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Recent Submissions

  • An Assistive Magnetic Skin System: Enabling Technology for Quadriplegics

    Almansouri, Abdullah S.; Upadhyaya, Lakshmeesha; Nunes, Suzana Pereira; Salama, Khaled N.; Kosel, Jürgen (Advanced Engineering Materials, Wiley, 2020-10-27) [Article]
    People with quadriplegia no longer have control over their legs, neither the hands and cannot continue living their life independently. On top of that, severely injured quadriplegics (i.e., C1 and C2 injuries) suffer from speaking difficulties and minimal head and neck movements. With the advancement in wearable artificial skins and the Internet of Things, realizing comfortable and practical solutions for quadriplegics is more tangible than ever. Here, a comprehensive assistive magnetic skin system is presented that allows quadriplegics, including the severely injured ones, to move around individually and control their surroundings with ease. The system tracks facial expressions by tracking the movement of magnetic tattoos attached to the face, using magnetic field sensors incorporated into eyeglasses. The magnetic tattoos are made of highly flexible, stretchable, breathable, and biocompatible magnetic skins. In combination with smart-glasses, smart-wheelchair, and smart-gadgets, the users can move around and control their environment with their facial expressions. The system is also designed to allow quadriplegics to perform outdoor activities effortlessly. It supports line-of-sight communication and does not require pre-tethering to the smart-gadgets, unlike the existing solutions. Thus, enabling the user to walk on pathways, activate pedestrian lights, control public elevators, and perform various outdoor activities independently.
  • Towards Hardware Optimal Neural Network Selection with Multi-Objective Genetic Search

    Krestinskaya, O.; Salama, Khaled N.; James, A. P. (IEEE, 2020-09-29) [Conference Paper]
    The selection of hyperparameters and circuit components for optimum hardware implementation of a neural network is a challenging task, which has not been automated yet. This work proposes the method for the selection of optimum neural network architecture and hyperparameters using genetic algorithm based on the hardware-related performance metrics, such an on-chip area, power consumption, processing time and robustness to hardware non-idealities, and focus on memristor-based analog network architecture. The experimental results show that the proposed approach allows to select the optimum architecture based on the designers' preferences.
  • Rail-to-rail complementary input StrongARM comparator for low-power applications

    Al-Qadasi, Mohammed A.; Alshehri, Abdullah; Alturki, Abdullah; Almansouri, Abdullah S.; Salama, Khaled N.; Fariborzi, Hossein; Al Attar, Talal (IET Circuits, Devices and Systems, Institution of Engineering and Technology (IET), 2020-09-10) [Article]
    This study proposes a new scheme for rail-to-rail input StrongARM latch comparator. Additional differential input p-type metal-oxide-semiconductor (PMOS) and cross-coupled n-type metal-oxide-semiconductor (NMOS) transistors have been introduced to achieve the rail-to-rail input range. The proposed scheme offers low-energy consumption of 15.2 fJ and a high speed of 3 GHz, which makes it attractive for energy harvested Internet of Thing applications. The proposed architecture is fabricated in 65 nm complementary metal-oxide-semiconductor (CMOS) technology and the functionality is verified using post-layout simulations and chip measurements.
  • Semi-transparent graphite films growth on Ni and their double-sided polymer-free transfer.

    Deokar, Geetanjali Baliram; Genovese, Alessandro; Surya, Sandeep Goud; Long, Chen; Salama, Khaled N.; Da Costa, Pedro M. F. J. (Scientific reports, Springer Science and Business Media LLC, 2020-09-07) [Article]
    Nanorange thickness graphite films (NGFs) are robust nanomaterials that can be produced via catalytic chemical vapour deposition but questions remain regarding their facile transfer and how surface topography may affect their application in next-generation devices. Here, we report the growth of NGFs (with an area of 55 cm2 and thickness of ~ 100 nm) on both sides of a polycrystalline Ni foil and their polymer-free transfer (front- and back-side, in areas up to 6 cm2). Due to the catalyst foil topography, the two carbon films differed in physical properties and other characteristics such as surface roughness. We demonstrate that the coarser back-side NGF is well-suited for NO2 sensing, whereas the smoother and more electrically conductive front-side NGF (2000 S/cm, sheet resistance - 50 Ω/sq) could be a viable conducting channel or counter electrode in solar cells (as it transmits 62% of visible light). Overall, the growth and transfer processes described could help realizing NGFs as an alternative carbon material for those technological applications where graphene and micrometer-thick graphite films are not an option.
  • Numerical modeling for terahertz testing of non-metallic pipes

