Optimizing sensor-devices based on individual color centers in diamond

2019 ◽  
Author(s):  
Richard Nelz ◽  
Philipp Fuchs ◽  
Michel Challier ◽  
Abdallah Slablab ◽  
Mariusz Radtke ◽  
...  
Keyword(s):  
Author(s):  
Makoto Motoyoshi ◽  
Hirofumi Nakamura ◽  
Manabu Bonkohara ◽  
Mitsumasa Koyanagi
Keyword(s):  

ACS Photonics ◽  
2021 ◽  
Author(s):  
Prince Khatri ◽  
Ralph Nicholas Edward Malein ◽  
Andrew J. Ramsay ◽  
Isaac J. Luxmoore

Photonics ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 70
Author(s):  
Maria Raposo ◽  
Carlota Xavier ◽  
Catarina Monteiro ◽  
Susana Silva ◽  
Orlando Frazão ◽  
...  

Thin graphene oxide (GO) film layers are being widely used as sensing layers in different types of electrical and optical sensor devices. GO layers are particularly popular because of their tuned interface reflectivity. The stability of GO layers is fundamental for sensor device reliability, particularly in complex aqueous environments such as wastewater. In this work, the stability of GO layers in layer-by-layer (LbL) films of polyethyleneimine (PEI) and GO was investigated. The results led to the following conclusions: PEI/GO films grow linearly with the number of bilayers as long as the adsorption time is kept constant; the adsorption kinetics of a GO layer follow the behavior of the adsorption of polyelectrolytes; and the interaction associated with the growth of these films is of the ionic type since the desorption activation energy has a value of 119 ± 17 kJ/mol. Therefore, it is possible to conclude that PEI/GO films are suitable for application in optical fiber sensor devices; most importantly, an optical fiber-based interrogation setup can easily be adapted to investigate in situ desorption via a thermally stimulated process. In addition, it is possible to draw inferences about film stability in solution in a fast, reliable way when compared with the traditional ones.


2021 ◽  
pp. 1-5
Author(s):  
Max A. Little

Parkinson’s disease is a complex and heterogeneous condition, and there are many gaps in the medical community’s scientific and practical understanding of the disease. Closing these gaps relies on objective data about symptoms and signs, collected over long durations. Smartphones contain sensor devices which can be used to remotely capture behavioral signals. From these signals, computational algorithms can distill metrics of symptom severity and progression. This brief review introduces the main concepts of the discipline, addressing the experimental, hardware and software logistics, and computational analysis. The article finishes with an exploration of future prospects for the technology.


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