A spin wave driven skyrmion-based diode on a T-shaped nanotrack.

The propagation of spin waves is one of the promising ways to design nanoscale spintronic devices. The spin waves can interact with the magnetic skyrmion, a particle-like object that is topologically stabilized by Dzyaloshinskii-Moriya interaction (DMI) in thin film heterostructures. In this work, a spin wave-driven skyrmion-based diode is proposed by employing a T-shaped ferromagnetic nanotrack. The one-way motion of the skyrmion is achieved by exploiting the mid-arm at the center of the nanotrack. This prevents the reverse motion of the skyrmion owing to the skyrmion Hall effect (SkHE) and the absence of a repulsive force from the far edge in the mid-arm region. In order to facilitate the diode functionality of the spin wave-driven skyrmion, the amplitude and frequency of the excitation field should be considered in the ranges 0.07 T ≤ H0 ≤ 0.4 T and 60 GHz ≤ f ≤ 80 GHz, respectively. The micromagnetic interaction energy between the edges and the spin wave-driven skyrmion creates a potential gradient that induces the force which is responsible for the longitudinal motion of the skyrmion. The suggested spin wave driven diode exhibits a processing speed on the order of 100 m s-1 at 60 GHz frequency and 0.4 T amplitude. Hence, this device paves the way for the development of complete non-charge based magnetic devices for various spintronic applications.

Quantized Magnetic Domain Wall Synapse for Efficient Deep Neural Networks.

The quantization of synaptic weights using emerging nonvolatile memory (NVM) devices has emerged as a promising solution to implement computationally efficient neural networks on resource constrained hardware. However, the practical implementation of such synaptic weights is hampered by the imperfect memory characteristics, specifically the availability of limited number of quantized states and the presence of large intrinsic device variation and stochasticity involved in writing the synaptic states. This article presents on-chip training and inference of a neural network using quantized magnetic domain wall (DW)-based synaptic array and CMOS peripheral circuits. A rigorous model of the magnetic DW device considering stochasticity and process variations has been utilized for the synapse. To achieve stable quantized weights, DW pinning has been achieved by means of physical constrictions. Finally, VGG8 architecture for CIFAR-10 image classification has been simulated by using the extracted synaptic device characteristics. The performance in terms of accuracy, energy, latency, and area consumption has been evaluated while considering the process variations and nonidealities in the DW device as well as the peripheral circuits. The proposed quantized neural network (QNN) architecture achieves efficient on-chip learning with 92.4% and 90.4% training and inference accuracy, respectively. In comparison to pure CMOS-based design, it demonstrates an overall improvement in area, energy, and latency by 13.8 × , 9.6 × , and 3.5 × , respectively.

Skyrmion motion under temperature gradient and application in logic devices.

Under the presence of temperature gradient (TG) on a nanotrack, it is necessary to investigate the skyrmion dynamics in various magnetic systems under the combined effect of forces due to magnonic spin transfer torque(µSTT),thermal STT (τSTT), entropic difference(dS),as well as thermal induced dipolar field (DF). Hence, in this work, the dynamics of skyrmions in ferromagnets (FM), synthetic antiferromagnets (SAF), and antiferromagnets (AFM) have been studied under different TGs and damping constants (αG). It is observed thatαGplays a major role in deciding the direction of skyrmion motion either towards the hotter or colder side in different magnetic structures. Later, FM skyrmion based logic device is proposed that consists of a cross-coupled nanotrack, where the skyrmions on horizontal and vertical nanotrack are controlled by exploiting TG and electrical STT (eSTT), respectively by taking the advantages of thermal induced skyrmion Hall effect (SkHE). The proposed device performs AND and OR logic functionalities simultaneously, when the applied current density is2×1011Am-2.Moreover, the proposed device is also able to exhibit the half adder functionality by tuning the applied current density to3×1011Am-2.The total energy consumption for AND and OR logic operation and half adder are 33.63 fJ and 25.06 fJ, respectively. This paves the way for the development of energy-efficient logic devices with ultra-high storage density.

Spin device-based image edge detection architecture for neuromorphic computing.

