Microcantilevers are widely adopted in various fields for micro-mass sensing due to their simple structure and high sensitivity. The conventional microcantilever-based measurement methods required ultra-precision temperature control. However, such a requirement could hardly be satisfied and the temperature shifting would deteriorate the accuracy of mass sensing. In this research, an impedance-based temperature decoupling method is developed for robust mass sensing over a wide temperature range. The relative relationships of the peaks in the impedance signals are adopted for decoupling the temperature dependent terms in the eigenvalue problem. Besides, a CBAM-CNN network is developed for the modeling and mass sensing. Experimental studies indicate the proposed method yield robust mass sensing with accuracy up to 99.10 % under temperature range from 25 ℃ to 55 ℃. This method enables cantilever-based mass sensing without temperature compensation.
The exploration of intelligent machines has recently spurred the development of physical neural networks, a class of intelligent metamaterials capable of learning, whether in silico or in situ, from observed data. In this letter, we introduce ’equilibrium learning’, a novel physical learning rule designed for lattice-based mechanical neural networks (MNNs) to achieve target performance. This approach leverages the steady states of nodes for back-propagation, efficiently updating the learning degrees of freedom. One-dimensional MNNs, trained with equilibrium learning in silico, can exhibit the desired behaviors on demand function as intelligent mechanical machines. The approach is then employed for the precise morphing control of two-dimensional MNNs subjected to shear or uniaxial loads. Moreover, the MNN is trained to execute classical machine learning tasks such as regression, and preprogrammed bandgap control, establishing it as a versatile platform for physical learning. Our approach presents an efficient pathway for the design of lattice-based mechanical metamaterials for a wide range of static and dynamic target functionalities, positioning them as powerful engines for physical learning.
Chronic wounds pose a substantial global health challenge, further complicated by the presence of biofilms, which are pathogenic bacterial colonies protected by a biopolymer matrix. These biofilms are notably resistant to both traditional antibiotics and host immune responses, underscoring the critical demand for innovative therapeutic strategies. Among such advancements, transdermal drug delivery mechanisms, particularly microneedle patches, have shown promise in addressing biofilm-related infections effectively. This research introduces a drug delivery system incorporating a piezoelectric transducer (PZT) with a strategically arranged array of drug-infused microneedles. The activation of this system through voltage application to the transducer generates ultrasound waves, facilitating the targeted dispersion of drugs through the creation of localized acoustic fields and fluid streaming. This study delves into the optimization of ultrasound parameters and the mechanics of acoustically assisted drug distribution, identifying the conditions under which ultrasound waves can enhance the transfer of therapeutic agents via microneedles. It distinguishes between resonant and non-resonant frequencies, which influence the pattern and efficiency of drug diffusion into biofilms. The analysis extends to the simulation of drug penetration into biofilms, offering insights into concentration profiles at various depths. This investigation not only highlights the potential of ultrasound-enhanced drug delivery for precision medicine but also suggests its applicability in treating a wide array of medical conditions. The ability to precisely control drug delivery, coupled with real-time monitoring, signifies a transformative approach to medical treatment, with the potential to significantly improve patient outcomes and quality of life.
We proposed a physics-guided machine-learning based inverse design approach for realizing multifunctional wave control in active metabeams connecting with negative capacitances. The transfer matrix method which relates the wave field and its derivative to carry the wave propagation information will be embedded in the ML network to construct the mapping between the input and output responses of the unit cell. After this network is well trained, global wave propagation behavior in the active metabeam can be accurately described by the concatenation of networks of each unit cells into a global stiffness matrix. We further apply the proposed network as a surrogate model for genetic algorithm on the inverse design of the metabeam for multifunctional wave control. Our proposed approach can not only be easily extended to design other types of active/passive metamaterials, but also provides some insights into optimization aided engineering in high-dimensional design space of metamaterials.
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