Flexible tactile sensors are garnering substantial interest for various promising applications, including artificial intelligence, prosthetics, healthcare monitoring, and human–machine interactions (HMI). However, it still remains a critical challenge in developing high‐resolution tactile sensors without involving high‐cost and complicated manufacturing processes. Herein, a flexible high‐resolution triboelectric sensing array (TSA) for self‐powered real‐time tactile sensing is developed through a facile, mask‐free, high‐efficient, and environmentally friendly laser direct writing technique. A 16 × 16 pixelated TSA with a resolution of 8 dpi based on patterned laser‐induced graphene (LIG) electrodes (7 Ω sq−1) is fabricated by the complementary intersection overlapping between upper and lower aligned semicircular electrode arrays. With the especially patterning design, the complexity of TSA and the number of data channels is reduced. Meanwhile, the TSA platform exhibits excellent durability and synchronicity and enables the achievement of real‐time visualization of multipoint touch, sliding, and tracking motion trajectory without power consumption. Furthermore, a smart wireless controlled HMI system, composed of a 9‐digital arrayed touch panel based on a LIG‐patterned triboelectric nanogenerator, is constructed to control personal electronics wirelessly. Consequently, the self‐powered TSA as a promising platform demonstrates great potential for an active real‐time tactile sensing system, wireless controlled HMI, security identification and, many others.
Advanced mRNA vaccines play vital roles against SARS-CoV-2. However, most current mRNA delivery platforms need to be stored at −20 °C or −70 °C due to their poor stability, which severely restricts their availability. Herein, we develop a lyophilization technique to prepare SARS-CoV-2 mRNA-lipid nanoparticle vaccines with long-term thermostability. The physiochemical properties and bioactivities of lyophilized vaccines showed no change at 25 °C over 6 months, and the lyophilized SARS-CoV-2 mRNA vaccines could elicit potent humoral and cellular immunity whether in mice, rabbits, or rhesus macaques. Furthermore, in the human trial, administration of lyophilized Omicron mRNA vaccine as a booster shot also engendered strong immunity without severe adverse events, where the titers of neutralizing antibodies against Omicron BA.1/BA.2/BA.4 were increased by at least 253-fold after a booster shot following two doses of the commercial inactivated vaccine, CoronaVac. This lyophilization platform overcomes the instability of mRNA vaccines without affecting their bioactivity and significantly improves their accessibility, particularly in remote regions.
Virtual prototyping of power electronic modules aims to allow rapid evaluation of potential designs without building and testing physical prototypes. Among the interests in thermal models of the virtual modules, process of compact thermal models needs effective methodology to fast generate small models describing the thermal performance of a potential design. This study chooses the Generalized Minimized Residual (GMRES) Algorithm to process thermal models due to its efficiency. Based on that, a machine learning aided surrogate model is proposed for the prediction of thermal performance since existing approaches take much time to determine the thermal response to a particular input power. This surrogate model is created by training a dedicated artificial neural network (ANN) on simulation data, after that this model can quickly map the module temperature and the power input in time domain. In the training process, crossvalidation method is introduced to determine which neuron structure should be selected for the practical data generated by thermal equations. The test group is noted in cross-validation to give the prediction performance of structure candidates. To verify the proposed method, the resulting data of trained surrogate models are compared with the accurate simulation data after the ANN based cross-validation.
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