Image patterned molecular delivery into live cells using gold particle coated substrates.

An image-patterned molecular delivery system for mammalian cells is demonstrated by pulsed laser irradiation of gold particles immobilized on a substrate below a cell monolayer. Patterned cavitation bubble nucleation was captured using a time-resolved imaging system and molecular delivery verified by observing the uptake of a membrane-impermeable fluorescent dye, calcein. Delivery efficiency as high as 90% was observed and multiplexed, patterned dye delivery was demonstrated

Optimization of Demand Response and Power-Sharing in Microgrids for Cost and Power Losses

The number of microgrids within a smart distribution grid can be raised in the future. Microgrid-based distribution network reconfiguration is analyzed in this research by taking demand response programs and power-sharing into account to optimize costs and reduce power losses. The suggested method determined the ideal distribution network configuration to fulfil the best scheduling goals. The ideal way of interconnecting switches between microgrids and the main grid was also identified. For each hour of operation, the ideal topology of microgrid-based distribution networks was determined using optimal power flow. The results were produced with and without the use of a demand response program and power-sharing in each microgrid. Different load profiles, such as residential, industrial, commercial, and academic, were taken into account and modified using appropriate demand response programs and power-sharing using the Artificial Bee Colony algorithm. Various scenarios were explored independently to suit the diverse aims considered by the distribution network operator for improved observation. The ABC optimization in this research attempted to reduce the system’s total operation costs and power losses through efficient networked microgrid reconfiguration. The results of optimal microgrid topology revealed the effects of power-sharing and demand response (TOU) programs. The results obtained in the proposed idea shows that costs were reduced by 8.3% and power losses were reduced by 4%. The IEEE 33-bus test system was used to demonstrate the effectiveness of the proposed approach

Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end

Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end

Floating electrode optoelectronic tweezers: Light-driven dielectrophoretic droplet manipulation in electrically insulating oil medium

We report an optical actuation mechanism, floating electrode optoelectronic tweezers (FEOET). FEOET enables light-driven transport of aqueous droplets immersed in electrically insulating oil on a featureless photoconductive glass layer with direct optical images. We demonstrate that a 681 μm de-ionized water droplet immersed in corn oil medium is actuated by a 3.21 μW laser beam with an average intensity as low as 4.08 μW∕mm2 at a maximum speed of 85.1 μm∕s on a FEOET device. FEOET provides a promising platform for massively parallel droplet manipulation with optical images on low cost, silicon-coated glass. The FEOET device structure, fabrication, working principle, numerical simulations, and operational results are presented in this letter