    Farhat, Mohamed; Amer, A. M.; Cunningham, V. B.; Salama, Khaled N. (AIP Advances, AIP Publishing, 2020-09-04) [Article]
    In the oil and gas industry, safety and operational efficiency at production sites are of paramount importance. A reliable non-destructive testing technology for non-metallic pipes has a high potential financial impact, since it may facilitate the replacement of metallic pipes with non-metallic ones. This article features a perspective and future trends in the field of terahertz sensing technology. Importantly, several numerical simulations that illustrate many exciting potential applications for this emerging technology are described. These range from underground detection of spilt liquids and the content of pipes to the detection of cracks in plastic pipes using both frequency-domain and time-domain finite-element simulations.
  • Electrochemical sensors and biosensors using laser-derived graphene: A comprehensive review

    Ait Lahcen, Abdellatif; Rauf, Sakandar; Beduk, Tutku; Durmus, Ceren; Aljedaibi, Abdulrahman; Timur, Suna; Alshareef, Husam N.; Amine, Aziz; Wolfbeis, Otto S.; Salama, Khaled N. (Biosensors and Bioelectronics, Elsevier BV, 2020-08-27) [Article]
    Laser-derived graphene (LDG) technology is gaining attention as a promising material for the development of novel electrochemical sensors and biosensors. Compared to established methods for graphene synthesis, LDG provides many advantages such as cost-effectiveness, fast electron mobility, mask-free, green synthesis, good electrical conductivity, porosity, mechanical stability, and large surface area. This review discusses, in a critical way, recent advancements in this field. First, we focused on the fabrication and doping of LDG platforms using different strategies. Next, the techniques for the modification of LDG sensors using nanomaterials, conducting polymers, biological and artificial receptors are presented. We then discussed the advances achieved for various LDG sensing and biosensing schemes and their applications in the fields of environmental monitoring, food safety, and clinical diagnosis. Finally, the drawbacks and limitations of LDG based electrochemical biosensors are addressed, and future trends are also highlighted.
  • Laser scribed graphene: A novel platform for highly sensitive detection of eletroactive biomolecules

    Ghanam, Abdelghani; Ait Lahcen, Abdellatif; Beduk, Tutku; Alshareef, Husam N.; Amine, Aziz; Salama, Khaled N. (Biosensors and Bioelectronics, Elsevier BV, 2020-08-19) [Article]
    Laser-scribed graphene electrodes (LSGEs) have recently shown a potential for the development of electrochemical biosensors thanks to their electronic properties, porous structures, and large surface area that can support the charge transfer. In this paper, the authors present a comparative study of the electrochemical performances of LSGEs with the conventional screen-printed carbon electrodes (SPCEs) toward the detection of most commonly used phenolic compounds and biomolecules. Cyclic voltammetry measurements showed a significant enhancement in the electron transfer rate of all tested electroactive species at LSGEs compared to conventional SPCE. We have suggested, for the first time, a mechanistic study for catecholamine redox reactions at LSGE as the electron transfer–chemical reaction–electron transfer mechanism. Moreover, the excellent performances of LSGE were observed in terms of the electrocatalytic detection of paracetamol (PCM). Therefore, the second part of this study compared the analytical performances of LSGE and SPCE with respect to the detection of PCM. The LSGE allows a fast and reversible system for PCM with a low ΔEp of 88 mV while the SPCE exhibits a quasi-reversible system with a higher ΔEp of 384 mV. The LSGE demonstrated a PCM linear range of concentration between 0.1 μM and 10 μM, with a detection limit of 31 nM. In addition, the LSGE showed a successful applicability with good selectivity and sensitivity for PCM determination in real samples of pharmaceutical tablets. Hence, LSGEs could be an excellent platform for simple and low-cost electrochemical biosensor applications.
  • Multi-source ambient energy harvester based on RF and thermal energy: Design, testing, and IoT application

