No abstract

A new class of multifunctional silica nanoparticles carrying boron clusters (10-vertex closo-decaborate) and incorporating luminescent centers (fluorescein) has been developed as potential probes/carriers for potential application in boron neutron capture therapy (BNCT). These silica nanoparticles were chargedin situwith silylated-fluorescein fluorophoresviathe Stöber method and their surface was further functionalized with decaborate-triethoxysilane precursors. The resulting decaborate dye-doped silica nanoparticles were characterized by TEM, solid state NMR, DLS, nitrogen sorption, elemental analysis, and fluorescence spectroscopy.

Event based neural networks are currently being explored as efficient solutions for performing AI task at the extreme edge. To fully exploit their potential, event based neural networks coupled to adequate data pre-processing must be investigated. Within this context, we demonstrate a 4-bit-weight Spiking Neural Network (SNN) for radar gesture recognition is presented, achieving state-of-the-art 93% accuracy within only 4 processing time-steps while using only one convolutional layer and two fully-connected layers. This consumes very little energy and area if implemented in event-based hardware, which makes it suited for embedded extreme-edge applications. In addition, we demonstrate the importance of data pre-processing for achieving a high recognition accuracy in SNNs compared to Deep Neural Networks (DNNs) with the same network topology and training strategy. We show that efficient pre-processing prior to the neural network is drastically more important for SNNs compared to DNNs. We demonstrate for the first time that pre-processing parameters can affect SNNs and DNNs in antagonistic ways, prohibiting the generalization of conclusions drawn from DNN design to SNNs. We demonstrate our findings by rigorously comparing gesture recognition accuracy achieved with our SNN to a DNN with the same architecture and similar training. Unlike previously proposed neural networks for radar processing, this work enables ultra-low-power radar-based gesture recognition for extreme-edge devices.

The chemical reaction of rhodamine B by electro-generated species using Pt and BDD electrodes was performed. The product(s) of this chemical reaction are related to the supporting electrolyte and electrolysis time. The rate of discoloration is affected by the current density, initial pH, temperature, and the nature of the supporting electrolyte. However, the initial dye concentration and the ionic strength did not show any significant effect on both electrodes. Discoloration of the dye and mineralization were not observed in presence of sulfate and nitrate with the Pt electrode, but occurred slowly with the BDD electrode. In the presence of KCl and KBr, the discoloration was very fast with both electrodes, and was accompanied with partial degradation. In the presence of KCl, the colorless rhodamine B solution turned rose after several hours of being set at rest.