Navegação por Autores IPEN "FAROOQ, SAJID"

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  • IPEN-DOC 30192

    FAROOQ, SAJID ; GERMANO, GLEICE ; STANCARI, KLEBER A. ; RAFFAELI, ROCIO; CROCE, MARIA V.; CROCE, ADELA E.; ZEZELL, DENISE M. . A 3D discriminant analysis for hyperspectral FTIR images. In: INTERNATIONAL CONFERENCE ON OPTICAL MEMS AND NANOPHOTONICS; SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, July 31 - August 3, 2023, Campinas, SP. Proceedings... Piscataway, NJ, USA: IEEE, 2023. DOI: 10.1109/OMN/SBFOTONIOPC58971.2023.10230933

    Abstract: 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).

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  • IPEN-DOC 29572

    FAROOQ, SAJID ; DEL-VALLE, MATHEUS ; SANTOS, MOISES O. dos ; NASCIMENTO, SOFIA ; BERNARDES, EMERSON S. ; ZEZELL, DENISE M. . Breast cancer subtypes diagnostic via high performance supervised machine learning. In: INTERNATIONAL CONFERENCE ON CLINICAL SPECTROSCOPY, 12th, June 19-23, 2022, Dublin, Ireland. Abstract... 2022.

    Abstract: Aim: Breast cancer molecular subtypes are being used to improve clinical decision. The Fourier transform infrared (FTIR) spectroscopic imaging, which is a powerful and non-destructive technique, allows performing a non-perturbative and labelling free extraction of biochemical information towards diagnosis and evaluation for cell functionality. However, methods of measurements of large areas of cells demand a long time to achieve high quality images, making its clinical use impractical because of speed of data acquisition and dearth of optimized computational procedures. In order to cope with these challenges, Machine learning (ML) technologies can facilitate to obtain accurate prognosis of Breast Cancer (BC) subtypes with high action ability and accuracy. Methods: Here we propose a ML algorithm based method to distinguish computationally BC cell lines. The method is developed by coupling K neighbors Classifier (KNN) with Neighborhood Component Analysis (NCA) and NCA-KNN methods enables to identify BC subtypes without increasing model size as well additional parameters. Results: By incorporating FTIR imaging data, we show that using NCA-KNN method, the classification accuracies, specificities and sensitivities improve up to 97%, even at very low co-added scan (S_4). Moreover, a clear distinctive accuracy difference of our proposed method was obtained in comparison with other ML supervised models. Conclusion: For confirming our model results performance, the cross validation (k fold = 10) and receiver operation characteristics (ROC) curve were used and found in great agreement, suggest a potential diagnostic method for BC subtypes, even with small co-added scan < 8 at low spectral resolution (4 cm-1).

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  • IPEN-DOC 29364

    FAROOQ, SAJID ; CARAMEL-JUVINO, AMANDA ; FONTES, YASMIN R. ; GARDIANO, SABRINA A. ; ZEZELL, DENISE M. . Exploring enamel demineralization from SEM images using deep learning algorithms. In: LATIN AMERICA OPTICS AND PHOTONICS CONFERENCE, August 7-11, 2022, Recife, PE. Proceedings... Washington, DC, USA: Optica Publishing Group, 2022. DOI: 10.1364/LAOP.2022.W4A.36

    Abstract: Here, we employ segmentation and convolutional neural network (CNN) to identify and quantify enamel demineralization. Our results depict that CNN model using input SEM images achieve accuracy up to 79% for enamel demineralization diagnosis.

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  • IPEN-DOC 29000

    FAROOQ, SAJID ; RATIVA, DIEGO; SAID, ZAFAR; ARAUJO, RENATO E. de. High performance blended nanofluid based on gold nanorods chain for harvesting solar radiation. Applied Thermal Engineering, v. 218, p. 1-7, 2023. DOI: 10.1016/j.applthermaleng.2022.119212

    Abstract: Colloids composed of metallic nanoparticles are promising working fluids for solar radiation harvesting using Direct Absorption Solar Collectors (DASC), due to a high thermal conductivity characteristic and a broad optical absorption that can be tuned to match the solar spectrum. Recently, different studies report gold nanorod (Au-NR) chains for biosensing and photothermal applications, which have broadband and high absorption cross-section and potential possibilities to orientate the nanoparticle using electromagnetic fields. Moreover, colloids with nanoparticles blended configuration show an efficient solar radiation absorption characteristics. Here, working fluids for DASC based on gold nanorod chains in an unblended and blended configuration are evaluated using numerical simulations. The results indicate that the solar absorption increases proportional to the size of the Au-NR assembly, and the best configuration is obtained for a tetramer structure. By using different blended arrangements such as single Au monomers, dimers, trimmers, and tetramers nanorods, it is possible to obtain solar weighted absorption coefficients close to an ideal solar thermal collector, even obtained at low volume fraction (1×10(−5)). Moreover, the results show an enhancement of the temperature of 58.45 °C for tetramer compared with a monomer structure, both under one sun excitation. Therefore, the Au-NR assembly shows a high potentiality to be explored as a high-performance working fluid for solar thermal collectors.