    Bakytbekov, Azamat; Nguyen, Thang Q.; Li, Weiwei; Lee Cottrill, Anton; Zhang, Ge; Strano, Michael S.; Salama, Khaled N.; Shamim, Atif (Energy Science & Engineering, Wiley, 2020-07-28) [Article]
    Billions of wireless sensing devices must be powered for IoT applications. Collecting energy from the ambient environment to power sensor nodes is a promising solution. Solar energy has been one of the main sources of ambient energy due to its availability, higher power density, and the maturity of the solar photovoltaic industry. However, there are many scenarios (indoor environment, outdoor environment during nighttime, poor weather conditions, underground, etc) where ambient solar energy is either not available or not sufficient for practical applications. For such scenarios, other renewable sources of energy must be sought. Typically, not enough power is collected from one ambient source to charge sensor nodes for continuous operation. In this work, we present a multi-source energy harvester that collects RF and thermal energy (both available 24 hours) from the ambient environment simultaneously. The RF energy harvester is multi-band and collects power from GSM (900, 1800 MHz) and 3G (2100 MHz). The thermal harvester converts diurnal temperature fluctuations to electrical energy using high thermal effusivity phase change material. Extensive field testing has been performed in three different conditions—outdoors, indoors, and buried underground—to highlight the usefulness of the multi-source energy harvester in all these environments. When one source is disabled, the harvester still generates energy from the remaining active source and can enable continuous operation of futuristic IoT sensors. As a proof of concept, a real-world IoT application is demonstrated, where temperature and humidity sensors are powered by the multi-source energy harvester. Continuous robust operation of the sensors and wireless data transmission after each 3.7 seconds are expected when both harvesters operate in full mode. Scenarios, where only single thermal energy harvester or only single RF energy harvester operates, are also demonstrated and data transmission with average time intervals of 30 seconds and 9 minutes is achieved, respectively.
  • An in-field integrated capacitive sensor for rapid detection and quantification of soil moisture

    Surya, Sandeep Goud; Yuvaraja, Saravanan; Varrla, Eswaraiah; Baghini, Maryam Shojaei; Palaparthy, Vinay S.; Salama, Khaled N. (Sensors and Actuators B: Chemical, Elsevier BV, 2020-06-29) [Article]
    The development of in-situ soil moisture sensors (SMS) with advanced materials is the requirement of the future autonomous agriculture industry. However, an open challenge for these sensors is to control changes in the capacitance rather than resistance while attaining reliability, high performance, scalability and stability. In this work, a series of materials such as Graphite oxide (GO), Molybdenum disulfide (MoS2), Vanadium oxide (V2O5), and Molybdenum oxide (MoO3) are tested in realizing a receptor layer that can efficiently sense soil moisture. Here, we found that MoS2 offers the sensitivity, which is nearly three times higher (1200 pF) than in the case of V2O5 for any given range of soil-moisture content outperforming both GO and MoO3 materials. The corresponding increase in the sensitivities for MoO3, GO, MoS2, and V2O5 are ∼13%, ∼11%, ∼30%, and ∼9% respectively, for a variety of temperature up to 45 °C. A temperature variation of 25 °C to 50 °C showed a minimal increase in the sensitivity response for all the devices. We further demonstrated a record sensitivity of 540% with MoS2 in black soil and the corresponding response time was 65 sec. Finally, the recovery time for the MoS2 sensor is 27 s, which is quite fast.
  • Towards Efficient Neuromorphic Hardware: Unsupervised Adaptive Neuron Pruning

    Guo, Wenzhe; Yantir, Hasan Erdem; Fouda, Mohamed E.; Eltawil, Ahmed; Salama, Khaled N. (Electronics, MDPI AG, 2020-06-27) [Article]
    To solve real-time challenges, neuromorphic systems generally require deep and complex network structures. Thus, it is crucial to search for effective solutions that can reduce network complexity, improve energy efficiency, and maintain high accuracy. To this end, we propose unsupervised pruning strategies that are focused on pruning neurons while training in spiking neural networks (SNNs) by utilizing network dynamics. The importance of neurons is determined by the fact that neurons that fire more spikes contribute more to network performance. Based on these criteria, we demonstrate that pruning with an adaptive spike count threshold provides a simple and effective approach that can reduce network size significantly and maintain high classification accuracy. The online adaptive pruning shows potential for developing energy-efficient training techniques due to less memory access and less weight-update computation. Furthermore, a parallel digital implementation scheme is proposed to implement spiking neural networks (SNNs) on field programmable gate array (FPGA). Notably, our proposed pruning strategies preserve the dense format of weight matrices, so the implementation architecture remains the same after network compression. The adaptive pruning strategy enables 2.3× reduction in memory size and 2.8× improvement on energy efficiency when 400 neurons are pruned from an 800-neuron network, while the loss of classification accuracy is 1.69%. And the best choice of pruning percentage depends on the trade-off among accuracy, memory, and energy. Therefore, this work offers a promising solution for effective network compression and energy-efficient hardware implementation of neuromorphic systems in real-time applications.
  • Efficient Acceleration of Stencil Applications through In-Memory Computing

