SAJID FAROOQ
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Artigo IPEN-doc 30424 Diffusion‑enhanced efficiency of perovskite solar cells2024 - CARDOZO, OLAVO; FAROOQ, SAJID; KIYMAZ, AYKUT; FARIAS, PATRICIA M.A.; FRAIDENRAICH, NAUM; STINGL, ANDREAS; MAIA-JUNIOR, RICARDO; ALVES-JUNIOR, SEVERINO; ARAUJO, RENATO E. deThis study proposes a novel approach to improve the performance of third-generation solar cells, particularly perovskite solar cells (PSCs), by employing zinc oxide (ZnO) nanoparticles (NPs). The ZnO NPs are dispersed on the upper surface of the device, acting as nanodiffusers. This reduces reflection and increases solar radiation absorption in the photovoltaic active layer, enhancing the light pathway within the device. To analyze the impact of ZnO nanodiffusers on solar cell performance, computer simulations using the finite element method (FEM) and experimental analysis were conducted. Green synthesis methods were employed to synthesize ZnO nanoparticles with an average size of 160 nm, which were subsequently characterized. Thin films of ZnO NPs were deposited on the transparent indium tin oxide (ITO) electrode using spin coating, and their optical response was evaluated. This study proposes methodologies for optical and electrical modeling of third-generation photovoltaic cells using ZnO NPs. Optical computational modeling results evidence that ZnO nanospheres with a diameter of 160 nm predominantly scatter solar radiation in the forward direction. The incorporation of ZnO NPs (160 nm in diameter) reduces device reflectance, resulting in efficient light coupling and increased absorbance in the active layer. The integrated effects of light trapping and anti-reflective properties enhance photocurrent generation, leading to an increase in short-circuit current density. Experimental verification with ZnO NP deposition on PSCs confirms a 23.5% enhancement in photovoltaic device efficiency, increasing from 10.6 to 13.1% in an 11.68 cm2 perovskite cell. The study presents the optical benefits of ZnO nanostructures, including anti-reflective effects and light scattering, when integrated into devices containing thin films as active material.Artigo IPEN-doc 29788 Rapid identification of breast cancer subtypes using micro-FTIR and machine learning methods2023 - FAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, MOISES O. dos; SANTOS, SOFIA N. dos; BERNARDES, EMERSON S.Breast cancer (BC) molecular subtypes diagnosis involves improving clinical uptake by Fourier transform infrared (FTIR) spectroscopic imaging, which is a non-destructive and powerful technique, enabling label free extraction of biochemical information towards prognostic stratification and evaluation of cell functionality. However, methods of measurements of samples demand a long time to achieve high quality images, making its clinical use impractical because of the data acquisition speed, poor signal to noise ratio, and deficiency of optimized computational framework procedures. To address those challenges, machine learning (ML) tools can facilitate obtaining an accurate classification of BC subtypes with high actionability and accuracy. Here, we propose a ML-algorithmbased method to distinguish computationally BC cell lines. The method is developed by coupling the K-neighbors classifier (KNN) with neighborhood components analysis (NCA), and hence, the NCA-KNN method enables to identify BC subtypes without increasing model size as well as adding additional computational parameters. By incorporating FTIR imaging data, we show that classification accuracy, specificity, and sensitivity improve, respectively, 97.5%, 96.3%, and 98.2%, even at very low co-added scans and short acquisition times. Moreover, a clear distinctive accuracy (up to 9 %) difference of our proposed method (NCA-KNN) was obtained in comparison with the second best supervised support vector machine model. Our results suggest a key diagnostic NCA-KNN method for BC subtypes classification that may translate to advancement of its consolidation in subtype-associated therapeutics.Artigo IPEN-doc 29715 Selecting plasmonic nanoshells for colorimetric sensors2023 - BALTAR, RAPHAEL M.S.M.; FAROOQ, SAJID; ARAUJO, RENATO E. deIn this work, the use of gold and silver nanoshells was evaluated as a starting point for the establishment of colorimetric sensor platforms. The sensitivity and linearity of the nanoplatforms (SiO2 core–metallic shell nanoparticles) were assessed under the influence of the nanoshell configuration, color space, and light source illuminant. A computational procedure for selecting high-performance plasmonic colorimetric sensor platforms is described. The evaluation methodology involves considering five different color spaces and 15 different color components. By exploring crucial figures of merit for sensing, the performance of the plasmonic nanoplatforms was evaluated, exploring Mie theory. We determined that gold nanoshells are highly efficient on colorimetric sensing, while silver nanoshells are a better choice for spectroscopic sensors. Plasmonic nanoplatforms based on nanoshells with 10 nm SiO2 core radii and 5 nm thick Au shells presented sensitivity values up to 4.70 RIU−1 , considering the hue angle of the HSV color space. Color variation of up to 40% was observed, due to the adsorption of a 10 nm thick molecular layer on the gold nanoshell surface. In the search for advances in colorimetric biosensors, the optimization approach used in this work can be extended to different nanostructures.