Artificial intelligence and deep learning today are utilized for several applications namely image processing, smart surveillance, edge computing, and so on. The hardware implementation of such applications has been a matter of concern due to huge area and energy requirements. The concept of computing in-memory and the use of non-volatile memory (NVM) devices have paved a path for resource-efficient hardware implementation. We propose a dual-level spin-orbit torque magnetic random-access memory (SOT-DLC MRAM) based crossbar array design for image edge detection. The presented in-memory edge detection algorithm framework provides spin-based crossbar designs that can intrinsically perform image edge detection in an energy-efficient manner. The simulation results are scaled down in energy consumption for data transfer by a factor of 8x for grayscale images with a comparatively smaller crossbar than an equivalent CMOS design. DLC SOT-MRAM outperforms CMOS-based hardware implementation in several key aspects, offering 1.53x greater area efficiency, 14.24x lower leakage power dissipation, and 3.63x improved energy efficiency. Additionally, when compared to conventional spin transfer torque (STT-MRAM and SOT-MRAM, SOT-DLC MRAM achieves higher energy efficiency with a 1.07x and 1.03x advantage, respectively. Further, we extended the image edge extraction framework to spiking domain where ant colony optimization (ACO) algorithm is implemented. The mathematical analysis is presented for mapping of conductance matrix of the crossbar during edge detection with an improved area and energy efficiency at hardware implementation. The pixel accuracy of edge-detected image from ACO is 4.9% and 3.72% higher than conventional Sobel and Canny based edge-detection.

Nitrogen-doped zinc oxide nanoribbons for potential resonant tunneling diode applications.

Armchair ZnONRs doped with nitrogen are investigated in the current manuscript for possible applications based on negative differential resistance (NDR). To conduct the theoretical research, we use density functional theory (DFT) in conjunction with the non-equilibrium Green's function (NEGF) formalism to carry out first principles computations. Pristine ZnONR (P-ZnONRs) is a semiconductor with a wide energy bandgap (Eg) of 2.53 eV. However, one edge N-doped ZnONRs (SN-ZnO) and both edge N-doped ZnONRs (DN-ZnO) are metallic. Partial density of states (PDOS) reveals that the metallicity is caused by the doped nitrogen atom. The transport characteristics analysis revealed the negative differential resistance (NDR) characteristics in the N-doped ZnONRs. The peak-to-valley current ratios (PVCR) are computed and measured to be 4.58 × 1021 and 1.83 × 1022 for SN-ZnO and DN-ZnO, respectively. The obtained findings suggest the significant potential of armchair ZnONRs for NDR-based applications such as switches, rectifiers, oscillators, memory devices, etc.

Hydrogenated cove-edge aluminum nitride nanoribbons for ultrascaled resonant tunneling diode applications: a computational DFT study.

While synthesizing quasi-one-dimensional nanoribbons, there is a finite probability that edges have cove-edge defects. This paper focuses on the structural, electronic, and transport properties of cove-edge aluminum nitride nanoribbons (AlNNR) using density functional theory and the non-equilibrium Green's function (NEGF) method. The cove-edge AlNNRs are thermodynamically stable and exhibit metallic behavior. Interestingly, the calculated current-voltage characteristics of the cove-edge AlNNR-based nanodevices show negative differential resistance (NDR). The H-AlN-Cove nanodevice exhibits high peak-to-valley current ratio (PVCR) of the order of 107. The calculated PVCR of the H-AlN-Cove nanodevice is 106times higher than that of the silicene nanoribbon (SiNR) and graphene nanoribbon (GNR), and 104times higher than that of the phosphorene nanoribbon (PNR) and arsenene nanoribbons (ANR)-based devices respectively. The NDR feature with high PVCR provides a prospect for the cove-edge AlNNR in nanodevice applications.

Antiferromagnetic skyrmion-based high speed diode.

Antiferromagnetic (AFM) skyrmions are favored over ferromagnetic (FM) skyrmions as they can be driven parallel to in-plane driving currents and eventually prevent the annihilation at the edges of nanotrack. In this study, an AFM skyrmion-based diode is proposed to realize the one-way skyrmion motion that is crucial for data processing in nanoelectronic and spintronic devices. The skyrmion transport is controlled by exploiting the staircase notch region in the middle of the nanotrack. By virtue of this, the micromagnetic interaction energy between the skyrmion and the notch edges generates a potential gradient that further gives rise to repulsive forces on the skyrmion. The resultant of the forces from the driving current and edge repulsions make the skyrmion move along the notch region to overcome the device window and reach the detection region. The notch is designed in such a way that it prevents the movement of the skyrmion in the reverse direction, thereby achieving diode functionality. The proposed device offers processing speed in the order of 103 m s-1, hence paving the way for the development of energy-efficient and high-speed devices in antiferromagnetic spintronics.

Novel Wearable Optical Sensors for Vital Health Monitoring Systems-A Review.