    Yantir, Hasan Erdem; Eltawil, Ahmed; Salama, Khaled N. (Micromachines, MDPI AG, 2020-06-26) [Article]
    The traditional computer architectures severely suffer from the bottleneck between the processing elements and memory that is the biggest barrier in front of their scalability. Nevertheless, the amount of data that applications need to process is increasing rapidly, especially after the era of big data and artificial intelligence. This fact forces new constraints in computer architecture design towards more data-centric principles. Therefore, new paradigms such as in-memory and near-memory processors have begun to emerge to counteract the memory bottleneck by bringing memory closer to computation or integrating them. Associative processors are a promising candidate for in-memory computation, which combines the processor and memory in the same location to alleviate the memory bottleneck. One of the applications that need iterative processing of a huge amount of data is stencil codes. Considering this feature, associative processors can provide a paramount advantage for stencil codes. For demonstration, two in-memory associative processor architectures for 2D stencil codes are proposed, implemented by both emerging memristor and traditional SRAM technologies. The proposed architecture achieves a promising efficiency for a variety of stencil applications and thus proves its applicability for scientific stencil computing.
  • Automating Analogue AI Chip Design with Genetic Search

    Krestinskaya, Olga; Salama, Khaled N.; James, Alex P. (Advanced Intelligent Systems, Wiley, 2020-06-25) [Article]
    Optimization of analogue neural circuit designs is one of the most challenging, complicated, time-consuming, and expensive tasks. Design automation of analogue neuromemristive chips is made difficult by the need to design chips at low cost, ease of scaling, high-energy efficiency, and small on-chip area. The rapid progress in edge AI computing applications generates high demand for developing smart sensors. The integration of high-density analogue computing AI chips as coprocessing units to sensors is gaining popularity. This article proposes a hardware–software codesign framework to speed up and automate the design of analogue neuromemristive chips. This work uses genetic algorithms with objective functions that take into account hardware nonidealities such as limited precision of devices, the device-to-device variability, and device failures. The optimized neural architectures and hyperparameters successfully map with the library of relevant neuromemristive analogue hardware blocks. The results demonstrate the advantage of proposed automation to speed up the analogue circuit design of large-scale neuromemristive networks and reduce overall design costs for AI chips.
  • A Highly Selective Metal-Organic Framework Textile Humidity Sensor

    Rauf, Sakandar; Vijjapu, Mani Teja; Andres, Miguel Angel; Gascón, Ignacio; Roubeau, Olivier; Eddaoudi, Mohamed; Salama, Khaled N. (ACS Applied Materials & Interfaces, American Chemical Society (ACS), 2020-06-19) [Article]
    The increase in demand and popularity of smart textiles brings new and innovative ideas to develop a diverse range of textile-based devices for our daily life applications. Smart textile-based sensors (TEX sensors) become attractive due to the potential to replace current solid-state sensor devices with flexible and wearable devices. We have developed a smart textile sensor for humidity detection using a metal-organic framework (MOF) as an active thin-film layer. We show for the first time, the use of the Langmuir-Blodgett (LB) technique for the deposition of a MIL-96(Al) MOF thin film directly onto the fabrics containing interdigitated textile electrodes for the fabrication of a highly selective humidity sensor. The humidity sensors were made from two different types of textiles, namely, linen and cotton, with the linen based sensor giving the best response due to better coverage of MOF. The TEX sensor showed a reproducible response after multiple cycles of measurements. After three weeks of storage, the sensor showed a moderate decrease in response. Moreover, TEX sensors showed a high level of selectivity for the detection of water vapors in the presence of several volatile organic compounds (VOCs). Interestingly, the selectivity is superior to some of the previously reported MOF coated solid-state interdigitated electrode devices and textile sensors. The method herein described is generic and can be extended to other textiles and coating materials for the detection of toxic gases and vapors.
  • Fully Inkjet-Printed, Ultrathin and Conformable Organic Photovoltaics as Power Source Based on Cross-Linked PEDOT:PSS Electrodes

