In the microscopic world, laser trapping is an effective method for the precise manipulation of micro-/nano-objects. Conventional optical tweezers are based on the principle of photon momentum exchange, which generates optical forces on the order of piconewtons (~10^-12 N). However, overcoming the motion resistance of micro-/nano-objects at solid interfaces is challenging as it typically requires forces on the order of micronewtons (~10^-6 N). Owing to their limitations, conventional optical tweezers are typically used in fluid environments, such as vacuum/air and liquids. Trapping and manipulating objects on solid interfaces can be challenging. Scholars have attempted to actuate micro-/nano-objects using pulsed lasers based on the principle of surface elastic waves to manipulate objects in direct adsorption contact with dry solid surfaces (solid-gas interfaces). However, this technique has yet to result in the stable trapping of objects. For techniques that do not offer trapping, a random misalignment between an object’s center of mass and the spot center introduces uncertainty in the direction of motion. This uncertainty hinders the precise, continuous, and arbitrary control of the object’s motion. Photothermal-Shock tweezers enable the laser trapping and manipulation of metallic nanomaterials on dry solid interfaces via the photothermal shock effect. Hence, their application is wide ranging. Additionally, the utilization and maintenance of laser-trapping methods typically necessitate the use of intricate equipment and specialized debugging techniques, which imposes numerous limitations on the operating environment and the personnel operating the equipment. This goal of this study is to design a micro-/nano-object control system based on deep learning. The system will enable the high-precision laser trapping and intelligent motion control of objects at dry solid interfaces via a photothermal-shock tweezer platform.

This paper presents a micro-/nano-object control system comprising three components: a photothermal-shock tweezer platform, an integrated control module, and an image-feedback module. The composition of the control module and the method of operating the photothermal-shock tweezer platform, including the hardware-structure construction and the corresponding control program design, are analyzed. The resolution and control range of the control module are analyzed via calculation and testing. The image-feedback module of the system is designed to detect the position of micro-/nano-objects in microscopic images and to provide dynamic feedback. The image-feedback module uses the YOLOv8 model for object detection and the OpenCV algorithm for center-of-mass localization. The models mentioned above are trained using a customized dataset created from microscopic images. Subsequently, they are tested on various sample images, and the detection resolution and error are analyzed. Finally, an experimental setup is constructed, as shown in Fig. 1, and motion-control experiments are conducted on multiple samples to evaluate the overall system performance.

The trained YOLOv8 model and OpenCV algorithm are used by the image-feedback module to locate the center of mass in various types of microscopically acquired images (Fig. 5). The average detection error of the module is 116.1 nm. Motion-control experiments are conducted using the overall system. In the experiments, Pd nanosheets measuring approximately 10 μm are used, and a transparent silicon-dioxide sheet is used as the substrate. The system controls the laser to trap the nanosheet sample and actuate it along a predetermined path (Fig. 7). The average control error of the spot is 71.8 nm, whereas that of the sample is 108.9 nm. The data shown in Table 1 indicate that the control system successfully realizes the nanoscale closed-loop control of micro-/nano-objects on dry solid interfaces with a high degree of control freedom and a small control error. Additionally, the sample is tested at varying speeds (Fig. 8), and the system’s control errors at different speeds are obtained experimentally (Table. 2). As the sample’s movement speed increases, the control accuracy of the system decreases. If the sample propagates extremely rapidly owing to the system setting, then it will not satisfy the response time required for the laser spot to re-trap the sample. Consequently, the sample will be outside the trapping range of the laser spot, thus causing the system to lose control of the sample. Within certain limits, the rate at which the sample is re-trapped can be increased by increasing the pulse frequency of the laser (Fig. 9). Thus, the micro-/nano-objects can be guaranteed to remain in the trap at higher laser-spot motion speeds. In addition, the use of higher-quality laser spots, flat-surface substrates, and smaller nanosheets allows objects to be trapped more rapidly and stably. For the control system designed in this study, the samples can be stabilized via numerous experimental trials when the system is specified to propagate at a speed of 5 μm/s or less.

A control system for micro-/nano-objects is proposed in this study. Combining this system with image feedback based on deep learning, high-precision laser trapping and the intelligent control of micro-/nano-objects on dry solid interfaces are realized by integrating the control of a photothermal-shock tweezer experimental platform. The system can realize the laser trapping and path control of objects based on the parameter input as well as identify and locate objects in microscopic images via the YOLOv8 and OpenCV algorithms. This method provides dynamic feedback regarding the trapping state of the system, thus enabling the intelligent control of objects. Additionally, the modularized design of the system and the gesture-control method endow the system with a certain level of compatibility and flexibility that facilitates the expansion of functions in different application scenarios as well as the operation and use of personnel in different fields.