Optimization of a portable nanoparticle detection device
Article Sidebar
Main Article Content
Abstract
The use of silver nanoparticles has grown in both the industrial and medical areas. Along its widespread use, interest in understanding its effects on the human body and the environment has also increased. As a result, novel and improved techniques and tools are being investigated to characterize this material. The neuroelectronics group at the Technical University of Munich is developing a portable device for detecting silver nanoparticles in-situ. To achieve this, using cell phone technology to communicate with the device is anticipated, as well as making the necessary calculations for the characterization of these nanoparticles. The system consists of analog and digital circuits which still require performance and portability optimizations, in such a way that they contribute to the successful completion of this task. This article will describe three phases for achieving device optimization. The first phase includes the design and implementation of a power management system, which will allow the portability of the sensor with an operating time of at least one hour. The second phase will be about noise characterization and reduction in the analog system. The third and last phase will focus on verifying the cutoff frequency of the circuit. In addition, this article will explain how these three phases relate to each other and contribute to optimizing the silver nanoparticle detection device.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Los autores conservan los derechos de autor y ceden a la revista el derecho de la primera publicación y pueda editarlo, reproducirlo, distribuirlo, exhibirlo y comunicarlo en el país y en el extranjero mediante medios impresos y electrónicos. Asimismo, asumen el compromiso sobre cualquier litigio o reclamación relacionada con derechos de propiedad intelectual, exonerando de responsabilidad a la Editorial Tecnológica de Costa Rica. Además, se establece que los autores pueden realizar otros acuerdos contractuales independientes y adicionales para la distribución no exclusiva de la versión del artículo publicado en esta revista (p. ej., incluirlo en un repositorio institucional o publicarlo en un libro) siempre que indiquen claramente que el trabajo se publicó por primera vez en esta revista.
References
Chitra, K. & Annadurai, G. Antibacterial activity of pH-dependent biosynthesized silver nanoparticles against
clinical pathogen. BioMed Research International 2014, 6 (2014). URL https://doi.org/10.1155/2014/725165
Sambale, F. et al. Investigations of the toxic effect of silver nanoparticles on mammalian cell lines. J.
Nanomaterials 16, 6:6–6:6 (2015). URL https://doi.org/10.1155/2015/136765.
Krause, K. J., Yakushenko, A. & Wolfrum, B. Stochastic on-chip detection of subpicomolar concentrations of
silver nanoparticles. Analytical Chemistry 87, 7321–7325 (2015). URL https://pubs.acs.org/doi/10.1021/acs.
analchem.5b01478.
Mohammadi Bardbori, A., Korani, M., Ghazizadeh, E., Korani, S. & Hami, Z. Effects of silver nanoparticles on
human health 7, 51–62 (2015). URL https://doi.org/10.1515/ejnm-2014-0032
Sokolov, S. V., Eloul, S., Kätelhön, E., Batchelor-McAuley, C. & Compton, R. G. Electrode-particle impacts: a
users guide. Phys. Chem. Chem. Phys.19, 28–43 (2017). URL http://dx.doi.org/10.1039/C6CP07788A.
Grob, L. Design and Implementation of a Miniaturised Bipotentiostat. Master thesis (2015).
Kumsa, D. W. et al. Electron transfer processes occurring on platinum neural stimulating electrodes: a tutorial
on the i(Ve) profile. Journal of Neural Engineering 13, 052001 (2016). URL http://stacks.iop.org/1741-2552/13/
i=5/a=052001.
Shuang, T. H. & G., C. R. “Nano-impacts”: An electrochemical technique for nanoparticle sizing in optically opaque solutions. ChemistryOpen 4, 261–263. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/
open.201402161.
Kanokkanchana, K., Saw, E. N. & Tschulik, K. Nano-impact electrochemistry: Effects of electronic filtering on
peak height, duration, and area (2018). URL https://doi.org/10.1002/celc.201800738.
Pini, A. Analyze noise with time, frequency and statistics (2017).
Smith, S. W. Statistics, Probability and Noise. In technical Publishin, California (ed.) The Scientist and Engineer’s
Guide to Digital Signal Processing Statistics, Probability and Noise, chap. 2, 664 (San Diego, California, 1999),
second edn.
Kay, A. Operational Amplifier Noise: Techniques and Tips for Analyzing and Reducing Noise. January (2012),
first edn.
Teel, J. C. Understanding noise in linear regulators 5–8 (2005).
Keysight Technologies. Spectrum Analysis Basics 1–89.
Renterghem, K. V. How to measure LDO noise (2015).
Gamry Instruments. The Faraday Cage : What Is It ? How Does It Work ? 1–3 (2010).
Sadiku, M. N. O. Elements of Electromagnetics (Oxford, United Kingdom, 2000), 3 edn.
Kumar, A. & Madaan, P. Top 10 EMC Design Considerations. Cypress Semiconductor 4 (2011).
Roy, R. V. Power Management Introduction 1–11 (2014).
Texas Instruments. Understanding Boost Power Stages in Switchmode Power Supplies: Application Report (1999).
Texas Instruments. Understanding Buck Power Stages in Switchmode Power Supplies: Application Report (1999).
Day, M. Understanding Low Drop Out (LDO) Regulators (2002).
Renterghem, K. V. How to measure LDO noise (2015).