    Palavras-Chave: colloids; plasmons; nanoparticles; solar collectors; thermal conductivity; gold

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  • IPEN-DOC 30188

    PERES, DANIELLA L. ; FAROOQ, SAJID ; RAFFAELI, ROCIO; CROCE, MARIA V.; CROCE, ADELA E.; ZEZELL, DENISE M. . Identification of basal cell carcinoma skin cancer using FTIR and Machine learning. In: INTERNATIONAL CONFERENCE ON OPTICAL MEMS AND NANOPHOTONICS; SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, July 31 - August 3, 2023, Campinas, SP. Proceedings... Piscataway, NJ, USA: IEEE, 2023. DOI: 10.1109/OMN/SBFOTONIOPC58971.2023.10230945

    Abstract: 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%.

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  • IPEN-DOC 29302

    CARAMEL-JUVINO, AMANDA ; FAROOQ, SAJID ; ROMANO, MARIANA; ZEZELL, DENISE M. . Identification of enamel demineralization using high performance convolutional neural network. In: SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, October 13-15, 2022, Recife, PE. Proceedings... Piscataway, NJ, USA: IEEE, 2022. DOI: 10.1109/SBFotonIOPC54450.2022.9992381

    Abstract: Here, we traces use segmentation and convolutional neural network (CNN) to trace, diagnose and quantify enamel demineralization for research. The preprocessing, histograms based methods are used to enhance the contrast and equalize the brightness through the scanning electron microscope images. Our result evidence that the deep learning based CNN model is highly efficient to process the dental image to achieve high accuracy of enamel demineralization and presents promising outcomes with optimal precision.

    Palavras-Chave: neural networks; dentistry; enamels; demineralization; learning

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  • IPEN-DOC 29360

    FAROOQ, SAJID ; DEL-VALLE, MATHEUS ; SANTOS, SOFIA ; BERNARDES, EMERSON S. ; ZEZELL, DENISE M. . Identifying breast cancer cell lines using high performance machine learning methods. In: LATIN AMERICA OPTICS AND PHOTONICS CONFERENCE, August 7-11, 2022, Recife, PE. Proceedings... Washington, DC, USA: Optica Publishing Group, 2022. DOI: 10.1364/LAOP.2022.Tu5A.3

    Abstract: We present a computational framework based on machine learning classifiers K-Nearest Neighbors and Neighborhood Component analysis for breast cancer (BC) subtypes prognostic. Our results has up to 97% accuracy for prognostic stratification of BC subtypes.

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  • IPEN-DOC 30186

    FAROOQ, SAJID ; PERES, DANIELLA L. ; CAIXETA, DOUGLAS C.; LIMA, CASSIO; SILVA, ROBINSON S. da; ZEZELL, DENISE M. . Monitoring changes in urine from diabetic rats using ATR-FTIR and Machine Learning. In: INTERNATIONAL CONFERENCE ON OPTICAL MEMS AND NANOPHOTONICS; SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, July 31 - August 3, 2023, Campinas, SP. Proceedings... Piscataway, NJ, USA: IEEE, 2023. DOI: 10.1109/OMN/SBFOTONIOPC58971.2023.10230957

    Abstract: 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%.

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  • IPEN-DOC 28859

    FAROOQ, SAJID ; WALI, FAIZ; ZEZELL, DENISE M. ; ARAUJO, RENATO E. de; RATIVA, DIEGO. Optimizing and quantifying gold nanospheres based on LSPR label-free biosensor for dengue diagnosis. Polymers, v. 14, n. 8, p. 1-12, 2022. DOI: 10.3390/polym14081592