    Bihar, Eloise; Corzo Diaz, Daniel Alejandro; Hidalgo, Tania C.; Rosas Villalva, Diego; Salama, Khaled N.; Inal, Sahika; Baran, Derya (Advanced Materials Technologies, Wiley, 2020-06-14) [Article]
    Ultra-lightweight solar cells have attracted enormous attention due to their ultra-conformability, flexibility, and compatibility with applications including electronic skin or miniaturized electronics for biological applications. With the latest advancements in printing technologies, printing ultrathin electronics is becoming now a reality. This work offers an easy path to fabricate indium tin oxide (ITO)-free ultra-lightweight organic solar cells through inkjet-printing while preserving high efficiencies. A method consisting of the modification of a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) ink with a methoxysilane-based cross-linker (3-glycidyloxypropyl)trimethoxysilane (GOPS)) is presented to chemically modify the structure of the electrode layer. Combined with plasma and solvent post-treatments, this approach prevents shunts and ensures precise patterning of solar cells. By using poly(3-hexylthiophene) along rhodanine-benzothiadiazole-coupled indacenodithiophene (P3HT:O-IDTBR), the power conversion efficiency (PCE) of the fully printed solar cells is boosted up to 4.73% and fill factors approaching 65%. All inkjet-printed ultrathin solar cells on a 1.7 µm thick biocompatible parylene substrate are fabricated with PCE reaching up to 3.6% and high power-per-weight values of 6.3 W g−1. After encapsulation, the cells retain their performance after being exposed for 6 h to aqueous environments such as water, seawater, or phosphate buffered saline, paving the way for their integration in more complex circuits for biological systems.
  • Simplified Modal-Cancellation Approach for Substrate-Integrated-Waveguide Narrow-Band Filter Design

    Celis Sierra, Sebastian; Farhat, Mohamed; Almansouri, Abdullah S.; Bagci, Hakan; Salama, Khaled N. (Electronics, MDPI AG, 2020-06-09) [Article]
    Current substrate-integrated-waveguide (SIW) filter design methodologies can be extremely computational and time-inefficient when a narrow-band filter is required. A new approach to designing compact, highly selective narrow-band filters based on smartly positioned obstacles is thus presented here. The proposed modal-cancellation approach is achieved by translating or eliminating undesired modes within the frequency of interest. This is performed by introducing smartly located obstacles in the maxima and nulls of the modes of interest. This approach is different from the traditional inverter technique, where a periodic number of inductive irises are coupled in a ladder configuration to implement the desired response of an nth-order filter, and significantly reduces the complexity of the resulting filter structure. Indeed, the proposed method may be used to design different filters for several frequency bands and various applications. The methodology was experimentally verified through fabricated prototypes.
  • A Compact, Passive Frequency-Hopping Harmonic Sensor Based on a Microfluidic Reconfigurable Dual-Band Antenna

    Zhu, Liang; Farhat, Mohamed; Chen, Yi-Chao; Salama, Khaled N.; Chen, Pai-Yen (IEEE Sensors Journal, IEEE, 2020-06-08) [Article]
    We propose here a fully-passive wireless liquid sensor using a harmonic transponder, which comprises a dual-band microstrip antenna reconfigured by different types of liquids injected in a fluidic cavity. Different from traditional radio-frequency (RF) backscatter sensors, the proposed harmonic-transponder sensor (or harmonic sensor) receives frequency-hopped RF monotones and backscatters their second harmonics, with the peak frequency shifted by dielectric properties of liquid mixtures. This microstrip antenna has a hybrid-feed structure, of which an outer split-ring patch exhibits a narrow-band TM310 mode at the fundamental frequency (f0) and an inner elliptical patch displays a wideband resonance centered at the second-harmonic frequency (2f0), achieved with hybridization of TMe110 and TMo110 modes. In particular, the outer split-ring patch is loaded with a fluidic channel system to tune the resonance frequency of the TM310 mode (f0). We demonstrate that the type of liquid mixture filling in the fluidic cavity can be clearly perceived by reading the peak received signal strength indicator (RSSI) in the spectrum of second harmonics. Our results show the potential for deploying this passive wireless sensor in noisy environments that include clutters, multiple reflections, jamming, and crosstalks.
  • Room Temperature Operable Semiconducting Metal Oxide Chemi-Resistive NO2 Gas Sensor

