AUS Repository

Recent Submissions

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    Estimation of Metallic Coating Thicknesses Using Eddy Current Spectroscopy and Machine Learning
    (2025-03) Aldbaisi, Atheer Ghiath; Abu-Nabah, Bassam; Alkhader, Maen
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    Bearing Capacity Factors for Strip Footings on Soft Clay Stabilized with a Trapezoidal Granular Trench
    (2025-05) Abu Othman, Abdel Razzaq; Attom, Mousa; Yamin, Mohammad
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    Acetone Sensor Readout Circuit for Noninvasive Diabetes Diagnosis and Monitoring
    (2025-05) Georgeous, Joel Nabil; Albasha, Lutfi; Husseini, Ghaleb
    This thesis explores the development of a breath acetone sensor readout method for the non-invasive monitoring and diagnosis of diabetes mellitus (DM). DM is a chronic metabolic disorder characterized by insufficient insulin production or its impaired use in the cells, leading to a high blood glucose level. This disease requires constant monitoring of blood glucose levels. Traditional blood glucose monitoring techniques are invasive and inconvenient, highlighting the need for non-invasive alternatives. Breath acetone, a byproduct of fat metabolism in diabetic patients, has been identified and used as a biomarker for diabetes as it is directly related to blood glucose levels. Its concentration is significantly higher in the breath of diabetic patients, making it an effective indicator of the disease’s progression. This research aims to develop a real-time, precise readout method for a highly selective and sensitive acetone sensor developed previously in the literature. The sensor utilizes a capacitive measurement technique where its dielectric constant varies with acetone concentrations. A capacitive readout circuit processes the sensor’s output, which converts capacitance to a DC output voltage. The capacitance is measured through a series of inverters that output a pure square wave. The phase shift in the square wave is correlated with a change in the sensor’s capacitance. A subtractor op-amp finds the difference between the original square wave and the one with a phase shift. A passive low-pass filter finds the average of the difference output signal, generating a DC signal with a value corresponding to the difference extent. Next, the signal is processed in a microprocessor that displays health information on a graphic user interface (GUI).
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    Development of a MOF-based Material for Cholesterol Detection
    (2025-05) Abed, Heba Farid; Sabouni, Rana; Ghommem, Mehdi
    Cholesterol detection is essential for early diagnosis and monitoring of cholesterol-related diseases, such as atherosclerosis, hypercholesterolemia, and liver diseases. A variety of nanomaterials have been designed and synthesized for cholesterol detection via electrochemical and spectrophotometric techniques. Metal organic frameworks (MOFs) have emerged as promising detector materials for cholesterol sensing. Recent research has explored MOFs as spectrophotometric cholesterol sensors with remarkable performance in terms of limit of detection (LOD) and selectivity. Given the growing interest in cholesterol sensing and limitations of existing biosensors, this thesis aims to develop a novel MOF-based spectrophotometric sensing platform for cholesterol detection. First, this thesis reviews recent advances in MOF-based spectrophotometric cholesterol sensors, outlining the different mechanistic roles of MOFs in cholesterol detection, current challenges, and potential applications of MOF-based sensors for cholesterol detection in point-of-care devices and medical diagnostics. Then, iron-based MOF (Fe-BTC) is introduced as a novel, peroxidase mimic nanozyme for cholesterol detection, marking its first reported use in this application. Characterization studies, including Fourier transform infrared spectroscopy, X-ray diffraction, and zeta potential, revealed stable, amorphous nature of Fe-BTC and potential peroxidase activity. Parametric studies including pH, time, temperature, and reagent concentrations were performed to determine optimal conditions for H2O2 and cholesterol detection. Mechanistic studies demonstrated biosensor operation via OH· radical formation by Fe-BTC. The present biosensor achieved a cholesterol limit of detection (LOD) of 2.91 μM and 2.88 μM at 25 ⁰C and 37 ⁰C cholesterol incubation, respectively, with a linear detection range of 6.56–78.75 μM. The biosensor had good selectivity to cholesterol in the presence of interfering analytes, including glycine, uric acid, glucose, and NaCl. Overall, our novel Fe-BTC-based biosensor demonstrated comparable performance to nanomaterial-based cholesterol sensors reported in the literature and shows great promise for cholesterol detection via spectrophotometric methods.

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