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The photothermal effect of plasmonic nanostructures has numerous applications, such as cancer therapy, photonic gene circuit, large cargo delivery, and nanostructure-enhanced laser tweezers. The photothermal operation can also induce unwanted physical and biochemical effects, which potentially alter the cell behaviors. However, there is a lack of techniques for characterizing the dynamic cell responses near the site of photothermal operation with high spatiotemporal resolution. In this work, we show that the incorporation of locked nucleic acid probes with gold nanorods allows photothermal manipulation and real-time monitoring of gene expression near the area of irradiation in living cells and animal tissues. The multimodal gold nanorod serves as an endocytic delivery reagent to transport the probes into the cells, a fluorescence quencher and a binding competitor to detect intracellular mRNA, and a plasmonic photothermal transducer to induce cell ablation. We demonstrate the ability of the gold nanorod-locked nucleic acid complex for detecting the spatiotemporal gene expression in viable cells and tissues and inducing photothermal ablation of single cells. Using the gold nanorod-locked nucleic acid complex, we systematically characterize the dynamic cellular heat shock responses near the site of photothermal operation. The gold nanorod-locked nucleic acid complex enables mapping of intracellular gene expressions and analyzes the photothermal effects of nanostructures toward various biomedical applications.

Distributed-drive vehicles utilize independent drive motors on the four-wheel hubs. The working conditions of the wheel-hub motors are so harsh that the motors are prone to failing under different driving conditions. This study addresses the impact of drive motor faults on vehicle performance, particularly on slippery roads where sudden faults can lead to accidents. A fault-tolerant control system integrating motor fault diagnosis and a direct yaw moment control (DYC) based fault-tolerant controller are proposed to ensure the stability of the vehicle during various motor faults. Due to the difficulty of identifying the parameters of the popular permanent magnet synchronous wheel hub motors (PMSMs), the system employs a model-free backpropagation neural network (BPNN)-based fault detector. Turn-to-turn short circuits, open-phase faults, and diamagnetic faults are considered in this research. The fault detector is trained offline and utilizes rotor speed and phase currents for online fault detection. The system assigns the torque outputs from both healthy and faulted motors based on fault categories using sliding mode control (SMC)-based DYC. Simulations with four-wheel electric vehicle models demonstrate the accuracy of the fault detector and the effectiveness of the fault-tolerant controller. The proposed system is prospective and has potential for the development of distributed electric vehicles. [ABSTRACT FROM AUTHOR]

Excessive vibration has always been a serious problem for conical shell structures, while the application of the graphene-based free-constrained layer (GFCL) based on carbon fiber-reinforced composite (CFRC) structure is a novel way to improve structural performance. An analytical model for vibration and dynamic characteristics of the GFCL-CFRC conical shell resting on the Winkler–Pasternak elastic foundation with arbitrary boundary conditions is constructed, and four types of GFCL porosity distribution and GFCL dispersion pattern are considered in this model. The multi-segment technique and virtual spring technique are utilized to simulate arbitrary boundary conditions. Then, the first-order shear deformation theory (FSDT) and Hamilton's principle are employed to obtain the motion equation of the GFCL-CFRC conical shell, and the motion equation of the GFCL-CFRC conical shell is solved by the Ritz method. In conclusion, the dispersion mode of GFCL, thickness ratio of GFCL, and fiber angle have influence on the dynamic performance. With a reasonable design, the dynamic performance of the GFCL-CFRC conical shell can be further improved. [ABSTRACT FROM AUTHOR]

Motors constitute one critical part of industrial production and everyday life. The effective, timely and convenient diagnosis of motor faults is constantly required to ensure continuous and reliable operations. Infrared imaging technology, a non-invasive industrial fault diagnosis method, is usually applied to detect the equipment status in extreme environments. However, conventional Infrared thermal images inevitably show a large amount of noise interference, which affects the analysis results. In addition, each motor may only possess a small amount of fault data in practice, as collecting an infinite amount of motor data to train the diagnostic system is impossible. To overcome these problems, a novel automatic fault diagnosis system is proposed in this study. Data features are enhanced by a normalization module based on color bars first, as the same color in various infrared thermal images represent different temperatures. Then, the few-shot learning method is used to diagnose the faults of unseen electric motors. In the few-shot learning method, the minimum dataset size required to expand system universality is fifteen pieces, effectively solving the universality problem of artificial-to-natural data migration. The method saves a large amount of training data resources and the experimental training data collection. The accuracy of the fault diagnosis system achieved 98.9% on similar motor datasets and 91.8% on the dataset of motors that varied a lot from the training motor, which proves the high reliability and universality of the system. [ABSTRACT FROM AUTHOR]

Bioprocessors for concentrating bioparticles, such as cells and molecules, are commonly needed in bioanalysis systems. In this microfluidic processor, a global flow field generated by ac electroosmosis transports the embedded particles to the regions near the electrode surface. The processor then utilizes electrophoretic and dielectrophoretic forces, which are effective in short range, to trap the target cells and molecules on the electrode surface. By optimizing the operating parameters, we have concentrated various biological objects in a large range of sizes, including Escherichia coli bacteria, lambda phage DNA, and single-stranded DNA fragments as small as 20 bases that have a radius of gyration of only 3 nm.