Single particle differentiation through 2d optical fiber trapping and back-scattered signal statistical analysis: An exploratory approach

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

Joana S. Paiva (Autor) Outra; Rita S. R. Ribeiro (Autor) Outra; João P. S. Cunha (Autor) FEUP; Carla C. Rosa (Autor) FCUP; Pedro A. S. Jorge (Autor) FCUP

Revista

Título: Sensors

Vol. 18 Nº 3

Páginas: 1-30

ISSN: 1424-3210

Editora: MDPI

Indexação

ISI Web of Knowledge - 13 Citações

ISI Web of Science

Scopus - 20 Citações

INSPEC

Outras Informações

ID Authenticus: P-00N-MDQ

DOI: 10.3390/s18030710

Abstract (EN): Recent trends on microbiology point out the urge to develop optical micro-tools with multifunctionalities such as simultaneous manipulation and sensing. Considering that miniaturization has been recognized as one of the most important paradigms of emerging sensing biotechnologies, optical fiber tools, including Optical Fiber Tweezers (OFTs), are suitable candidates for developing multifunctional small sensors for Medicine and Biology. OFTs are flexible and versatile optotools based on fibers with one extremity patterned to form a micro-lens. These are able to focus laser beams and exert forces onto microparticles strong enough (piconewtons) to trap and manipulate them. In this paper, through an exploratory analysis of a 45 features set, including time and frequency-domain parameters of the back-scattered signal of particles trapped by a polymeric lens, we created a novel single feature able to differentiate synthetic particles (PMMA and Polystyrene) from living yeasts cells. This single statistical feature can be useful for the development of label-free hybrid optical fiber sensors with applications in infectious diseases detection or cells sorting. It can also contribute, by revealing the most significant information that can be extracted from the scattered signal, to the development of a simpler method for particles characterization (in terms of composition, heterogeneity degree) than existent technologies. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 30