Programmable assembly of particles on a Chladni plate

Streaming-based Tweezers for Routing, Engineering, and Manipulation of multiparticles: STREAM

Contactless manipulation of samples, particularly the ability to dynamically handle multiple fragile specimens while maintaining their integrity and viability, is crucial for various applications in biology, medicine, engineering, and physics. While hydrodynamic tweezers have emerged as a promising approach for gentle, label-free manipulation of a wide range of sample types and sizes, they typically have limited flexibility in terms of dynamic control, making it challenging to realize high-resolution and programmable manipulation of multiple samples. Here, we introduce the Streaming-based Tweezers for Routing, Engineering, And Manipulation of multiparticles (STREAM) with sub-wavelength resolution. The platform employs an array of piezoelectric plates arranged in a space-reciprocal pattern to generate acoustic streaming, creating localized trapping points. The mechanism of particle trapping and the improvement of routing resolution via multiunit activation were investigated. Subsequently, a convolutional neural network (CNN) with arbitrary voltage combination as the input and predicted trapping position as the output was integrated into the system. The CNN calibration is vital to the system as it enhances the platform's performance, enabling precise control of the trapping positions beyond traditional physical unit size limitations. We demonstrated the STREAM platform's capabilities through single particle routing with sub-wavelength precision, simultaneous manipulation of multiple particles, and on-demand assembly of samples. The STREAM platform opens new possibilities for applications requiring precise and dynamic control of particles and samples, with the potential to advance fields including biology, chemistry, and materials science.

Swarm Autonomy: From Agent Functionalization to Machine Intelligence

Swarm behaviors are common in nature, where individual organisms collaborate via perception, communication, and adaptation. Emulating these dynamics, large groups of active agents can self-organize through localized interactions, giving rise to complex swarm behaviors, which exhibit potential for applications across various domains. This review presents a comprehensive summary and perspective of synthetic swarms, to bridge the gap between the microscale individual agents and potential applications of synthetic swarms. It is begun by examining active agents, the fundamental units of synthetic swarms, to understand the origins of their motility and functionality in the presence of external stimuli. Then inter-agent communications and agent-environment communications that contribute to the swarm generation are summarized. Furthermore, the swarm behaviors reported to date and the emergence of machine intelligence within these behaviors are reviewed. Eventually, the applications enabled by distinct synthetic swarms are summarized. By discussing the emergent machine intelligence in swarm behaviors, insights are offered into the design and deployment of autonomous synthetic swarms for real-world applications.

Acoustically shaped DNA-programmable materials

Recent advances in DNA nanotechnology allow for the assembly of nanocomponents with nanoscale precision, leading to the emergence of DNA-based material fabrication approaches. Yet, transferring these nano- and micron-scale structural arrangements to the macroscale morphologies remains a challenge, which limits the development of materials and devices based on DNA nanotechnology. Here, we demonstrate a materials fabrication approach that combines DNA-programmable assembly with actively driven processes controlled by acoustic fields. This combination provides a prescribed nanoscale order, as dictated by equilibrium assembly through DNA-encoded interactions, and field-shaped macroscale morphology, as regulated by out-of-equilibrium materials formation through specific acoustic stimulation. Using optical and electron microscopy imaging and x-ray scattering, we further revealed the nucleation processes, domain fusion, and crystal growth under different acoustically stimulated conditions. The developed approach provides a pathway for the fabrication of complexly shaped macroscale morphologies for DNA-programmable nanomaterials by controlling spatiotemporal characteristics of the acoustic fields.

A vibrating capillary for ultrasound rotation manipulation of zebrafish larvae

Multifunctional micromanipulation systems have garnered significant attention due to the growing interest in biological and medical research involving model organisms like zebrafish (Danio rerio). Here, we report a novel acoustofluidic rotational micromanipulation system that offers rapid trapping, high-speed rotation, multi-angle imaging, and 3D model reconstruction of zebrafish larvae. An ultrasound-activated oscillatory glass capillary is used to trap and rotate a zebrafish larva. Simulation and experimental results demonstrate that both the vibrating mode and geometric placement of the capillary contribute to the developed polarized vortices along the long axis of the capillary. Given its capacities for easy-to-operate, stable rotation, avoiding overheating, and high-throughput manipulation, our system poses the potential to accelerate zebrafish-directed biomedical research.

