Dr. Adil Mustafa

Human activities are increasingly contaminating surface and groundwater reserves. Among various pollutants, ethylene glycol (EG) contamination in water is particularly dangerous. At low concentrations it can enter the body undetected and causes serious health problems such as kidney failure and gastrointestinal disorders. This study demonstrates the use of symmetrically etched single-mode plastic optical fiber (POF) sensor model operating at 1550 nm for detecting EG presence in water using COMSOL Multiphysics. The working of the sensor is based on evanescent field interactions with surrounding medium to detect refractive index (RI) changes, while transmission variations through etched POF serving as the sensing metric. Simulations were conducted for aqueous EG solutions ranging from 0 to 0.15 weight fraction, corresponding to RI values ranging between 1.316 and 1.330. The sensor design was optimized by examining the impact of etched cladding diameter and etched length on sensitivity. These parameters were varied from 60 to 7.05 and 1 to 30 μm, respectively. This in turn lead to sensitivity values in the range of 0.39 × 10
−3
to 99.50 × 10
−3
Trans. (A.U)/RIU. Highlighting the importance of evanescent field-surrounding interaction for etched POF sensors, these findings revealed that sensitivity has direct relation with the length of etched region and inverse relation with cladding diameter. The maximum sensitivity of 99.50 × 10
−3
 Trans. (A.U)/RIU was achieved with a 30 μm etched length and 7.05 μm cladding diameter. The proposed POF-based sensor demonstrates strong potential for applications in biomedical engineering, biochemical monitoring, and beverage industry offering a compact and sensitive solution for EG contamination detection in water.

The possibility of tightly controlling the cellular microenvironment within microfluidic devices represents an important step toward precision analysis of cellular phenotypes in vitro. Microfluidic platforms that allow both long-term mammalian cell culture and dynamic modulation of the culture environment can support quantitative studies of cells’ responses to drugs. Here, we report the design and testing of a novel microfluidic device of simple production (single Polydimethylsiloxane layer), which integrates a micromixer with vacuum-assisted cell loading for long-term mammalian cell culture and dynamic mixing of four different culture media. Finite element modeling was used to predict flow rates and device dimensions to achieve diffusion-based fluid mixing. The device showed efficient mixing and dynamic exchange of media in the cell-trapping chambers, and viability of mammalian cells cultured for long-term in the device. This work represents the first attempt to integrate single-layer microfluidic mixing devices with vacuum-assisted cell-loading systems for mammalian cell culture and dynamic stimulation.

The compatibility of both silicon on insulator (SOI) and phase-changing materials such as vanadium dioxide (VO2) to the CMOS-based fabrication process gave birth to a new breed of sensing devices. These optical sensors are robust, small in size, and require less power and volume of precious analyte for analysis. In this paper, we propose and design a 2D linear Fabry-Perot(FP) label-free bio-sensor for different types of cancer cell detection. A silicon strip waveguide supported on silica with longitudinal ends terminated by the conductive VO2 behaves like a linear optical resonator. Sensing occurs when the evanescent tail of the guided mode interacts with the changing external refractive indices of different analytes leading to a shift in resonant frequency. The proposed model was simulated and analysed using the finite element method provided in COMSOL Multiphysics. Analysis performed for the 4
m long resonator having 1.28
m thick VO2 coating showed the maximum sensitivity of about 12.292 THz/RIU (101.44nm/RIU) for the cervical cancer. The overall sensitivity response of the investigated optical sensor is 12.312 THz/RIU (101.16 nm/RIU) with a correlation coefficient (R2) equal to 0.999. The detectable change in full width at half maximum (FWHM) of resonating mode from 3.6552 to 3.6960 THz for varying external refractive index (RI) makes the sensor immune to laser frequency fluctuations, a common source of noise in most of the resonant frequency shift-based sensing devices. The reported sensing structure has potential applications for highly sensitive label-free optical bio-sensing.

