Compact electrostatic beam optics for multi-element focused ion beams: simulation and experiments

Electrostatic beam optics for a multi-element focused ion beam (MEFIB) system comprising of a microwave multicusp plasma (ion) source is designed with the help of two widely known and commercially available beam simulation codes: AXCEL-INP and SIMION. The input parameters to the simulations are obtained from experiments carried out in the system. A single and a double Einzel lens system (ELS) with and without beam limiting apertures (S) have been investigated. For a 1 mm beam at the plasma electrode aperture, the rms emittance of the focused ion beam is found to reduce from ～0.9 mm mrad for single ELS to ～0.5 mm mrad for a double ELS, when S of 0.5 mm aperture size is employed. The emittance can be further improved to ～0.1 mm mrad by maintaining S at ground potential, leading to reduction in beam spot size (～10 μm). The double ELS design is optimized for different electrode geometrical parameters with tolerances of ±1 mm in electrode thickness, electrode aperture, inter electrode distance, and ±1° in electrode angle, providing a robust design. Experimental results obtained with the double ELS for the focused beam current and spot size, agree reasonably well with the simulations.     

"Flow valve" microfluidic devices for simple, detectorless, and label-free analyte quantitation

Simplified analysis systems that offer the performance of benchtop instruments but the convenience of portability are highly desirable. We have developed novel, miniature devices that feature visual inspection readout of a target's concentration from a ～1 μL volume of solution introduced into a microfluidic channel. Microchannels are constructed within an elastomeric material, and channel surfaces are coated with receptors to the target. When a solution is flowed into the channel, the target cross-links multiple receptors on the surface, resulting in constriction of the first few millimeters of the channel and stopping of flow. Quantitation is performed by measuring the distance traveled by the target solution in the channel before flow stops. A key advantage of our approach is that quantitation is accomplished by simple visual inspection of the channel, without the need for complex detection instrumentation. We have tested these devices using the model system of biotin as a receptor and streptavidin as the target. We have also characterized three factors that influence flow distance: solution viscosity, device thickness, and channel height. We found that solution capillary flow distance scales with the negative logarithm of target concentration and have detected streptavidin concentrations as low as 1 ng/mL. Finally, we have identified and evaluated a plausible mechanism wherein time-dependent channel constriction in the first few millimeters leads to concentration-dependent flow distances. Their simplicity coupled with performance makes these "flow valve" systems especially attractive for a host of analysis applications.     

A secure data analytics scheme for multimedia communication in a decentralized smart grid

With the exponential increase in energy demands (commercial as well as residential), the traditional grid infrastructure significantly shifted to intelligent ICT-based Smart Grid (SG) infrastructure. In the SG environment, only efficient energy management may not be sufficient as the SG dynamics have significant impacts on multimedia communications such as video surveillance of the technical/non-technical losses of energy and many more. The inevitable energy losses can be identified by process and analyze the massive amount of heterogeneous data, i.e., Big Data (BD) generated through smart devices such as sensors, Smart Meters (SMs), and others. The key challenges in analyzing multimedia BD are computational complexity, operational integration complexity, data security, and privacy. To overcome the aforementioned issues, this paper proposes a blockchain-based data analytics scheme called ChoIce, which offers secure data collection, analysis, and decision support for the SG systems. It works in two phases; (i) secure data collection over Ethereum and (ii) BD analytics and decision-making using deep learning (DL). The robust and secure data analytics, efficient network management, and high-performance computing for BD are crucial towards the optimization of SG operation. The performance of ChoIce is evaluated considering parameters such as the data storage cost, multimedia communication latency, and prediction accuracy. Thus, the results of ChoIce shows that it outperforms in contrast to other state-of-the-art approaches.