SAJID FAROOQ
20 resultados
Resultados de Busca
Agora exibindo 1 - 10 de 20
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 30192 A 3D discriminant analysis for hyperspectral FTIR images2023 - FAROOQ, SAJID; GERMANO, GLEICE; STANCARI, KLEBER A.; RAFFAELI, ROCIO; CROCE, MARIA V.; CROCE, ADELA E.; ZEZELL, DENISE M.Here, we apply a 3D discriminant analysis approach to analyze FTIR hyperspectral images of normal vs malignant Melanoma (MM) samples for skin cancer diagnosis. For this porpose we used 2 samples, for Normal (49k) and for MM(90k). Our results evidence the outstanding performance with accuracy up to 81% for big data (> 100k).Artigo IPEN-doc 30188 Identification of basal cell carcinoma skin cancer using FTIR and Machine learning2023 - PERES, DANIELLA L.; FAROOQ, SAJID; RAFFAELI, ROCIO; CROCE, MARIA V.; CROCE, ADELA E.; ZEZELL, DENISE M.Here we applied ATR-FTIR spectroscopy combined with computational modeling based on 3D-discriminant analysis (3D-PCA-QDA). Our results present an exceptional performance of 3D-discriminant algorithms to diagnose BCC skin cancer, indicating the accuracy up to 99%.Artigo IPEN-doc 30186 Monitoring changes in urine from diabetic rats using ATR-FTIR and Machine Learning2023 - FAROOQ, SAJID; PERES, DANIELLA L.; CAIXETA, DOUGLAS C.; LIMA, CASSIO; SILVA, ROBINSON S. da; ZEZELL, DENISE M.Here, we aim to better characterize diabetes mellitus (DM) by analyzing 149 urine spectral samples, comprising of diabetes versus healthy control groups employing ATR-FTIR spectroscopy, combined with a 3D discriminant analysis machine learning approach. Our results depict that the model is highly precise with accuracy close to 100%.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.