Silicon on insulator (SOI) based sensors provide a reasonable solution to the issues common in traditional linear optical cavities such as wavelength dependant nature of mirrors, size, and maintaining the resonant condition. In this study we presented polydimethylsiloxane (PDMS) coated SOI based linear optical temperature sensing resonator model and analysed it in finite element method by using COMSOL Multiphysics. The phase changing material (PCM) VO
2
on each side of the Si waveguide helped to achieve the resonant condition and thermal tunability of the resonator. An almost linear variation in resonant frequency (wavelength)
f
r
due to the temperature change in the range of 0–90 °C resulted in maximum sensitivity of 0.01 THz/°C or 79.4 pm/°C for the 10 µm cavity length. The recorded sensitivity is at least 5-times (or more) higher than the previous studies. The prominent reasons behind this improvement can be PDMS coating, adequate light matter interaction and proper confinement of resonating mode. The demonstrated sensor model has wide operational frequency range spanning from 10 to 210 THz. Moreover, the reported model also showed an increase in temperature sensitivity from 0.00967 to 0.01 THz/°C while the length of resonator was changed from 2 to 10 µm.

In the past decade, solar photovoltaic (PV) modules have emerged as promising energy sources worldwide. The only limitation associated with PV modules is the efficiency with which they can generate electricity. The dust is the prime ingredient whose accumulation on the surface of PV impacts negatively over its efficiency at a greater rate. This research aims to explore the effects of dust accumulation on the energy output and operating temperature of polycrystalline silicon PV panels situated in two different climatic regions of Pakistan, i.e., Islamabad and Bahawalpur. In both the regions, one PV module is kept in ambient environment for six weeks to allow dust to deposit over its surface and perform experimental analysis with one clean module as reference for performance comparison. After six weeks of atmospheric exposure, dusty modules displayed significantly smaller efficiency as a function of different dust densities in the two regions. Dust samples from both cities are collected and analyzed to evaluate their structural attributes and composition. The PV module in Islamabad region had a dust layer over its surface with a density of 6.388 g/m2 and its efficiency was reduced by 15.08%. In Bahawalpur region, the dust density was observed to be 10.254 g/m2 which caused the output power to be slashed by 25.42%. Temperature analysis of modules shows that dust increases their temperatures which is also a quantity responsible for lower PV power generation with same amount of irradiance. The research findings are crucial for determining and predicting PV power degradation in two different atmospheres and determining the schedule of cleaning cycle.

A microfluidic sensing device utilizing fluid–structure interactions and machine learning algorithms is demonstrated. The deflection of microsensors due to fluid flow within a microchannel is analysed using machine learning algorithms to calculate the viscosity of Newtonian and non-Newtonian fluids. Newtonian fluids (glycerol/water solutions) within a viscosity range of 5–100 cP were tested at flow rates of 15–105 mL h−1 (γ = 60.5–398.4 s−1 ) using a sample volume of 80–400 μL. The microsensor deflection data were used to train machine learning algorithms. Two different machine learning (ML) algorithms, support vector machine (SVM) and k-nearest neighbour (k-NN), were employed to determine the viscosity of unknown Newtonian fluids and whole blood samples. An average accuracy of 89.7% and 98.9% is achieved for viscosity measurement of unknown solutions using SVM and k-NN algorithms, respectively. The intelligent microfluidic viscometer presented here has the potential for automated, real-time viscosity measurements for rheological studies.

In this study, a novel viscosity measurement technique based on measuring the deflection of flexible (poly) dimethylsiloxane (PDMS) micropillars is presented. The experimental results show a nonlinear relationship between fluid viscosity and the deflection of micropillars due to viscoelastic properties of PDMS. A calibration curve, demonstrating this nonlinear relationship, is generated, and used to determine the viscosity of an unknown fluid. Using our method, viscosity measurements for Newtonian fluids (glycerol/water solutions) can be performed within 2–100 cP at shear rates γ = 60.5–398.4 s−1. We also measured viscosity of human whole blood samples (non-Newtonian fluid) yielding 2.7–5.1 cP at shear rates γ = 120–345.1 s−1, which compares well with measurements using conventional rotational viscometers (3.6–5.7 cP). With a sensitivity better than 0.5 cP, this method has the potential to be used as a portable microfluidic viscometer for real-time rheological studies.
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•Novel viscosity measurement method: flexible micropillar displacement tracking.•Newtonian and non-Newtonian fluids viscosity measurements with microliter sample volumes.•Sensitivity of device depends on micropillar arrangement.•Device sensitivity increases with high aspect ratio of micropillars.