Browsing by Author "Vivek B. Singh"

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Advancements in optical waveguide sensors through Fano resonance
(Elsevier Ltd, 2025) Ayushi Rawat; Rajiv Maurya; Ankit Mishra; Gaurav Singh; Aayush Dixit; Vivek B. Singh
Fano resonance, a unique interference phenomenon between a discrete state and a continuum, has shown great potential to enhance the performance of optical waveguide sensors. FR-based sensors exhibit sharp, asymmetric spectral features with a narrow full width at half maximum, offering high sensitivity and a high figure of merit. These characteristics make them promising candidates for refractive index sensing and various biochemical applications. While many waveguide configurations have been explored, this work focuses on planar and fiber optical waveguide platforms due to their practical advantages in integration, fabrication, and real-world applications. This review highlights recent advancements in FR-based optical waveguide sensors, their working principles, design strategies, and potential applications. Continued research in novel waveguide designs, material engineering, and resonance tuning will drive future innovations, ultimately paving the way for next-generation optical waveguide sensing technologies for diverse applications in biosensing, environmental monitoring, and more. © 2025 Elsevier Ltd
Detection of aqueous nitro explosives using SPR sensors integrated with D-shaped optical fibers
(Springer, 2025) Abhishek Upadhyay; Gaurav Sharma; Rajiv Maurya; Ankit Mishra; Vivek B. Singh
This study presents an optimization and simulation of a surface plasmon resonance-based optical fiber sensor with a D-shaped configuration, for the precise detection of aqueous nitro explosive. The proposed sensor undergoes simulation and analysis using finite element techniques. Optimization of the Au layer thickness, with a 30 nm layer identified for maximum SPR curve reflection, Subsequently, a polyaniline layer was applied to enable selective detection. Varying sample concentrations interact uniquely with the polyaniline sensing surface, as concentration increases, the SPR dip shifts toward longer wavelengths like red-shift in the spectra. Simulating sensor lengths ranging from 1 to 5 mm provides valuable insights into sensor responses, showing improved detection accuracy and figure of merit as the sensor length decreases. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
Evaluation of 3D-Var and 4D-Var data assimilation on simulation of heavy rainfall events over the Indian region
(John Wiley and Sons Ltd, 2025) Shivaji Singh Patel; Ashish Routray; Vivek B. Singh; R. Bhatla; Rohan Kumar; Elena D. Surovyatkina
The present study delineates the relative performance of 3D-Var and 4D-Var data assimilation (DA) techniques in the regional NCUM-R model to simulate three heavy rainfall events (HREs) over the Indian region. Four numerical experiments for three extreme rainfall cases were conducted by assimilating different combinations of observations from surface, aircraft, upper-air and satellite-derived Atmospheric Motion Vectors (AMVs) using 3D-Var and 4D-Var techniques. These experiments generated initial conditions (ICs) for the NCUM-R forecast model to simulate HREs. Key atmospheric variables, such as wind speed and direction, vertically integrated moisture transport (VIMT: kg.m−1.s−1), vertical profiles of relative humidity and temperature as well as various stability indices are analysed during the HREs. Forecast verification was performed using statistical skill scores and object-based methods from the METplus tool, comparing NCUM-R output against GPM rainfall data. The results demonstrate that the 4D-Var technique improves simulation accuracy compared to 3D-Var, particularly when assimilating satellite wind data. Incorporating satellite-derived AMVs improved the representation of rainfall intensity and spatial patterns, as well as other atmospheric variables. It is found that rainfall for Case-01, the VIMT was notably high along the eastern coast of India and southwest of BoB, with the 4DVS simulation better capturing moisture transport patterns compared to 3DVS and 3DV. The SWEAT index ranged from 205 to 250 J·kg−1 in the morning, rising to 250–300 J·kg−1 by noon, indicating increasing convective instability. On 18 March 2023 (Day-1), the K-index exceeded 30, signalling scattered thunderstorms, consistent with the IMD's reports of isolated to scattered rainfall on 19th and 20th March 2023. Similarly, it is found that satellite wind assimilation improved the statistical skill scores in predicting heavy precipitation in all three cases. Overall, the study suggested that the performance of the NCUM-R model integrated with the 4D-Var technique improved the model's forecast skill in the simulation of HREs. © 2025 The Author(s). Meteorological Applications published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.
