SAJID FAROOQ
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Artigo IPEN-doc 30774 High-performance plasmonics nanostructures in gas sensing2025 - FAROOQ, SAJID; BERECZKI, ALLAN; HABIB, MUHAMMAD; COSTA, ISOLDA; CARDOZO, OLAVOPlasmonic nanostructures have emerged as indispensable components in the construction of high-performance gas sensors, playing a pivotal role across diverse applications, including industrial safety, medical diagnostics, and environmental monitoring. This review paper critically examines seminal research that underscores the remarkable efficacy of plasmonic materials in achieving superior attributes such as heightened sensitivity, selectivity, and rapid response times in gas detection. Offering a synthesis of pivotal studies, this review aims to furnish a comprehensive discourse on the contemporary advancements within the burgeoning domain of plasmonic gas sensing. The featured investigations meticulously scrutinize various plasmonic structures and their applications in detecting gases like carbon monoxide, carbon dioxide, hydrogen and nitrogen dioxide. The discussed frameworks encompass cutting-edge approaches, spanning ideal absorbers, surface plasmon resonance sensors, and nanostructured materials, thereby elucidating the diverse strategies employed for advancing plasmonic gas sensing technologies.Artigo IPEN-doc 30501 Advances in metallic‑based localized surface plasmon sensors for enhanced tropical disease detection2024 - FAROOQ, SAJID; ZEZELL, DENISE M.Tropical diseases present significant challenges to global health, particularly in resource-limited regions. Early and accurate detection of these diseases is vital for effective management and control. In recent years, metallic-based LSPR sensors have emerged as promising diagnostic tools for sensitive and rapid detection of tropical diseases. This comprehensive review aims to provide an in-depth analysis of the current state of research on metallic-based LSPR sensors for the detection of various tropical diseases. In this study, we focused on the connection between neglected tropical diseases (NTDs) and its risk using metallic-based LSPR sensors to identify potential inflammatory biomarkers. We conducted a literature search using PubMed, Web of Science, and Google Scholar. Only published materials written in English were considered, resulting in the identification of 220 articles. After a comprehensive evaluation, we selected 35 relevant ones. Our analysis revealed 35 links to neglected tropical diseases, providing valuable insights into their relationship using metallic-based LSPR sensors. Moreover, we explore the potential of metallic-based LSPR sensors in point-of-care testing and their integration with emerging technologies such as microfluidics and smartphone-based diagnostics. This review underscores the need for continued research efforts to develop affordable, sensitive, and user-friendly metallic-based LSPR sensors for early detection and surveillance of tropical diseases.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 30368 Recognition of breast cancer subtypes using FTIR hyperspectral data2024 - FAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, SOFIA N. dos; BERNARDES, EMERSON S.; ZEZELL, DENISE M.Fourier -transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro -environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data -acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and nonluminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three -dimension (3D) -discriminant analysis approach based on 3D -principle component analysis -linear discriminant analysis (3D-PCA-LDA) and 3D -principal component analysis -quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCALDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.Artigo IPEN-doc 30351 Bridging the gap2024 - HABIB, MUHAMMAD; MUHAMMAD, ZAHIR; HALEEM, YASIR A.; FAROOQ, SAJID; NAWAZ, RAZIQ; KHALIL, ADNAN; SHAHEEN, FOZIA; NAEEM, HAMZA; ULLAH, SAMI; KHAN, RASHIDLayered transition metal dichalcogenides (TMDCs) have garnered immense interest in supercapacitor energy storage applications. Despite the growing reports on TMDCs in the context of electrochemical supercapacitor studies, the prevailing use of carbon-based additives often obscures their correct analysis and overshadows their intrinsic behavior. In this work, we meticulously analyzed supercapacitor characteristics of distinct TMDC materials without using carbon or any other conductive, revealing their pure intrinsic behavior, specifically focusing on highly crystalline 2H phase tantalum (Ta), tungsten (W) and zirconium (Zr)-based TMDCs, grown using the chemical vapor transport (CVT) technique. The grown materials were characterized using cutting-edge techniques like X-ray diffraction (XRD), Raman spectroscopy, and high-resolution transmission electron microscopy (HRTEM), ensuring a comprehensive perspective of the synthesized TMDCs. To delve into the electrochemical properties of the prepared electrodes, extensive analysis using cyclic voltammetry (CV), galvanostatic charge-discharge (GCD) and electrochemical impedance