Wearable sensors are pioneering devices to monitor health issues that allow the constant monitoring of physical and biological parameters. The immunity towards electromagnetic interference, miniaturization, detection of nano-volumes, integration with fiber, high sensitivity, low cost, usable in harsh environments and corrosion-resistant have made optical wearable sensor an emerging sensing technology in the recent year. This review presents the progress made in the development of novel wearable optical sensors for vital health monitoring systems. The details of different substrates, sensing platforms, and biofluids used for the detection of target molecules are discussed in detail. Wearable technologies could increase the quality of health monitoring systems at a nominal cost and enable continuous and early disease diagnosis. Various optical sensing principles, including surface-enhanced Raman scattering, colorimetric, fluorescence, plasmonic, photoplethysmography, and interferometric-based sensors, are discussed in detail for health monitoring applications. The performance of optical wearable sensors utilizing two-dimensional materials is also discussed. Future challenges associated with the development of optical wearable sensors for point-of-care applications and clinical diagnosis have been thoroughly discussed.

Cardiac Troponin I Detection Using Gold/Cerium-Oxide Nanoparticles Assisted Hetro-Core Fiber Structure.

The article describes the development of a hetro-core optical fiber sensor structure based on localized surface plasmon resonance (LSPR) for the detection of cardiac troponin I (cTnI) solution. This was accomplished by fabricating a single-mode fiber - multimode fiber - single-mode fiber (SMS) structure. Then, fiber structure is immobilized with gold nanoparticles (AuNPs) and cerium oxide nanoparticles (CeO2-NPs) to improve its sensing capabilities. An UV-Vis spectrophotometer and a high-resolution transmission electron microscope (HR-TEM) are used to determine the morphology of synthesized nanoparticles. Scanning electron microscopy (SEM) is used to examine the state of immobilized NPs on the surface of sensing region. The developed sensor probe has a linear range of 0 to 1000 ng/mL cTnI, a sensitivity of 3 pm/(ng/mL), and a limit of detection (LoD) of 108.15 ng/mL. In real time, the proposed sensor will be used in a practice to detect acute myocardial infarction (AMI).

A Plus Shaped Cavity in Optical Fiber Based Refractive Index Sensor.

The optical fiber grating sensors have strong potential for the detection of biological samples. However, a careful effort is still in demand to enhance the performance of existing grating sensors especially in biological sensing. Therefore, in this work, we have introduced a novel plus shaped cavity (PSC) in optical fiber model and used it for the detection of haemoglobin (Hb) refractive index (RI). The numerical analysis of designed model is done by the testing of single and double vertical slots cavity in optical fiber core structure. The testing of designed sensor model is done at the wavelength of 800 nm at which the RI of oxygenated and deoxygenated Hb is 1.392 and 1.389, respectively. The analysis of reported PSC sensor model is done in the wide range of Hb RI from 1.333 to 1.392. The tested range of RI corresponds to the Hb concentration from 0 to 140 gl-1. The obtained results states that for the tested range of RI, the autocorrelation coefficient of R2 = 99.51 % is achieved. The analysis of projected work is done by using finite difference time domain (FDTD) method. The introduction of PSC can increase in sensitivity. In proposed PSC, the length and width of created slots are [Formula: see text] and [Formula: see text], respectively, which is quite enough to observe the response of analytes RI. This can minimize the creation of multiple gratings required for observing the analyte response.

Recent advancements in optical biosensors for cancer detection.

Optical biosensors are rapid, real-time, and portable, have a low detection limit and a high sensitivity, and have a great potential for diagnosing various types of cancer. Optical biosensors can detect cancer in a few million malignant cells, in comparison to conventional diagnosis techniques that use 1 billion cells in tumor tissue with a diameter of 7 nm-10 nm. Current cancer detection methods are also costly, inconvenient, complex, time consuming, and require technical specialists. This review focuses on recent advances in optical biosensors for early detection of cancer. It is primarily concerned with advancements in the design of various biosensors using resonance, scattering, chemiluminescence, luminescence, interference, fluorescence, absorbance or reflectance, and various fiber types. The development of various two-dimensional materials with optical properties such as biocompatibility, field enhancement, and a higher surface-to-volume ratio, as well as advancements in microfabrication technologies, have accelerated the development of optical sensors for early detection of cancer and other diseases. Surface enhanced Raman spectroscopy technology has the potential to detect a single molecule with high specificity, and terahertz waves are a recently explored technology for cancer detection. Due to the low electromagnetic interference, small size, multiplexing, and remote sensing capabilities of optical fiber-based platforms, they may be a driving force behind the rapid development of biosensors. The advantages and disadvantages of existing and future optical biosensor designs for cancer detection are discussed in detail. Additionally, a prospect for future advancements in the development of optical biosensors for point-of-care and clinical applications is highlighted.