    Abstract: The localized surface plasmon resonance (LSPR) due to light–particle interaction and its dependence on the surrounding medium have been widely manipulated for sensing applications. The sensing efficiency is governed by the refractive index-based sensitivity (ηRIS) and the full width half maximum (FWHM) of the LSPR spectra. Thereby, a sensor with high precision must possess both requisites: an effective ηRIS and a narrow FWHM of plasmon spectrum. Moreover, complex nanostructures are used for molecular sensing applications due to their good ηRIS values but without considering the wide-band nature of the LSPR spectrum, which decreases the detection limit of the plasmonic sensor. In this article, a novel, facile and label-free solution-based LSPR immunosensor was elaborated based upon LSPR features such as extinction spectrum and localized field enhancement. We used a 3D full-wave field analysis to evaluate the optical properties and to optimize the appropriate size of spherical-shaped gold nanoparticles (Au NPs). We found a change in Au NPs’ radius from 5 nm to 50 nm, and an increase in spectral resonance peak depicted as a red-shift from 520 nm to 552 nm. Using this fact, important parameters that can be attributed to the LSPR sensor performance, namely the molecular sensitivity, FWHM, ηRIS, and figure of merit (FoM), were evaluated. Moreover, computational simulations were used to assess the optimized size (radius = 30 nm) of Au NPs with high FoM (2.3) and sharp FWHM (44 nm). On the evaluation of the platform as a label-free molecular sensor, Campbell’s model was performed, indicating an effective peak shift in the adsorption of the dielectric layer around the Au NP surface. For practical realization, we present an LSPR sensor platform for the identification of dengue NS1 antigens. The results present the system’s ability to identify dengue NS1 antigen concentrations with the limit of quantification measured to be 0.07 μg/mL (1.50 nM), evidence that the optimization approach used for the solution-based LSPR sensor provides a new paradigm for engineering immunosensor platforms.

    Palavras-Chave: plasmons; resonance; surfaces; nanostructures; sensors; sensitivity; optimization

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  • IPEN-DOC 29788

    FAROOQ, SAJID ; DEL-VALLE, MATHEUS ; SANTOS, MOISES O. dos; SANTOS, SOFIA N. dos ; BERNARDES, EMERSON S. . Rapid identification of breast cancer subtypes using micro-FTIR and machine learning methods. Applied Optics, v. 62, n. 8, p. C80 - C87, 2023. DOI: 10.1364/AO.477409

    Abstract: 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.

    Palavras-Chave: diagnosis; diagnostic techniques; neoplasms; mammary glands; fourier transformation; infrared spectrometers; machine learning

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  • IPEN-DOC 29510

    PEDROSA, TULIO de L.; FAROOQ, SAJID ; ARAUJO, RENATO E. de. Selecting high-performance gold nanorods for photothermal conversion. Nanomaterials, v. 12, n. 23, p. 1-11, 2022. DOI: 10.3390/nano12234188

    Abstract: In this work, we establish a new paradigm on identifying optimal arbitrarily shaped metallic nanostructures for photothermal applications. Crucial thermo-optical parameters that rule plasmonic heating are appraised, exploring a nanoparticle size-dependence approach. Our results indicate two distinct figures of merit for the optimization of metallic nanoheaters, under both non-cumulative femtosecond and continuum laser excitation. As a case study, gold nanorods are evaluated for infrared photothermal conversion in water, and the influence of the particle length and diameter are depicted. For non-cumulative femtosecond pulses, efficient photothermal conversion is observed for gold nanorods of small volumes. For continuous wave (CW) excitation at 800 nm and 1064 nm, the optimal gold nanorod dimensions (in water) are, respectively, 90 × 25nm and 150 × 30 nm. Figure of Merit (FoM) variations up to 700% were found considering structures with the same peak wavelength. The effect of collective heating is also appraised. The designing of high-performance plasmonic nanoparticles, based on quantifying FoM, allows a rational use of nanoheaters for localized photothermal applications.

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  • IPEN-DOC 29715

    BALTAR, RAPHAEL M.S.M.; FAROOQ, SAJID ; ARAUJO, RENATO E. de. Selecting plasmonic nanoshells for colorimetric sensors. Journal of the Optical Society of America B, v. 40, n. 4, p. C40-C47, 2023. DOI: 10.1364/JOSAB.479446

    Abstract: In 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.

    Palavras-Chave: plasmons; nanostructures; colorimetric dosemeters; gold

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  • IPEN-DOC 29299

    BALTAR, RAPHAEL M.S.M.; ARAUJO, RENATO E. de; FAROOQ, SAJID . Selecting silver nanoshells for colorimetric sensors. In: SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, October 13-15, 2022, Recife, PE. Proceedings... Piscataway, NJ, USA: IEEE, 2022. DOI: 10.1109/SBFotonIOPC54450.2022.9992430

    Abstract: In this work the use of silver nanoshell as a starting point for the establishment of colorimetric sensor platforms, under solar illumination, was evaluated. Mie theory was explored on the analysis of the nanosensor linearity and sensitivity, considering 4 different color spaces and the influence of the nanoshell geometry. A high performance plasmonic nanoplatform was identified. The nanosensor platform based on nanoshells, with 35 nm SiO 2 core radius and 25 nm Ag shell thickness, showed sensitivity values up to 2.78 RIU -1 and linearity higher than 0.96, considering the Hue parameter of the HSV color space. The identification of optimized plasmonic nanoplatforms may extend the use naked-eye colorimetric applications in low-resource environments.

