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About

My academic path started with a graduation in Physics Engineering (IST/Lisbon), followed by an MSc in Electrotechnical Eng. and Computers (IST/Lisbon), and a PhD in Physics /FCUP/UPorto). Along the way, I participated in different projects that had, as a common feature,  the development of new solutions or approaches in  Applied Optics  (LIDAR measuring systems, biosensors, fiber optic sensing, high resolution optical imaging, optical coherence tomography). As a lecturer at UPorto, I have been mostly envolved in the Physics Eng. and Medical Physics MSc programs. At the Center of Applied Photonics (INESC TEC) I have found the place/team to  develop my research in applied optics, looking for solutions for academic challenges, but most importantly for the challenges we receive from industrial partners, seeking for new measuring approaches using light as a probing agent. 

Interest
Topics
Details

Details

  • Nationality

    Portugal
  • Centre

    Applied Photonics
  • Contacts

    +351220402301
    carla.c.rosa@inesctec.pt
005
Publications

2021

Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers

Authors
Carvalho, IA; Silva, NA; Rosa, CC; Coelho, LCC; Jorge, PAS;

Publication
Sensors

Abstract
The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.

2019

Optical fiber-based sensing method for nanoparticle detection through supervised back-scattering analysis: a potential contributor for biomedicine

Authors
Paiva, JS; Jorge, PAS; Ribeiro, RSR; Sampaio, P; Rosa, CC; Cunha, JPS;

Publication
International Journal of Nanomedicine

Abstract

2019

Optical fiber-based sensing method for nanoparticles detection through back-scattering signal analysis

Authors
Paiva, JS; Ribeiro, RSR; Jorge, PAS; Rosa, CC; Sampaio, P; Cunha, JPS;

Publication
Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Abstract
In view of the growing importance of nanotechnologies, the detection of nanoparticles type in several contexts has been considered a relevant topic. Several organisms, including the National Institutes of Health, have been highlighting the urge of developing nanoparticles exposure risk assessment assays, since very little is known about their physiological responses. Although the identification/characterization of synthetically produced nanoparticles is considered a priority, there are many examples of "naturally" generated nanostructures that provide useful information about food components or human physiology. In fact, several nanoscale extracellular vesicles are present in physiological fluids with high potential as cancer biomarkers. However, scientists have struggled to find a simple and rapid method to accurately detect/identify nanoparticles, since their majority have diameters between 100-150 nm-far below the diffraction limit. Currently, there is a lack of instruments for nanoparticles detection and the few instrumentation that is commonly used is costly, bulky, complex and time consuming. Thus, considering our recent studies on particles identification through back-scattering, we examined if the time/frequency-domain features of the back-scattered signal provided from a 100 nm polystyrene nanoparticles suspension are able to detect their presence only by dipping a polymeric lensed optical fiber in the solution. This novel technique allowed the detection of synthetic nanoparticles in distilled water versus "blank solutions" (only distilled water) through Multivariate Statistics and Artificial Intelligence (AI)-based techniques. While the state-of-The-Art methods do not offer affordable and simple approaches for nanoparticles detection, our technique can contribute for the development of a device with innovative characteristics. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

2018

Optical Fiber Tips for Biological Applications: from Light Confinement, Biosensing to Bioparticles Manipulation

Authors
Paiva, JS; Jorge, PAS; Rosa, CC; Cunha, JPS;

Publication
Biochimica et Biophysica Acta (BBA) - General Subjects

Abstract

2018

Single Particle Differentiation through 2D Optical Fiber Trapping and Back-Scattered Signal Statistical Analysis: An Exploratory Approach

Authors
Paiva, JS; Ribeiro, RSR; Cunha, JPS; Rosa, CC; Jorge, PAS;

Publication
Sensors

Abstract

Supervised
thesis

2021

Analytical Tweezers for cell manipulation and diagnostic

Author
Inês Alves Carvalho

Institution
UP-FCUP

2021

Preliminary research on machine learning for X-ray CT calibration in proton therapy

Author
Lourival Beltrão Martins Júnior

Institution
UP-FCUP

2021

Fabrication of Optofluidic Systems by Femtosecond Laser Micromachining

Author
João Miguel Mendes da Silva Maia

Institution
UP-FCUP

2019

Fabrication of Optofluidic Systems by Femtosecond Laser Micromachining

Author
João Miguel Mendes da Silva Maia

Institution
UP-FCUP

2018

Lensless diffraction limited imaging with optical fibers

Author
Rui Luís Vieira Oliveira

Institution
UP-FCUP