Water Pollutants p-Cresol Detection Based on Au-ZnO Nanoparticles Modified Tapered Optical Fiber.

In this work, a localized plasmon-based sensor is developed for para-cresol (p-cresol) - a water pollutant detection. A nonadiabatic [Formula: see text] of tapered optical fiber (TOF) has been experimentally fabricated and computationally analyzed using beam propagation method. For optimization of sensor's performance, two probes are proposed, where probe 1 is immobilized with gold nanoparticles (AuNPs) and probe 2 is immobilized with the AuNPs along with zinc oxide nanoparticles (ZnO-NPs). The synthesized metal nanomaterials were characterized by ultraviolet-visible spectrophotometer (UV-vis spectrophotometer) and transmission electron microscope (HR-TEM). The nanomaterials coating on the surface of the sensing probe were characterized by a scanning electron microscope (SEM). Thereafter, to increase the specificity of the sensor, the probes are functionalized with tyrosinase enzyme. Different solutions of p-cresol in the concentration range of [Formula: see text] - [Formula: see text] are prepared in an artificial urine solution for sensing purposes. Different analytes such as uric acid, ß -cyclodextrin, L-alanine, and glycine are prepared for selectivity measurement. The linearity range, sensitivity, and limit of detection (LOD) of probe 1 are [Formula: see text] - [Formula: see text], 7.2 nm/mM (accuracy 0.977), and [Formula: see text], respectively; and for probe 2 are [Formula: see text] - [Formula: see text], 5.6 nm/mM (accuracy 0.981), and [Formula: see text], respectively. Thus, the overall performance of probe 2 is quite better due to the inclusion of ZnO-NPs that increase the biocompatibility of sensor probe. The proposed sensor structure has potential applications in the food industry and clinical medicine.

Antiferromagnetic skyrmion repulsion based artificial neuron device.

Magnetic skyrmions are potential candidates for neuromorphic computing due to their inherent topologically stable particle-like behavior, low driving current density, and nanoscale size. Antiferromagnetic skyrmions are favored as they can be driven parallel to in-plane electrical currents as opposed to ferromagnetic skyrmions which exhibit the skyrmion Hall effect and eventually cause their annihilation at the edge of nanotracks. In this paper, an antiferromagnetic skyrmion based artificial neuron device consisting of a magnetic anisotropy barrier on a nanotrack is proposed. It exploits inter-skyrmion repulsion, mimicking the integrate-fire (IF) functionality of a biological neuron. The device threshold represented by the maximum number of skyrmions that can be pinned by the barrier can be tuned based on the particular current density employed on the nanotrack. The corresponding neuron spiking event occurs when a skyrmion overcomes the barrier. By raising the device threshold, lowering the barrier width and height, the operating current density of the device can be decreased to further enhance its energy efficiency. The proposed device paves the way for developing energy-efficient neuromorphic computing in antiferromagnetic spintronics.

Implementation of an efficient magnetic tunnel junction-based stochastic neural network with application to iris data classification.

Stochastic neuromorphic computation (SNC) has the potential to enable a low power, error tolerant and scalable computing platform in comparison to its deterministic counterparts. However, the hardware implementation of complementary metal oxide semiconductor (CMOS)-based stochastic circuits involves conversion blocks that cost more than the actual processing circuits. The realization of the activation function for SNCs also requires a complicated circuit that results in a significant amount of power dissipation and area overhead. The inherent probabilistic switching behavior of nanomagnets provides an advantage to overcome these complexity issues for the realization of low power and area efficient SNC systems. This paper presents magnetic tunnel junction (MTJ)-based stochastic computing methodology for the implementation of a neural network. The stochastic switching behavior of the MTJ has been exploited to design a binary to stochastic converter to mitigate the complexity of the CMOS-based design. The paper also presents the technique for realizing stochastic sigmoid activation function using an MTJ. Such circuits are simpler than existing ones and use considerably less power. An image classification system employing the proposed circuits has been implemented to verify the effectiveness of the technique. The MTJ-based SNC system shows area and energy reduction by a factor of 13.5 and 2.5, respectively, while the prediction accuracy is 86.66%. Furthermore, this paper investigates how crucial parameters, such as stochastic bitstream length, number of hidden layers and number of nodes in a hidden layer, need to be set precisely to realize an efficient MTJ-based stochastic neural network (SNN). The proposed methodology can prove a promising alternative for highly efficient digital stochastic computing applications.

