control a networked system may not be unique. There exist multiple set of driver nodes which may be utilized to control the networked system. This is imperative to characterize these sets of driver nodes for identification of an optimal set of driver nodes. This paper presents mathematical formulation and two algorithms using the Region of Attraction (ROA) to identify an optimal set of driver nodes. Optimal set of driver nodes is identified to maximize the stability region. For any practical networked systems, driver node has limited actuating capacity, this paper consider this limitation a priori. The proposed mathematical formulation and algorithms are verified with the help of numerical examples using the MATLAB simulation. The proposed algorithms have been validated by applying on a robotic network. © 2018, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.

International Journal of Control, Automation and Systems

Identification of Optimal Set of Driver Nodes in Complex Networked Systems Using Region of Attraction

Under low bit-rate requirements, JPEG baseline causes compression artifacts in the image. Through this paper, a novel region-of-interest (ROI) dependent quantization method in JPEG framework is proposed. The proposed method judiciously quantizes DCT coefficients belonging to salient and non-salient regions of the image. In this work, multiple ROIs are optimally identified and ranked by using variances. The number of classes is adaptively calculated using goodness-ofsegmentation. After the number of classes and their ranks are obtained, the image is divided into blocks of size 8 × 8. These blocks may belong to more than one class and hence these are ranked based on their membership in various classes. 2D-DCT coefficients of each block are obtained and then quantized adaptively based on their ranks. Overhead for rank information of blocks is minimized by applying delta-encoding. Results are analyzed in terms of objective quality parameters and visual perception and found that the blocking artifacts in the proposed method are significantly lower than JPEG. The efficiency of the proposed method is demonstrated by results of recently published similar methods and the former is found superior in terms of quality of the reconstructed image. © The Institution of Engineering and Technology 2018.

IET Image Processing

Saliency enabled compression in JPEG framework

In this study, a promising microbial fuel cell (MFC) system has been developed, wherein algae is cultivated in the cathode chamber, algae biomass is harvested and lipids are extracted. The lipid extracted algal (LEA) biomass was then used as an electron donor substrate. The performance of MFCs fed with LEA biomass was compared with that of fruit waste fed MFCs (FP-MFCs), wherein LEA-fed MFC was superior in all aspects. Power density of 2.7 W m−3 was obtained by LEA-fed MFCs which is 145% and 260% higher than FP MFC and control MFC respectively. The volumetric algae productivity of 0.028 kg m−3 day−1 in cathode chamber was achieved. The system was able to generate 0.0136 kWh Kg−1 COD day−1 of electric energy and 0.0782 kWh m−3 day−1 of algal oil energy. The proposed system is a net energy producer which does not rely heavily on the external supply of electron donor substrates. © 2017 Elsevier Ltd

Department of Physics

Bioresource Technology

Microbial fuel cell powered by lipid extracted algae: A promising system for algal lipids and power generation

We report enhanced hydrogen-gas-sensing performance of a Ni-doped ZnO sensor decorated with the optimum concentration of reduced graphene oxide (rGO). Ni-doped ZnO nanoplates were grown by radio frequency sputtering, rGO was synthesized by Hummer's method and decorated by the drop cast method of various concentration of rGO (0-1.5 wt %). The current-voltage characteristics of the rGO-loaded sensor are highly influenced by the loading concentration of rGO, where current conduction decreases and sensor resistance increases as the rGO concentration is increased up to 0.75 wt % because of the formation of various Schottky heterojunctions at rGO/ZnO interfaces. With the combined effect of more active site availability and formation of various p-n heterojunctions due to the optimum loading concentration of rGO (0.75 wt %), the sensor shows the maximum sensing response of ∼63.8% for 100 ppm hydrogen at moderate operating temperature (150 °C). The rGO-loaded sensors were able to detect a minimum of 1 ppm hydrogen concentration and showed high selectivity. However, a further increase in the rGO concentration (1.5 wt %) leads to the reduction of the relative response of hydrogen gas, ascribed to the formation of interconnections of rGO between electrodes. Therefore, it reduces the total resistance of the sensor and minimizes the effect of p-n heterojunction on sensor response. © 2018 American Chemical Society.

ACS Applied Materials and Interfaces

Improved Sensitivity with Low Limit of Detection of a Hydrogen Gas Sensor Based on rGO-Loaded Ni-Doped ZnO Nanostructures

We demonstrated an ultraviolet detector employing an in-plane transport channel of n-type MoS2 with out-of-plane p-type rGO, which acts as a sensitizer for underlying n-type MoS2 photodetector. A developed vertical built-in field from vertical p-n nano-heterojunction separates the photo-excited carriers at the rGO/MoS2 interface. Therefore, the rGO/MoS2 device showed a notably improved photo-responsivity of \sim 6.92 A)/W and an excellent detectivity of 1.26 \times 1012 Jones under the irradiation of ultraviolet light. Moreover, the device exhibited an excellent reproducibility and stability in ambient environment even after four months. © 2018 IEEE.

