We report on the synthesis of dopamine loaded magnetic nanoparticles (MNPs) for new, simple, fast and repeatable extraction of ochratoxin A from different solvents and milk without utilizing immunoaffinity columns and even high-tech devices. To this end, Fe 3 O 4 nanoparticles (NPs) were synthesized using thermal decomposition reaction and dopamine (DPA) was then conjugated with Fe 3 O 4 nanoparticles (NPs) to form Fe 3 O 4 -DPA NPs. Dynamic light scattering, field emission scanning electron microscopy and transmission electron microscopy revealed an average size of about 15 nm for Fe 3 O 4 -DPA NPs. Moreover, zeta potential measurement and vibrating sample magnetometer confirmed positively charged (16.8 mV) and superparamagnetic behavior of MNPs, which are effective factors for a good adsorbent in the extraction. Various solvents and different effective parameters were measured until acetonitrile:methanol was selected as the best extraction solvent. In addition, based on the pH-partition theory, with changes in pH, we were able to increase and enhance the extraction rate to 94%. Moreover, the ability of Fe 3 O 4 -DPA NPs in solid phase extraction of ochratoxin A from spiked milk was evaluated. The recovery rate for extraction of OTA from milk was 68%.
The aim of this paper is to show the problems of implementing the wireless adaptive networks with the free space optical (FSO) technology. Implementing adaptive networks with the wireless optical communication technology has several benefits and also some hindering problems. The thermal optical noise modeled with Gaussian distribution and link turbulence is two of the major problems of this implementation. In this paper, the theoretical analysis of the FSO link effects that are modeled with K-distribution and Negative exponential distributions are considered on the estimation performance of the adaptive incremental networks. These distributions arise when the FSO link is contaminated with strong optical turbulence. Experiments are designed to cover these conditions and the analysis is based on the steady state mean square deviation (MSD) and excess mean square error (EMSE) values for the incremental LMS (ILMS) algorithm and these are the metrics that show how well the adaptive network performs. Simulation results are presented for different parameters of K -distribution and negative exponential distribution and the results show perfect match with the theoretical outcomes. Based on these results, we show that implementing the incremental adaptive networks in the strong turbulence conditions is not feasible and we must think of some countermeasures for these cases.
INDEX TERMSFree space optical communications, distributed processing, negative exponential distribution, adaptive networks, strong turbulence, estimation. JUAN WANG (S'19) is currently pursuing the M.Sc. degree in communication and information engineering with the Nanjing University of Posts and Telecommunications, Nanjing, China. Her research interest includes machine learning for wireless communications.
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