Etched multicore fiber sensor using copper oxide and gold nanoparticles decorated graphene oxide structure for cancer cells detection.

A compact, portable, label-free, and ultra-sensitive sensor is proposed to detect cancerous cells based on Multi-Core Fiber (MCF) comprising of seven cores arranged in a hexagonal shape spliced with Single-Mode Fiber (SMF). Here, cytosensing based on fiber optic Localized Surface Plasmon Resonance (LSPR) is used for the efficient detection of different types of cancer cells. The proposed sensor structure is etched in a controlled manner to increase the evanescent wave (EWs) and coupling of modes between the cores of MCF. The etched MCF based LSPR probe has high refractive index sensitivity (RIS). To further increase the sensitivity, the sensor structure is immobilized with different nanomaterials (NMs) such as optimized size of gold nanoparticles (AuNPs), graphene oxide (GO), and copper oxide nanoflowers (CuO-NFs). AuNPs increase the sensitivity using LSPR, whereas, GO and CuO-NFs helps to increase the biocompatibility of sensor. The developed probe is further coated with 2-deoxy-D-glucose (2-DG) over NMs that are specific for the detection of cancer cells. In this work, various cancerous cell lines i.e. HepG2, Hepa 1-6, MCF-7, A549, and normal cell lines i.e. NCF and LO2 are detected using the developed sensing probe. Various analysis of proposed sensor such as selectivity, reusability, anti-interference ability, and involvement of GLUT receptor in detection has also been performed. The proposed etched sensor is ultra-sensitive for detection of HepG2, Hepa1 6, A549, MCF-7, LO2, and NCF cell lines with a limit of detection (LoD) of 3, 2, 2, 2, 4, 10 cells/mL, respectively in the linear range of 1 × 102-1 × 106 cells/mL.

Transition metal dichalcogenides integrated waveguide modulator and attenuator in silicon nitride platform.

Embedding transition metal dichalcogenides (TMDs) into optical devices enhance the light-matter interaction, which holds a great promise for designing compact integrated photonic components. The chemical composition and thickness of TMDs affect their electronic and optical properties. The optical properties demonstrate stable and strong gate tunable optical response near the excitonic transitions. These materials are, therefore, promising candidates for designing electro-optic modulators and attenuators. Here, an electro-absorption modulator is investigated based on integrating different TMD monolayers on silicon nitride waveguides near the excitonic binding energy. A comparison of absorption changes due to electrostatically induced charges in MoS2, MoSe2, WS2, WSe2, and graphene has been presented for modulator design. The results show that with the confinement factor of about 0.10% in the monolayer TMDs, the modulation strength is 10x higher in WS2 as compared to the graphene-based modulator design. The WS2 based modulator shows the highest modulation strength with an improvement by a factor of 5 as compared to Mo based designs. Further, the change in the spectral response of these materials with thickness and chemical composition has been exploited for the design of attenuator. A micro-opto-mechanical system technology with TMD integrated supersubstrate above a Si3N4 waveguide affecting the optical response is investigated. By replacing the TMD in the supersubstrate with Se atom instead of S in the MX2 and WX2 compound, the attenuation is shifted from visible to near-infrared range allowing tuning from 620 to 750 nm. The tuning of the attenuation wavelength will help the designer choose the best material for visible light photonic applications.

Detection of Collagen-IV Using Highly Reflective Metal Nanoparticles-Immobilized Photosensitive Optical Fiber-Based MZI Structure.

In this work, a photosensitive (PS) optical fiber-based Mach-Zehnder interferometer (MZI) structure is developed to diagnose the presence of collagen-IV in human bodies. The MZI is fabricated by sequentially splicing the single mode-multimode-photosensitive-multimode-single mode (SMPMS) fiber segments. The sensing region in MZI structure is created by partially removing the cladding of photosensitive fiber by using 40% hydrofluoric (HF) acid and depositing the layers of highly reflective metal nanoparticles (NPs) over it. The used NPs are polyvinyl alcohol stabilized silver nanoparticles (PVA-AgNPs), gold nanoparticles (AuNPs), and zinc oxide nanoparticles (ZnO-NPs). The size of AuNPs, PVA-AgNPs, and ZnO-NPs are 10 ± 0.2 nm,  âˆ¼  4 -5 nm, and < 50 nm, respectively. In order to avoid the interference of other biomolecules in the detection of collagen-IV, the sensing region is functionalized with a collagenase enzyme. The sensing ability of the probe is ascertained by sensing a wide concentration of collagen solution ranging from 0 ng/ml to [Formula: see text]/ml. It is observed that sensing performance of probe is much better on immobilizing it with PVA-AgNPs and ZnO-NPs.

