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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.

 

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.

 

Modern biomedical research and preclinical pharmaceutical development rely heavily on the phenotyping of small vertebrate models for various diseases prior to human testing. In this article, we demonstrate an acoustofluidic rotational tweezing platform that enables contactless, high-speed, 3D multispectral imaging and digital reconstruction of zebrafish larvae for quantitative phenotypic analysis. The acoustic-induced polarized vortex streaming achieves contactless and rapid (~1 s/rotation) rotation of zebrafish larvae. This enables multispectral imaging of the zebrafish body and internal organs from different viewing perspectives. Moreover, we develop a 3D reconstruction pipeline that yields accurate 3D models based on the multi-view images for quantitative evaluation of basic morphological characteristics and advanced combinations of metrics. With its contactless nature and advantages in speed and automation, our acoustofluidic rotational tweezing system has the potential to be a valuable asset in numerous fields, especially for developmental biology, small molecule screening in biochemistry, and pre-clinical drug development in pharmacology.

 

Traumatic brain injury (TBI) is a global cause of morbidity and mortality. Initial management and risk stratification of patients with TBI is made difficult by the relative insensitivity of screening radiographic studies as well as by the absence of a widely available, noninvasive diagnostic biomarker. In particular, a blood-based biomarker assay could provide a quick and minimally invasive process to stratify risk and guide early management strategies in patients with mild TBI (mTBI). Analysis of circulating exosomes allows the potential for rapid and specific identification of tissue injury. By applying acoustofluidic exosome separation—which uses a combination of microfluidics and acoustics to separate bioparticles based on differences in size and acoustic properties—we successfully isolated exosomes from plasma samples obtained from mice after TBI. Acoustofluidic isolation eliminated interference from other blood components, making it possible to detect exosomal biomarkers for TBI via flow cytometry. Flow cytometry analysis indicated that exosomal biomarkers for TBI increase in the first 24 h following head trauma, indicating the potential of using circulating exosomes for the rapid diagnosis of TBI. Elevated levels of TBI biomarkers were only detected in the samples separated via acoustofluidics; no changes were observed in the analysis of the raw plasma sample. This finding demonstrated the necessity of sample purification prior to exosomal biomarker analysis. Since acoustofluidic exosome separation can easily be integrated with downstream analysis methods, it shows great potential for improving early diagnosis and treatment decisions associated with TBI.

 

Surface acoustic waves (SAWs) that propagate on the surface of a solid at MHz frequencies are widely used in sensing, communication, and acoustic tweezers. However, their properties are difficult to be tuned electrically, and current devices suffer from complicated configurations, complicated tuning mechanisms, or small ranges of tunability. Here a structure featuring a thin‐film transistor configuration is proposed to achieve electrically tunable SAW propagation based on conductivity tuning. When a DC gate voltage is applied, the on‐site conductivity of the piezoelectric substrate is modulated, which leads to velocity and amplitude tuning of SAWs. The use of carbon nanotubes and crystalline nanocellulose as the channel and gate materials results in high tuning capacity and low gate voltage requirement. The tunability is manifested by a 2.5% phase velocity tuning and near 10 dB on/off switching of the signals. The proposed device holds the potential for the next generation SAW‐based devices.

 

The valley degree of freedom in crystals offers great potential for manipulating classical waves, however, few studies have investigated valley states with complex wavenumbers, valley states in graded systems, or dispersion tuning for valley states. Here, we present tunable valley phononic crystals (PCs) composed of hybrid channel-cavity cells with three tunable parameters. Our PCs support valley states and Dirac cones with complex wavenumbers. They can be configured to form chirped valley PCs in which edge modes are slowed to zero group velocity states, where the energy at different frequencies accumulates at different designated locations. They enable multiple functionalities, including tuning of dispersion relations for valley states, robust routing of surface acoustic waves, and spatial modulation of group velocities. This work may spark future investigations of topological states with complex wavenumbers in other classical systems, further study of topological states in graded materials, and the development of acoustic devices.

 

Synthesis of nanoparticles and particulate nanomaterials with tailored properties is a central step toward many applications ranging from energy conversion and imaging/display to biosensing and nanomedicine. While existing microfluidics‐based synthesis methods offer precise control over the synthesis process, most of them rely on passive, partial mixing of reagents, which limits their applicability and potentially, adversely alter the properties of synthesized products. Here, an acoustofluidic (i.e., the fusion of acoustic and microfluidics) synthesis platform is reported to synthesize nanoparticles and nanomaterials in a controllable, reproducible manner through acoustic‐streaming‐based active mixing of reagents. The acoustofluidic strategy allows for the dynamic control of the reaction conditions simply by adjusting the strength of the acoustic streaming. With this platform, the synthesis of versatile nanoparticles/nanomaterials is demonstrated including the synthesis of polymeric nanoparticles, chitosan nanoparticles, organic–inorganic hybrid nanomaterials, metal–organic framework biocomposites, and lipid‐DNA complexes. The acoustofluidic synthesis platform, when incorporated with varying flow rates, compositions, or concentrations of reagents, will lend itself unprecedented flexibility in establishing various reaction conditions and thus enable the synthesis of versatile nanoparticles and nanomaterials with prescribed properties.

 

Metasurfaces open up unprecedented potential for wave engineering using subwavelength sheets. However, a severe limitation of current acoustic metasurfaces is their poor reconfigurability to achieve distinct functions on demand. Here a programmable acoustic metasurface that contains an array of tunable subwavelength unit cells to break the limitation and realize versatile two‐dimensional wave manipulation functions is reported. Each unit cell of the metasurface is composed of a straight channel and five shunted Helmholtz resonators, whose effective mass can be tuned by a robust fluidic system. The phase and amplitude of acoustic waves transmitting through each unit cell can be modulated dynamically and continuously. Based on such mechanism, the metasurface is able to achieve versatile wave manipulation functions, by engineering the phase and amplitude of transmission waves in the subwavelength scale. Through acoustic field scanning experiments, multiple wave manipulation functions, including steering acoustic waves, engineering acoustic beams, and switching on/off acoustic energy flow by using one design of metasurface are visually demonstrated. This work extends the metasurface research and holds great potential for a wide range of applications including acoustic imaging, communication, levitation, and tweezers.

 

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.