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The integration of MEMS sensors and actuators onto macro mechanical parts is a critical technology necessary for the potential realization of intelligent mechanical structures. The current planar fabrication methods offered by the MEMS/IC industry restrict the possibility of integrating micro devices onto contoured (non-planar) mechanical structures. We have developed a lithographic technique to directly fabricate micron-sized sensing and actuation devices onto a cylindrical surface. This novel technology encompasses the development of flexible masks, photoresist spraying technique, and customized alignment systems. Results indicate that line resolution of

A physical model is derived to create a biological cell separator that is based on controlling the entropy in a microfluidic system having conjoined spherical structures. A one-dimensional simplified model of this three-dimensional problem in terms of the corresponding effects of entropy on the Brownian motion of particles is presented. This dynamic mechanism is based on the Langevin equation from statistical thermodynamics and takes advantage of the characteristics of the Fokker-Planck equation. This mechanism can be applied to manipulate biological particles inside a microfluidic system with identical, conjoined, spherical compartments. This theoretical analysis is verified by performing a rapid and a simple technique for separating yeast cells in these conjoined, spherical microfluidic structures. The experimental results basically match with our theoretical model and we further analyze the parameters which can be used to control this separation mechanism. Both numerical simulations and experimental results show that the motion of the particles depends on the geometrical boundary conditions of the microfluidic system and the initial concentration of the diffusing material. This theoretical model can be implemented in future biophysics devices for the optimized design of passive cell sorters. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have been widely used for intelligent building management applications. Typically, indoor environment parameters such as illumination, temperature, humidity and air quality are monitored and adjusted by an intelligent building management system. However, owing to the short life-span of the batteries used at the sensor nodes, the maintenance of such systems has been labor-intensive and time-consuming. This paper discusses a battery-less self-powering system that converts the mechanical energy from the airflow in ventilation ducts into electrical energy. The system uses a flutter energy conversion device (FECD) capable of working at low airflow speeds while installed on the ventilation ducts inside of buildings. A power management strategy implemented with a circuit system ensures sufficient power for driving commercial electronic devices. For instance, the power management circuit is capable of charging a 1 F super capacitor to 2 V under ventilation duct airflow speeds of less than 3 m/s. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm for hand gesture tracking and recognition based on the integration of a custom-built microelectromechanical systems (MEMS)-based inertial sensor (or measurement unit) and a low resolution imaging (i.e., vision) sensor. We discuss the 2-D gesture recognition and tracking results here, but the algorithm can be extended to 3-D motion tracking and gesture recognition in the future. Essentially, this paper shows that inertial data sampled at 100 Hz and vision data at 5 frames/s could be fused by an extended Kalman filter, and used for accurate human hand gesture recognition and tracking. Since an inertial sensor is better at tracking rapid movements, while a vision sensor is more stable and accurate for tracking slow movements, a novel adaptive algorithm has been developed to adjust measurement noise covariance according to the measured accelerations and the angular rotation rates. The experimental results verify that the proposed method is capable of reducing the velocity error and position drift in an MEMS-based inertial sensor when aided by the vision sensor. Compensating for the time delay due to the visual data processing cycles, a moving average filter is applied to remove the high frequency noise and propagate the inertial signals. The reconstructed trajectories of the first 10 Arabic numerals are further recognized using dynamic time warping with a direct cosine transform for feature extraction, resulting in an accuracy of 92.3% and individual numeral recognition within 100 ms. [ABSTRACT FROM PUBLISHER]

Antiviral vaccine is essential for preventing and controlling virus spreading, along with declining morbidity and mortality. A major challenge in effective vaccination lies in the ability to enhance both the humoral and cellular immune responses by adjuvants. Herein, self-assembled nanoparticles based on graphene oxide quantum dots with components of carnosine, resiquimod and Zn 2+ ions, namely ZnGC-R, are designed as a new adjuvant for influenza vaccine. With its high capability for antigen-loading, ZnGC-R enhances antigen utilization, improves DC recruitment, and activates antigen-presenting cells. Single cell analysis of lymphocytes after intramuscular vaccination revealed that ZnGC-R generated multifaceted immune responses. ZnGC-R stimulated robust CD4 + CCR7 lo PD-1 hi Tfh and durable CD8 + CD44 hi CD62L - T EM immune responses, and simultaneously promoted the proliferation of CD26 + germinal center B cells. Besides, ZnGC-R elicited 2.53-fold higher hemagglutination-inhibiting antibody than commercial-licensed aluminum salt adjuvant. ZnGC-R based vaccine induced 342% stronger IgG antibody responses compared with vaccines with inactivated virus alone, leading to 100% in vivo protection efficacy against the H1N1 influenza virus challenge., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)