Development of Dopamine Sensor Using Silver Nanoparticles and PEG-Functionalized Tapered Optical Fiber Structure.

This article presents a localized surface plasmon resonance (LSPR) phenomenon based optical fiber sensor (OFS) for the detection of dopamine (DA). DA functions as a hormone and a neurotransmitter in the human body and plays a crucial role in the peripheral system. To develop the OFS for DA detection, taper fiber probe was fabricated and immobilized with silver nanoparticles (AgNPs) and functionalized with Polyethylene glycol (PEG). The developed sensor shows the great selectivity in the presence of ascorbic acid (AA) oxidation due to PEG coating. The morphology of the AgNPs and uniformity of coating over the surface of sensing probe were confirmed with UV-visible spectrophotometer, transmission electron microscope (TEM), energy-dispersive X-ray spectroscopy (EDS), and scanning electron microscope (SEM). The calibration curve is found to be linear over the range of 10 nM-1 µM with the lowest detection limit of 0.058 µM. Also provides a wide dynamic range of detection (10 nm-100 µM). The parameters responsible for the performance of OFS, such as sensitivity, detection limit, and selectivity are greatly improved in the proposed sensor. The applicability of the proposed sensor has been validated and have the potential to use for routine diagnosis.

Development of Uric Acid Biosensor Using Gold Nanoparticles and Graphene Oxide Functionalized Micro-Ball Fiber Sensor Probe.

A highly sensitive and selective optical fiber-based enzymatic biosensor has been proposed in the present study for detection of uric acid (UA) in human serum. The working mechanism of sensor depends on surface plasma property and localized surface plasmon resonance technique. For this purpose, a micro-ball fiber sensor probe of [Formula: see text] diameter was fabricated using advanced fusion-splicer and coated with gold nanoparticles (AuNPs) and graphene oxide (GO) in order to enhance its sensitivity. UV-Visible spectrophotometer and high-resolution transmission electron microscope (HR-TEM) were used to characterize the AuNPs solution and GO aqueous dispersion. The absorbance spectrum of AuNPs and GO are recorded at 519 nm and 230 nm, respectively. The coating of AuNPs and GO over fiber surface were verified by using a scanning electron microscope (SEM) and energy-dispersive X-ray spectroscopy (EDX). Thereafter, sensor probe was functionalized with the specific enzyme i.e. uricase for the UA detection. The linearity response of uricase/GO/AuNPs-coated micro-ball optical fiber sensor is reported in the range of [Formula: see text]-1 mM UA concentrations. The reflectance of sensor linearly decreases with the increasing UA concentrations. Sensitivity of the sensor is 2.1 %/mM with a good slope of linearity with detection limit of [Formula: see text]. To test the accuracy of proposed sensor, UA concentration in serum samples have also tested by using proposed sensor and A5800 Automatic Biochemical Analyzer. The results of the developed sensor are consistent with the results of A5800 Automatic Biochemical Analyzer. Thus, proposed sensor can be successfully utilized for UA detection in human serum samples.

LSPR-based cholesterol biosensor using a tapered optical fiber structure.

Accurate cholesterol level measurement plays an important role in the diagnosis of severe diseases such as cardiovascular diseases, hypertension, anemia, myxedemia, hyperthyroidism, coronary artery illness. Traditionally, electrochemical sensors have been employed to detect the cholesterol level. However, these sensors have limitations in terms of sensitivity and selectivity. In this paper, a localized surface plasmon resonance (LSPR) -based biosensor is demonstrated that accurately detects and measures the concentration of cholesterol. In the present study, a tapered optical fiber-based sensor probe is developed using gold nanoparticles (AuNPs) and cholesterol oxidase (ChOx) to increase the sensitivity and selectivity of the sensor. Synthesized AuNPs were characterized by UV-visible spectrophotometer, transmission electron microscope (TEM), and energy dispersive X-ray spectroscopy (EDS). Further, coating of AuNPs over fiber was confirmed by scanning electron microscope (SEM). The developed sensor demonstrates for a clinically important cholesterol range of 0 to 10 mM, and the limit of detection is found to be 53.1 nM.