Evaluation of NCUM-R 4DVAR assimilation technique’s performance on simulation of tropical cyclones over NIO region
(Springer Science and Business Media Deutschland GmbH, 2025) Shivaji Singh Patel; Ashish Routray; Vivek B. Singh; Devajyoti Dutta; R. Bhatla; Biranchi Kumar Mahala; John Opatz
The present study evaluates the forecasting skill of a regional model (NCUM-R) with the 4DVAR analysis system on the simulation of pre- and post-genesis phases of three tropical cyclones (TCs) over the North Indian Ocean (NIO) region. Specifically, the study aims to determine whether the NCUM-R model can identify pre-genesis of the storm 2–3 days in advance to accurately predict the intensity and track of TCs. The selected storms include the Severe Cyclonic Storm (SCS) ‘Asani’ (2022), the extremely severe cyclonic storm (ESCS) ‘Mocha’ (2023), and ESCS ‘Biparjoy’ (2023). The initial conditions (ICs) are prepared for the NCUM-R forecasting model by assimilating various observations through the 4DVAR data assimilation (DA) technique. Various thermodynamic and dynamic variables, such as genesis potential parameters (GPP), potential vorticity (PV), relative humidity (RH), landfall position and time errors are examined. The study highlights equatorial moisture’s role in storm development. Moist winds from the equator release latent heat, greatly intensifying storms. The study shows that the track error forecast significantly improved by the NCUM-R model about 7%, 11.2%, 22.8% and 21.9% on 00, 24, 48 and 72 UTC forecasts. The landfall position and time error are relatively less in the regional model. Further, the NCUM-R model’s outputs are validated against India Meteorological Department (IMD) observations and Fifth Generation of ECMWF Atmospheric ReanalysisERA-5, demonstrating a good match in TCs’ pattern and intensity. Various statistical skill scores for rainfall are calculated with respect to satellite-merged estimated precipitation data (i.e. GPM) through the enhanced Model Evaluation Tool (METplus), showing a significant correlation with observed data. The model is suggested to capture low-intensity rainfall well, but its forecast skill decreases as rainfall thresholds increase with the forecast day. The study discerns that the NCUM-R model reasonably well forecasts the storm track, intensity, and structure before and after the genesis of the TCs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Influence of meteorological variability on aerosol size distribution during the winter fog campaign over Delhi: a case study
(Elsevier Ltd, 2025) Atul Kumar Kumar; Kirpa Ram; Deewan Singh F. Bisht; Made P. Raju; Vivek B. Singh; V. K. Soni
The aerosol size distribution, particularly the number and mass distributions, plays a crucial role in understanding changes in optical properties due to hygroscopic growth, which affects visibility and radiative forcing on a regional scale. The Indo-Gangetic Plain (IGP), including National Capital Region (NCR) of Delhi, experiences severe fog and haze with reduced visibility during the post-monsoon to winter months (October–February) every year. This study reports aerosol mass size distribution over Delhi during a winter fog campaign (December 15, 2015–February 15, 2016) using a ground-based optical particle counter. The fine and coarse mode aerosols were contributed to ∼85% and 15% to the total aerosol mass concentration during the campaign period. The characteristic changes in aerosol size distribution, effective radius, and the influence of meteorological factors, particularly relative humidity (RH) and temperature, under three visibility conditions: Vis-1 (<600 m), Vis-2 (600–1200 m), and Vis-3 (>1200 m) were investigated. Fine-mode aerosols accounted for ∼85 % of the total aerosol mass, with their concentration increasing by a factor of 3.7 during Vis-1 and 2.3 during Vis-2 compared to Vis-3, when the effective radius of aerosol was lowest (Reff: 0.44 μm). Fine particle concentrations showed a positive correlation with RH (R = 0.35) and a negative correlation with visibility (R = −0.65), suggesting that the high RH and fine-mode aerosols contribute to fog formation and reduced visibility in Delhi-NCR. © 2025 Elsevier Ltd
