Literature DB >> 32012556

A versatile and compact surface plasmon resonance spectrometer based on single board computer.

A Vestri1, G Margheri2, E Landini3, E Meacci1, B Tiribilli2.   

Abstract

The widespread diffusion of low-cost but high-performance hardware is enhancing the realization of scientific equipment with features at the research laboratory level. In this paper, we demonstrate hardware implementation of a surface plasmon resonance compact device with high accuracy and measurement times appropriate for many applications. Image acquisition is realized by a Raspberry Pi single board computer with a camera module, and a Python code is used to process data. A flexible optical setup can work in two different configurations, namely, the inspection mode and angle resolved measurement mode. The inspection mode is used to precisely locate the light-emitting diode interrogation beam on the sample, avoiding uneven or faulty regions. The measurement mode allows us to monitor in real time the position of the minimum reflectivity with subpixel resolution. Performance tests show a resolution in the bulk refractive index of 4.9 × 10-6 refractive index units for 10 s acquisition time.

Year:  2020        PMID: 32012556     DOI: 10.1063/1.5111829

Source DB:  PubMed          Journal:  Rev Sci Instrum        ISSN: 0034-6748            Impact factor:   1.523


  3 in total

1.  Development of a Portable SPR Sensor for Nucleic Acid Detection.

Authors:  Yafeng Huang; Lulu Zhang; Hao Zhang; Yichen Li; Luyao Liu; Yuanyuan Chen; Xianbo Qiu; Duli Yu
Journal:  Micromachines (Basel)       Date:  2020-05-21       Impact factor: 2.891

2.  SERS Biosensor Based on Engineered 2D-Aperiodic Nanostructure for In-Situ Detection of Viable Brucella Bacterium in Complex Matrix.

Authors:  Massimo Rippa; Riccardo Castagna; Domenico Sagnelli; Ambra Vestri; Giorgia Borriello; Giovanna Fusco; Jun Zhou; Lucia Petti
Journal:  Nanomaterials (Basel)       Date:  2021-03-31       Impact factor: 5.076

3.  Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications.

Authors:  Alessandro Tonelli; Veronica Mangia; Alessandro Candiani; Francesco Pasquali; Tiziana Jessica Mangiaracina; Alessandro Grazioli; Michele Sozzi; Davide Gorni; Simona Bussolati; Annamaria Cucinotta; Giuseppina Basini; Stefano Selleri
Journal:  Sensors (Basel)       Date:  2021-05-20       Impact factor: 3.576

  3 in total

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