spectroscopy (EIS) was performed. The obtained results were further supported with density functional theory (DFT) calculations to get insights regarding the charge transfer mechanism and electronic density distribution proximate to the Fermi levels. The synergy between the experimental results and theoretical calculations significantly improved the validity of our findings, thus probing the comprehension and optimization avenues of TMDCs for superior supercapacitor performance.Artigo IPEN-doc 30236 Quantitative analysis of high performance plasmonic metamolecules for targeted deep tissues applications2023 - FAROOQ, SAJID; RATIVA, DIEGO; ARAUJO, RENATO E. deRationally designed gold nanoparticles (Au NPs) show a great potential for biomedical applications. Specifically, for optically induced heating of deep tissues facilitated by plasmonic-assisted lasers, nanostructures with high optical absorption coefficient in biological window are required. Plasmonic metamolecules, such as gold nanodimers (NDs), exhibit a robust localized field enhancement with strong infrared optical absorption. However, an exclusive investigation of the optical/ thermal features of high-performance Au NDs for optical infrared heating remains a challenge. Here, we focus on Au NDs for optothermal characteristics in deep tissues heating procedures. Our analysis encompasses parameters such as absorption cross-sections, field enhancement, and temperature rise with a systematic methodology selecting optimal NDs. Our findings reveal a non-uniform spatial distribution of temperature at the nano-scale and show that short-pulsed laser excitation enhances the temperature near the dimer’s junction. Remarkably, when compared to monomeric gold nanorods under the same excitation resonance mode, optically generated heating of Au NDs leads a threefold higher temperature increase. These results evidence valuable insights for using Au NDs as efficient plasmonic nanoheaters in photothermal-assisted applications.Artigo IPEN-doc 30225 Diabetes monitoring through urine analysis using ATR-FTIR spectroscopy and machine learning2023 - FAROOQ, SAJID; ZEZELL, DENISE M.Diabetes mellitus (DM) is a widespread and rapidly growing disease, and it is estimated that it will impact up to 693 million adults by 2045. To cope this challenge, the innovative advances in non-destructive progressive urine glucose-monitoring platforms are important for improving diabetes surveillance technologies. In this study, we aim to better evaluate DM by analyzing 149 urine spectral samples (86 diabetes and 63 healthy control male Wistar rats) utilizing attenuated total reflection–Fourier transform infrared (ATR-FTIR) spectroscopy combined with machine learning (ML) methods, including a 3D discriminant analysis approach—3D–Principal Component Analysis–Linear Discriminant Analysis (3D-PCA-LDA)—in the ‘bio-fingerprint’ region of 1800–900 cm−1 . The 3D discriminant analysis technique demonstrated superior performance compared to the conventional PCA-LDA approach with the 3D-PCA-LDA method achieving 100% accuracy, sensitivity, and specificity. Our results show that this study contributes to the existing methodologies on non-destructive diagnostic methods for DM and also highlights the promising potential of ATR-FTIR spectroscopy with an ML-driven 3D-discriminant analysis approach in disease classification and monitoring.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 29718 Thermo-optical performance of bare laser-synthesized TiN nanofluids for direct absorption solar collector applications2023 - FAROOQ, SAJID; VITAL, CAIO V.P.; TIKHONOWSKI, GLEB; POPOV, ANTON A.; KLIMENTOV, SERGEY M.; MALAGON, LUIS A.G.; ARAUJO, RENATO E. de; KABASHIN, ANDREI V.; RATIVA, DIEGOTitanium nitride (TiN) nanoparticles (NPs) look very promising for solar energy harvesting owing to a strong plasmonic absorption with the maximum in the near-infrared range. However, the synthesis of TiN nanofluids is very challenging as one has to combine the plasmonic feature and long-term colloidal stability to withstand harsh conditions of direct absorption solar collectors (DASC). Here, we explore solutions of bare (ligand free) TiN NPs synthesized by pulsed laser ablation in acetone as the nanofluid. We show that such NPs are low size-dispersed (mean size 25 nm) and exhibit a broad absorption peak around 700 nm, while their negative charge ensures a prolonged electrostatic stabilization of solutions. Solar weighted absorption coefficient of such TiN nanofluids reaches 95.7% at very low volume fractions (1.0 × 10−5), while nanofluid temperature can be increased up to 29 °C under 1.25-sun illumination. Our data evidence that the thermal efficiency of a DASC using TiN nanofluid is 80% higher compared to Au-based counterparts. The recorded high photothermal efficiency and excellent colloidal stability of TiN nanofluids promises a major advancement of DASC technology, while laser-ablative synthesis can offer easy scalability and relative cost-efficiency required for the implementation of systems for solar energy harvesting.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.