LMR and SPR induced Fano resonance in a planar waveguide-coupled D-shaped optical fiber for enhanced refractive index sensing in the Vis–NIR region
(Optica Publishing Group (formerly OSA), 2025) Rajiv Maurya; Ankit Mishra; Chandan Singh Yadav; Abhishek Upadhyay; Gaurav Sharma; Vivek B. Singh
This study examines the performance of an indium tin oxide coated D-shaped optical fiber (ITO-DOF) sensor and a planar waveguide-coupled indium tin oxide coated D-shaped optical fiber (PWG-DOF) sensor for inline refractive index sensing applications. The ITO-DOF sensor enables lossy mode resonance in the visible region and surface plasmon resonance in the near-infrared region. The PWG-DOF sensor enables the simultaneous generation of Fano resonance in both regions by utilizing lossy mode resonance and surface plasmon resonance effects across the visible and near-infrared regions. It is observed that the PWG-DOF sensor achieves a higher figure of merit than the ITO-DOF sensor due to its narrower full-width at half-maximum. Also, the penetration depth of the Fano resonance mode is recorded at 127.99 nm in the visible region and 500.81 nm in the near-infrared region, surpassing lossy mode resonance (126.75 nm) and surface plasmon resonance (499.91 nm). These values increase with film thickness, highlighting the Fano resonance as a superior sensing signal. Given its improved figure of merit and penetration depth, this study suggests that Fano resonance can enhance the sensitivity and performance of refractive index sensors beyond conventional lossy mode resonance and surface plasmon resonance techniques. © 2025 Optica Publishing Group.
Long-Term Impact of Aerosols and Climate Variability on Rice Yields across Agroclimatic Zones in India
(Springer Science and Business Media Deutschland GmbH, 2025) Dileep Kumar Gupta; Subhajit Pramanick; Abhay Kumar Singh; Vivek B. Singh; Dhiraj Kumar Singh; Aqil Tariq; Hamza A. Halwani; Yazeed H. Alsubhi; Ahmed S. Hantoush; Gurwinder Singh
The need for a robust food security framework in India requires assessing the effects of air quality and weather on crop yields, while adopting practices such as choosing suitable varieties, adjusting planting schedules, and improving irrigation to reduce adverse impacts. In this study, a long-term assessment of the impact of weather, Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), soil moisture (SM), and Aerosol Optical Depth (AOD) on historical rice production was conducted across various Agroclimatic Zones in India from 1998 to 2019. A statistical model was developed for this purpose, achieving an exceptional accuracy of 94.9% for rice crop during the Kharif season in India. The findings indicate that the highest negative impacts of rainfall, FAPAR, and AOD on rice production were observed in the EPH region. However, the minimum and maximum temperatures had the most adverse effects in the MGP and GPH regions. Rainfall exhibited an almost negligible impact on rice yield during the studied period. Relative humidity (RH), FAPAR, and SM were generally favorable for rice yield across most of Agroclimatic Zones during the historical period. India experienced an average annual decline in rice yields of − 4.09%, − 1.11%, − 0.11%, and − 0.73% due to adverse fluctuations in maximum and minimum temperatures, rainfall, and aerosol levels, respectively. In contrast, RH, FAPAR, and SM contributed to yield increases of 0.56%, 0.08%, and 1.17% per year, respectively. Overall, the model indicates that Indian rice production declined by an average of − 3.93% annually between 1998 and 2019, due to the combined impact of weather/FAPAR/SM/AOD fluctuations. The combined effects of these factors led to an average annual decline in rice production in the states of Odisha, Maharashtra, Bihar, and Uttar Pradesh. The limitation of this study is the absence of future projections for AOD, which are essential to evaluate its potential impacts across diverse agroclimatic zones. © King Abdulaziz University and Springer Nature Switzerland AG 2025.
