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Abstract Extracellular vesicles (EVs) have been identified as promising biomarkers for the noninvasive diagnosis of various diseases. However, challenges in separating EVs from soluble proteins have resulted in variable EV recovery rates and low purities. Here, we report a high-yield ( > 90%) and rapid ( < 10 min) EV isolation method calledFLocculation viaOrbitalAcousticTrapping (FLOAT). The FLOAT approach utilizes an acoustofluidic droplet centrifuge to rotate and controllably heat liquid droplets. By adding a thermoresponsive polymer flocculant, nanoparticles as small as 20 nm can be rapidly and selectively concentrated at the center of the droplet. We demonstrate the ability of FLOAT to separate urinary EVs from the highly abundant Tamm-Horsfall protein, addressing a significant obstacle in the development of EV-based liquid biopsies. Due to its high-yield nature, FLOAT reduces biofluid starting volume requirements by a factor of 100 (from 20 mL to 200 µL), demonstrating its promising potential in point-of-care diagnostics. 

Machine learning image recognition and classification of particles and materials is a rapidly expanding field. However, nanomaterial identification and classification are dependent on the image resolution, the image field of view, and the processing time. Optical microscopes are one of the most widely utilized technologies in laboratories across the world, due to their nondestructive abilities to identify and classify critical micro-sized objects and processes, but identifying and classifying critical nano-sized objects and processes with a conventional microscope are outside of its capabilities, due to the diffraction limit of the optics and small field of view. To overcome these challenges of nanomaterial identification and classification, we developed an intelligent nanoscope that combines machine learning and microsphere array-based imaging to: (1) surpass the diffraction limit of the microscope objective with microsphere imaging to provide high-resolution images; (2) provide large field-of-view imaging without the sacrifice of resolution by utilizing a microsphere array; and (3) rapidly classify nanomaterials using a deep convolution neural network. The intelligent nanoscope delivers more than 46 magnified images from a single image frame so that we collected more than 1000 images within 2 seconds. Moreover, the intelligent nanoscope achieves a 95% nanomaterial classification accuracy using 1000 images of training sets, which is 45% more accurate than without the microsphere array. The intelligent nanoscope also achieves a 92% bacteria classification accuracy using 50 000 images of training sets, which is 35% more accurate than without the microsphere array. This platform accomplished rapid, accurate detection and classification of nanomaterials with miniscule size differences. The capabilities of this device wield the potential to further detect and classify smaller biological nanomaterial, such as viruses or extracellular vesicles. 

Abstract Newly developed acoustic technologies are playing a transformational role in life science and biomedical applications ranging from the activation and inactivation of mechanosensitive ion channels for fundamental physiological processes to the development of contact-free, precise biofabrication protocols for tissue engineering and large-scale manufacturing of organoids. Here, we provide our perspective on the development of future acoustic technologies and their promise in addressing critical challenges in biomedicine. 

Abstract Nanocarrier and exosome encapsulation has been found to significantly increase the efficacy of targeted drug delivery while also minimizing unwanted side effects. However, the development of exosome-encapsulated drug nanocarriers is limited by low drug loading efficiencies and/or complex, time-consuming drug loading processes. Herein, we have developed an acoustofluidic device that simultaneously performs both drug loading and exosome encapsulation. By synergistically leveraging the acoustic radiation force, acoustic microstreaming, and shear stresses in a rotating droplet, the concentration, and fusion of exosomes, drugs, and porous silica nanoparticles is achieved. The final product consists of drug-loaded silica nanocarriers that are encased within an exosomal membrane. The drug loading efficiency is significantly improved, with nearly 30% of the free drug (e.g., doxorubicin) molecules loaded into the nanocarriers. Furthermore, this acoustofluidic drug loading system circumvents the need for complex chemical modification, allowing drug loading and encapsulation to be completed within a matter of minutes. These exosome-encapsulated nanocarriers exhibit excellent efficiency in intracellular transport and are capable of significantly inhibiting tumor cell proliferation. By utilizing physical forces to rapidly generate hybrid nanocarriers, this acoustofluidic drug loading platform wields the potential to significantly impact innovation in both drug delivery research and applications. 

Acoustic tweezers use ultrasound for contact-free, bio-compatible, and precise manipulation of particles from millimeter to submicrometer scale. In microfluidics, acoustic tweezers typically use an array of sources to create standing wave patterns that can trap and move objects in ways constrained by the limited complexity of the acoustic wave field. Here, we demonstrate spatially complex particle trapping and manipulation inside a boundary-free chamber using a single pair of sources and an engineered structure outside the chamber that we call a shadow waveguide. The shadow waveguide creates a tightly confined, spatially complex acoustic field inside the chamber without requiring any interior structure that would interfere with net flow or transport. Altering the input signals to the two sources creates trapped particle motion along an arbitrary path defined by the shadow waveguide. Particle trapping, particle manipulation and transport, and Thouless pumping are experimentally demonstrated.