High-performance ultraviolet detector employing out-of-plane rGO/MoS2 PN heterostructure

Toxic gases are produced during the burning of fossil fuels. Room temperature (RT) fast detection of toxic gases is still challenging. Recently, MoS2 transition metal dichalcogenides have sparked great attention in the research community due to their performance in gas sensing applications. However, MoS2 based gas sensors still suffer from long response and recovery times, especially at RT. Considering this challenge, here, we report photoactivated highly reversible and fast detection of NO2 sensors at room temperature (RT) by using mixed in-plane and edge-enriched p-MoS2 flakes (mixed MoS2). The sensor showed fast response with good sensitivity of â10.36% for 10 ppm of NO2 at RT without complete recovery. However, complete recovery was obtained with better sensor performance under UV light illumination at RT. The UV assisted NO2 sensing showed improved performance in terms of fast response and recovery kinetics with enhanced sensitivity to 10 ppm NO2 concentration. The sensor performance is also investigated under thermal energy, and a better sensor performance with reduced sensitivity and high selectivity toward NO2 was observed. A detailed gas sensing mechanism based on the density functional theory (DFT) calculations for favorable NO2 adsorption sites on in-plane and edge-enriched MoS2 flakes is proposed. This study revealed the role of favorable adsorption sites in MoS2 flakes for the enhanced interaction of target gases and developed a highly sensitive, reversible, and fast gas sensor for next-generation toxic gases at room temperature. © 2018 American Chemical Society.

ACS Sensors

Photoactivated Mixed In-Plane and Edge-Enriched p-Type MoS2 Flake-Based NO2 Sensor Working at Room Temperature

This paper deals with a new version of an incremental conductance algorithm for maximum power harvesting (MPH) from the solar photovoltaic array, which has inherent decision taking and self-adaptive ability. The working principle of a self-adaptive incremental conductance (SAInC) algorithm is based on three consecutive operating points on the power-voltage characteristic. These points smartly detect the dynamic condition, as well as under normal condition, search the maximum power peak (MPP) zone. Moreover, using triangular analogy, it decides the optimum operating position for next iteration, which is responsible for quick MPP tracking as well as good dynamic performance. Here, in every new iteration, the step-size is reduced by 90% from the previous step-size, which provides an oscillation-free steady-state performance. The effectiveness of the proposed technique is validated by MATLAB simulation as well as tested on an experimental system. Moreover, performance of an SAInC algorithm is compared with the popular and recent state-of-the-art methods. The satisfactory dynamic and steady-state performances with low complexity as well as low computational burden of the SAInC algorithm show the superiority over state-of-the-art methods. © 2005-2012 IEEE.

IEEE Transactions on Industrial Informatics

Self-Adaptive Incremental Conductance Algorithm for Swift and Ripple-Free Maximum Power Harvesting from PV Array

Pyroelectric infrared sensors utilize interfacial polymer bonding, thick porous silica layer and Si3N4 thin film as conventional methods of reducing heat sinking with substrate. The thick polymer and porous silica layers cause additional enhancement in thermal time constant (τD) as well thermal mass (Cth) of pyroelectric sensor which reduces the rate of temperature change (dT/dt) and limits responsivity at lower frequencies. The SiO2 thin layer posses lower heat conductivity than Si3N4 thin films and often used in Si bulk micromachining as etch barrier. This paper proposes use of micromachined SiO2 thin membrane as thermal insulation layer in bulk micromachined pyroelectric infrared sensor. The study is supported by the comparative radiation heat transfer, finite element analysis and the practical thermal measurements on fabricated pyroelectric infrared sensor using non destructive thermal imaging method. © 2017, Springer-Verlag GmbH Germany.

Microsystem Technologies

Efficient thermal utilization in MEMS bulk micromachined pyroelectric infrared sensor using thermal oxide thin layer

This work reports fabrication and characterization of Film Bulk Acoustic Resonator (FBAR) to improve the performance characteristics for RF filter and sensing application. Zinc oxide as a piezoelectric (PZE) material was deposited on an aluminum bottom electrode using an RF magnetron sputtering, at room temperature, and gold as top electrode for the resonator. Tetramethyl ammonium hydroxide (TMAH) setup was used for bulk silicon etching to make back side cavity to confine the acoustic signals. The transmission characteristics show that the FBARs have a central frequency at 1.77 GHz with a return loss of −10.7 dB. © 2018 Elsevier Ltd