Machine learning-enhanced detection of chlorpyrifos using molecularly imprinted polymer-coated optical fibers
(Elsevier B.V., 2025) Ankit Mishra; Rajiv Maurya; Suraj Prakash; Chandan Singh Yadav; Abhishek Upadhyay; Ritu Singh; Meenakshi K. Singh; Vivek B. Singh
This paper explores the use of large core declad optical fibers coated with molecularly imprinted polymers for chlorpyrifos detection, a key marker of organophosphate pesticides. The performance of sensor is evaluated using artificial neural networks and principal component analysis. By varying the declad length, the performance of molecularly imprinted polymer-coated fibers is compared to uncoated fibers, and both are used to identify commercial and pure samples of chlorpyrifos pesticides. Molecularly imprinted polymer-coated declad fiber sensors particularly those with longer declad lengths, exhibit significantly lower detection limits and higher sensitivity. The obtained maximum sensitivity, and minimum detection limit at 4 cm declad fiber length are 0.0027 mV/nM and 60.70 nM respectively. The results obtained also demonstrate that the artificial neural network can make an accurate prediction and the principal component analysis validates the efficacy of our molecularly imprinted polymer-coated fibers in chlorpyrifos detection. © 2025 Elsevier B.V.
Novel Planar Waveguide-Coupled D-Shaped Optical Fiber Sensor to Generate Fano Resonance for Enhanced Refractive Index Sensing Applications
(Institute of Electrical and Electronics Engineers Inc., 2025) Rajiv Maurya; Ankit Mishra; Chandan Singh Yadav; Abhishek Upadhyay; Gaurav Sharma; Vivek B. Singh
In this article, the generation of Fano resonance (FR) in a novel optical fiber platform, which addresses a significant challenge within the scientific community, is theoretically investigated. The proposed sensor is designed with a D-shaped surface plasmon resonance (SPR) fiber coupled with a three-layer planar waveguide (PWG) structure for inline and enhanced refractive index (RI) sensing applications. Our analysis demonstrates that an optimum thickness of low index dielectric material, i.e., cytop fluoropolymer, as a coupling layer is required to generate FR in association with SPR. It is observed that the FR demonstrates a significant enhancement in the figure of merit (FOM), achieving 6383 RIU-1 for wavelength interrogation and 13 195 a.u./RIU for intensity interrogation at df = 520 nm and dc = 700 nm. These values greatly surpass the FOM of conventional SPR-based sensors, which are 34.90 RIU-1 and 39.96 a.u./RIU. Also, the FOM increases by increasing the thickness of coupling layer. Furthermore, FWHM of the FR is consistent with the length of D-shaped region, whereas FWHM of SPR increases as the length of D-shaped region increases. The penetration depth of FR mode's evanescent field in the sensing region also increases with the film layer thickness, consistently exceeding the penetration depth of SPR (122.47 nm). Hence, the FR mode is proposed as the sensing signal instead of conventional SPR mode because it offers superior performance compared in terms of FOM and penetration depth. © 2025 IEEE.
Optimization and performance analysis of a D-shaped polymer optical fiber SPR sensor for selective detection of cadmium ions
(Elsevier GmbH, 2025) Ankit Mishra; Rajiv Maurya; Abhishek Upadhyay; Gaurav Sharma; Pushpender Kumar Gangwar; Dr Vivekanand Mishra; Vivek B. Singh
This article explores the theoretical optimization and performance analysis of a surface plasmon resonance sensor utilizing a single-mode D-shaped polymer optical fiber for the detection of cadmium ions. In this structure, a sensing layer made of polyvinylpyrrolidone is employed over metal to protect it from environmental chemical reactions and selective sensing application of cadmium ions. Numerical investigations of the proposed structure have been carried out employing the finite element method. By optimizing the thickness of the metal, residual cladding, and sensing layer, the sensitivity and detection accuracy of the surface plasmon resonance sensor are estimated. The proposed sensor can detect the cadmium ions of concentration ranging from 0.5 ppm to 1000 ppm. The highest sensitivity (1500 nm/RIU), detection accuracy (29.6921), and figure of merit (64.4640 /RIU) of proposed sensors is observed at 1 ppm concentration of cadmium ions. Despite some variation, the detection accuracy and figure of merit remain high across all considered concentrations of cadmium ions, indicating the reliable performance of the sensor. Its optimal performance at lower concentrations is particularly beneficial for early detection and continuous monitoring of cadmium contamination. © 2024 Elsevier GmbH