    Palavras-Chave: optical equipment; sensors; nanotechnology; plasmons; silver

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  • IPEN-DOC 29303

    FAROOQ, SAJID ; CARAMEL-JUVINO, AMANDA ; DEL-VALLE, MATHEUS ; SANTOS, SOFIA ; BERNARDES, EMERSON S. ; ZEZELL, DENISE M. . Superior Machine Learning Method for breast cancer cell lines identification. In: SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, October 13-15, 2022, Recife, PE. Proceedings... Piscataway, NJ, USA: IEEE, 2022. DOI: 10.1109/SBFotonIOPC54450.2022.9992467

    Abstract: We propose an artificial intelligence platform based on machine learning (ML) algorithm using Neighborhood Component analysis and K-Nearest Neighbors for breast cancer cell lines recognition. Our model presents up to 97% accuracy for identification of breast cancer cell lines.

    Palavras-Chave: neoplasms; mammary glands; tumor cells; machine learning; artificial intelligence; accuracy; identification systems

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  • IPEN-DOC 29086

    FAROOQ, SAJID ; SHAFIQUE, SHAREEN; AHSAN, ZISHAN; CARDOZO, OLAVO; WALI, FAIZ. Tailoring the scattering response of optical nanocircuits using modular assembly. Nanomaterials, v. 12, n. 17, p. 1-12, 2022. DOI: 10.3390/nano12172962

    Abstract: Owing to the localized plasmon resonance of an ensemble of interacting plasmonic nanoparticles (NPs), there has been a tremendous drive to conceptualize complex optical nanocircuits with versatile functionalities. In comparison to modern research, there is still not a sufficient level of sophistication to treat the nanostructures as lumped circuits that can be adjusted into complex systems on the basis of a metatronic touchstone. Here, we present the design, assembly, and characterization of single relatively complex photonic nanocircuits by accurately positioning several metallic and dielectric nanoparticles acting as modular lumped elements. In this research, Au NPs along with silica NPs were used to compare the proficiency and precision of our lumped circuit model analytically. On increasing the size of an individual Au NP, the spectral peak resonance not only modifies but also causes more scattering efficiency which increases the fringe capacitance linearly and decreases the nanoinductance of lumped circuit element. The NPs-based assembly induced the required spectral resonance ascribed by simple circuit methods and are depicted to be actively reconfigurable by tuning the direction or polarization of input signals. Our work demonstrates a vital step toward developing the modern modular designing tools of complex electronic circuits into nanophotonic-related applications.

    Palavras-Chave: plasmons; nanomaterials; elements; nanoparticles; electronic circuits

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  • IPEN-DOC 29718

    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, DIEGO. Thermo-optical performance of bare laser-synthesized TiN nanofluids for direct absorption solar collector applications. Solar Energy Materials and Solar Cells, v. 252, p. 1-10, 2023. DOI: 10.1016/j.solmat.2023.112203

    Abstract: Titanium 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.

    Palavras-Chave: nanofluids; titanium nitrides; plasmons; solar energy; lasers; ablation

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A elaboração do projeto do RI do IPEN foi iniciado em novembro de 2013, colocado em operação interna em julho de 2014 e disponibilizado na Internet em junho de 2015. Utiliza o software livre Dspace, desenvolvido pelo Massachusetts Institute of Technology (MIT). Para descrição dos metadados adota o padrão Dublin Core. É compatível com o Protocolo de Arquivos Abertos (OAI) permitindo interoperabilidade com repositórios de âmbito nacional e internacional.

O gerenciamento do Repositório está a cargo da Biblioteca do IPEN. Constam neste RI, até o presente momento 20.950 itens que tanto podem ser artigos de periódicos ou de eventos nacionais e internacionais, dissertações e teses, livros, capítulo de livros e relatórios técnicos. Para participar do RI-IPEN é necessário que pelo menos um dos autores tenha vínculo acadêmico ou funcional com o Instituto. Nesta primeira etapa de funcionamento do RI, a coleta das publicações é realizada periodicamente pela equipe da Biblioteca do IPEN, extraindo os dados das bases internacionais tais como a Web of Science, Scopus, INIS, SciElo além de verificar o Currículo Lattes. O RI-IPEN apresenta também um aspecto inovador no seu funcionamento. Por meio de metadados específicos ele está vinculado ao sistema de gerenciamento das atividades do Plano Diretor anual do IPEN (SIGEPI). Com o objetivo de fornecer dados numéricos para a elaboração dos indicadores da Produção Cientifica Institucional, disponibiliza uma tabela estatística registrando em tempo real a inserção de novos itens. Foi criado um metadado que contém um número único para cada integrante da comunidade científica do IPEN. Esse metadado se transformou em um filtro que ao ser acionado apresenta todos os trabalhos de um determinado autor independente das variáveis na forma de citação do seu nome.