Micro/nanorobots
have been extensively explored as a tetherless
small-scale robotic biodevice to perform minimally invasive interventions
in hard-to-reach regions. Despite the emergence of versatile micro/nanorobots
in recent years, matched in vivo development remains
challenging, limited by unsatisfactory integration of core functions.
Herein, we report a polydopamine (PDA)-coated magnetic microswimmer
consisting of a magnetized Spirulina (MSP) matrix and PDA surface. Apart from the properties of the existing
MSP (e.g., robust propulsion, natural fluorescence,
tailored biodegradation, and selective cytotoxicity), the introduced
PDA coating enhances the photoacoustic (PA) signal and photothermal
effect of the MSP, thus making PA image tracking and photothermal
therapy possible. Meanwhile, the PDA’s innate fluorescence
quenching and diverse surface reactivity allows an off–on fluorescence
diagnosis with fluorescence probes (e.g., coumarin
7). As a proof of concept, real-time image tracking (by PA imaging)
and desired theranostic capabilities of PDA-MSP microswimmer swarms
are demonstrated for the treatment of pathogenic bacterial infection.
Our study suggests a feasible antibacterial microrobot for in vivo development and a facile yet versatile functionalization
strategy of micro/nanorobots.
A multi-level supramolecular system produced by single-step Fe3+-mediated ionic crosslinking self-assembly can overcome the critical issues of current sonodynamic therapy (SDT) and address the need to monitor therapeutic effects in vivo with a non-invasive approach.
A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system.
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