    Vijjapu, Mani Teja; Surya, Sandeep Goud; Yuvaraja, Saravanan; Salama, Khaled N. (ECS Meeting Abstracts, The Electrochemical Society, 2020-05-12) [Abstract]
    The quest for the room temperature operable semiconducting metal oxide gas sensors has come to an end. We fabricated an InGaZnO based chemi-resistive gas sensor, which is remarkably sensitive and selective to NO2 gas. Conventional semiconducting metal oxide gas sensors are active at high temperatures or in presence of light, which makes them power-hungry. The fabricated sensor is room temperature operable and requires light only to regenerate the device after exposure. NO2 has adverse effects on human health at concentration as low as 2 ppm. The measured limit of detection of the sensor is 100 ppb. Comprehensive NO2 adsorption studies were performed using kelvin probe force microscopy (KPFM) and X-ray photoelectron spectroscopy (XPS). The detailed mechanism of sensing and reviving is proposed. The fabricated sensor is compatible with CMOS process and can be integrated with the CMOS circuitry to make compact sensing system.
  • EANN: Energy Adaptive Neural Networks

    Hassan, Salma; Attia, Sameh; Salama, Khaled N.; Mostafa, Hassan (Electronics, MDPI AG, 2020-05-04) [Article]
    This paper proposes an Energy Adaptive Feedforward Neural Network (EANN). It uses multiple approximation techniques in the hardware implementation of the neuron unit. The used techniques are precision scaling, approximate multiplier, computation skipping, neuron skipping, activation function approximation and truncated accumulation. The proposed EANN system applies the partial dynamic reconfiguration (PDR) feature supported by the FPGA platform to reconfigure the hardware elements of the neural network based on the energy budget. The PDR technique enables the EANN system to remain functioning when the available energy budget is reduced by factors of 46.2% to 79.8% of the total energy of the unapproximated neural network. Unlike the conventional operation that only uses certain amount of energy and cannot function properly if the energy budget falls below that energy level, the EANN system remains functioning for longer time after energy drop at the expense of less accuracy. The proposed EANN system is highly recommended in limited-energy applications as it adapts the hardware units to the degraded energy at the expense of some accuracy loss.
  • Fully Integrated Organic Field-Effect Transistor Platform to Detect and to Quantify NO 2 Gas

    Yuvaraja, Saravanan; Surya, Sandeep Goud; Vijjapu, Mani Teja; Chernikova, Valeriya; Shekhah, Osama; Eddaoudi, Mohamed; Salama, Khaled N. (physica status solidi (RRL) – Rapid Research Letters, Wiley, 2020-05-04) [Article]
    Herein, the gas sensing characteristics of PDVT-10 organic field-effect tran sistor(OFET) devices are explored and integrated to build a compact analog-to-digitalconverter (ADC) gas detection system. The electrical characteristics of thepristine PDVT-10 OFET exhibitIon/Ioffratio and threshold voltage as 104and12 V, respectively. Through the coatin g of a metal-organic framework (MOF) onthe surface of PDVT-10, theIon/Ioffimproves by one order of magnitudeaccompanied by significant positive threshold shift measured around 4 V. MOF isadded as a pre-concentrating material, and the device exhibits excellent selectivityand good sensitivity toward the target NO2gas. Most of the reports available inthe literature generally focus on the gas sensing performance of individualsensors, which does not have the potential to solve real-time sensing problems.Hence, for the first time, a fully integrated ADC system is designed with just twoOFETs. The integrated ADC system not only detects the NO2gas with highsensitivity and selectivity but also generates a 5 bit digital output correspondingto different NO2gas concentrations from 25 ppb to 1 ppm.
  • Spectrometer-Free Graphene Plasmonics Based Refractive Index Sensor

    Zhang, Li; Farhat, Mohamed; Salama, Khaled N. (Sensors, MDPI AG, 2020-04-20) [Article]
    We propose a spectrometer-free refractive index sensor based on a graphene plasmonic structure. The spectrometer-free feature of the device is realized thanks to the dynamic tunability of graphene’s chemical potential, through electrostatic biasing. The proposed sensor exhibits a 1566 nm/RIU sensitivity, a 250.6 RIU−1 figure of merit in the optical mode of operation and a 713.2 meV/RIU sensitivity, a 246.8 RIU−1 figure of merit in the electrical mode of operation. This performance outlines the optimized operation of this spectrometer-free sensor that simplifies its design and can bring terahertz sensing one step closer to its practical realization, with promising applications in biosensing and/or gas sensing.

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