SonoTransformers: Transformable acoustically activated wireless microscale machines

Shape transformation, a key mechanism for organismal survival and adaptation, has gained importance in developing synthetic shape-shifting systems with diverse applications ranging from robotics to bioengineering. However, designing and controlling microscale shape-shifting materials remains a fundamental challenge in various actuation modalities. As materials and structures are scaled down to the microscale, they often exhibit size-dependent characteristics, and the underlying physical mechanisms can be significantly affected or rendered ineffective. Additionally, surface forces such as van der Waals forces and electrostatic forces become dominant at the microscale, resulting in stiction and adhesion between small structures, making them fracture and more difficult to deform. Furthermore, despite various actuation approaches, acoustics have received limited attention despite their potential advantages. Here, we introduce "SonoTransformer," the acoustically activated micromachine that delivers shape transformability using preprogrammed soft hinges with different stiffnesses. When exposed to an acoustic field, these hinges concentrate sound energy through intensified oscillation and provide the necessary force and torque for the transformation of the entire micromachine within milliseconds. We have created machine designs to predetermine the folding state, enabling precise programming and customization of the acoustic transformation. Additionally, we have shown selective shape transformable microrobots by adjusting acoustic power, realizing high degrees of control and functional versatility. Our findings open new research avenues in acoustics, physics, and soft matter, offering new design paradigms and development opportunities in robotics, metamaterials, adaptive optics, flexible electronics, and microtechnology.

Active Micro/Nanoparticles in Colloidal Microswarms

Colloidal microswarms have attracted increasing attention in the last decade due to their unique capabilities in various complex tasks. Thousands or even millions of tiny active agents are gathered with distinctive features and emerging behaviors, demonstrating fascinating equilibrium and non-equilibrium collective states. In recent studies, with the development of materials design, remote control strategies, and the understanding of pair interactions between building blocks, microswarms have shown advantages in manipulation and targeted delivery tasks with high adaptability and on-demand pattern transformation. This review focuses on the recent progress in active micro/nanoparticles (MNPs) in colloidal microswarms under the input of an external field, including the response of MNPs to external fields, MNP-MNP interactions, and MNP-environment interactions. A fundamental understanding of how building blocks behave in a collective system provides the foundation for designing microswarm systems with autonomy and intelligence, aiming for practical application in diverse environments. It is envisioned that colloidal microswarms will significantly impact active delivery and manipulation applications on small scales.

Data-Driven Intelligent Manipulation of Particles in Microfluidics

Automated manipulation of small particles using external (e.g., magnetic, electric and acoustic) fields has been an emerging technique widely used in different areas. The manipulation typically necessitates a reduced-order physical model characterizing the field-driven motion of particles in a complex environment. Such models are available only for highly idealized settings but are absent for a general scenario of particle manipulation typically involving complex nonlinear processes, which has limited its application. In this work, the authors present a data-driven architecture for controlling particles in microfluidics based on hydrodynamic manipulation. The architecture replaces the difficult-to-derive model by a generally trainable artificial neural network to describe the kinematics of particles, and subsequently identifies the optimal operations to manipulate particles. The authors successfully demonstrate a diverse set of particle manipulations in a numerically emulated microfluidic chamber, including targeted assembly of particles and subsequent navigation of the assembled cluster, simultaneous path planning for multiple particles, and steering one particle through obstacles. The approach achieves both spatial and temporal controllability of high precision for these settings. This achievement revolutionizes automated particle manipulation, showing the potential of data-driven approaches and machine learning in improving microfluidic technologies for enhanced flexibility and intelligence.