A New Model for Thin Film Solar Cells Using Photon Cycling

The solar energy has emerged as one of the most promising and reliable renewable energy resources attracting much attention to the study of photovoltaics. A principal aim of solar cells is to maximize the absorption of light to increase the generation of electron-hole pairs and harnessing it to increase the power generated. An attractive approach of increasing the generation rate in a thin PV cell by employing photon cycling. In this thesis, I report the results of my study using the updated solar irradiance, and a new model for calculating the generation rate in thin film solar cells. I develop a multiple light path model to arrive at a generation rate using my approximation of absorption coefficient and other physical structures at the back and front contacts. By increasing the path lengths, the generation of photocarriers at each level results in enhanced photocurrent. In calculating this, I have used a new approximation of the absorption coefficient as a function of wavelength. The consequence of the bandgap narrowing effect on the absorption coefficient has been studied using an existing model to show its impact on the generation rate. Furthermore, an optimized design for thin film solar cells is introduced to examine the photon cycling effect on the generation rate. It shows that considering the impact of photon cycling efficiently leads to enhancing the total generation rate by 144%. This permits reducing the thickness of the solar cell, which eventually reduces the cost of the cell. A novel model for accurate computation of the photon cycling effect has been developed, applicable to different semiconductor PVs. Finally, the photon recycling and the luminescent coupling effect are investigated to show an improvement up to 65% for the GaAs generation rate

Noninvasive Glucose Detection Using Infrared Photoacoustic Spectroscopy and Machine Learning

The ideal method to monitor diabetes is to obtain the glucose level with a fast, accurate, and pain-free measurement that does not require blood drawing or finger pricking. Although the development of noninvasive devices for blood glucose measurement started three decades ago, no clinically proven devices were commercially released in the market. Among all the noninvasive glucose detection techniques, optical spectroscopy has rapidly advanced, including infrared (IR) and photoacoustic (PA) spectroscopy. The combination of IR and PA spectroscopy has shown promising developments in recent years as a substitute for invasive glucose monitoring technology. The IR region has a strong relationship with glucose due to the presence of glucose absorption peaks. PA spectroscopy utilizes the vibration modes of the glucose molecules in the IR region and the weak water absorption of acoustic signals as an alternative approach to compensate for the optical losses in the IR transmission and absorbance spectroscopy. The concept of PA spectroscopy relies on generating acoustic waves, by an electromagnetic source, that are distinguishable from one material to another and can be detected by sensitive ultrasonic or piezoelectric sensors. The first part of the thesis demonstrates the development of the IR and PA system for noninvasive glucose monitoring. The IR and PA system has been developed using a single wavelength quantum cascade laser (QCL), lasing at a glucose fingerprint of 1080 cm. In biomedical applications, phantoms are widely used as test models to substitute targeted body objects. Biomedical skin phantoms with similar properties to human skin have been prepared at different glucose concentrations of 25 mg/dL as test models for the setup. The system shows feasibility in detecting glucose using these skin phantoms, covering the normal and hyperglycemia blood glucose ranges. Machine learning classification models have been employed to enhance the prediction accuracy of glucose levels using unprocessed acoustic signals. The second part of the thesis extends the development of the IR and PA system. A dual single-wavelength quantum cascade lasers (QCLs) system has been developed using PA spectroscopy for noninvasive glucose monitoring. The glucose detection sensitivity of the IR and PA spectroscopy has improved to 12.5 mg/dL using dual QCLs lasing at 1080 and 970 cm, The artificial skin phantoms have been prepared with other blood components at different glucose concentrations. The dual QCLs system demonstrates sustainability in detecting glucose concentrations in the presence of albumin, sodium lactate, cholesterol, and urea. An ensemble classifier model has been developed to predict the glucose level of skin samples. The model has achieved 96.7% prediction accuracy for samples with and without blood components, with 100% of the predicted data located in zones A and B of Clarke’s error grid analysis (EGA). After demonstrating the glucose detectability of the IR and PA system for the in vitro measurements, the system has progressed to the in vivo experiments. The operating power of the QCLs has been lowered to fulfill the safety guidelines of using light sources on human skin. The blood glucose concentration can be potentially measured from the interstitial fluid (ISF) located underneath the skin in the epidermis layer. The glucose diffuses from the blood to the ISF layer, creating a significant opportunity for noninvasive monitoring systems. The in vivo measurements of the fiber-coupled dual QCLs and PA system have been assessed by oral glucose tolerance test (OGTT). The preliminary results from the In vivo measurements demonstrate that the mid-infrared (MIR) and PA system can detect glucose levels not only in the biological samples but also in real human skin. Finally, a Gaussian Process regression model has been developed to improve the prediction accuracy of the IR and PA system

A Single Wavelength Mid-Infrared Photoacoustic Spectroscopy for Noninvasive Glucose Detection Using Machine Learning

According to the International Diabetes Federation, 530 million people worldwide have diabetes, with more than 6.7 million reported deaths in 2021. Monitoring blood glucose levels is essential for individuals with diabetes, and developing noninvasive monitors has been a long-standing aspiration in diabetes management. The ideal method for monitoring diabetes is to obtain the glucose concentration level with a fast, accurate, and pain-free measurement that does not require blood drawing or a surgical operation. Multiple noninvasive glucose detection techniques have been developed, including bio-impedance spectroscopy, electromagnetic sensing, and metabolic heat conformation. Nevertheless, reliability and consistency challenges were reported for these methods due to ambient temperature and environmental condition sensitivity. Among all the noninvasive glucose detection techniques, optical spectroscopy has rapidly advanced. A photoacoustic system has been developed using a single wavelength quantum cascade laser, lasing at a glucose fingerprint of 1080 cm−1 for noninvasive glucose monitoring. The system has been examined using artificial skin phantoms, covering the normal and hyperglycemia blood glucose ranges. The detection sensitivity of the system has been improved to ±25 mg/dL using a single wavelength for the entire range of blood glucose. Machine learning has been employed to detect glucose levels using photoacoustic spectroscopy in skin samples. Ensemble machine learning models have been developed to measure glucose concentration using classification techniques. The model has achieved a 90.4% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke’s error grid analysis. This finding fulfills the US Food and Drug Administration requirements for glucose monitors