Nanoscience is a booming field incorporating some of the most fundamental questions concerning structure, function, and applications. The cutting-edge research in nanoscience requires access to advanced techniques and instrumentation capable of approaching these unanswered questions. Over the past few decades, atomic force microscopy (AFM) has been developed as a powerful platform, which enables in situ characterization of topological structures, local physical properties, and even manipulating samples at nanometer scale. Currently, an imaging mode called PeakForce Tapping (PFT) has attracted more and more attention due to its advantages of nondestructive characterization, high-resolution imaging, and concurrent quantitative property mapping. In this review, the origin, principle, and advantages of PFT on nanoscience are introduced in detail. Three typical applications of this technique, including high-resolution imaging of soft samples in liquid environment, quantitative nanomechanical property mapping, and electrical/electrochemical property measurement will be reviewed comprehensively. The future trends of PFT technique development will be discussed as well.

It remains a formidable challenge to accurately recognize motion intentions of patients thus to control hand exoskeletons according to their volition. Current methods primarily focus on recognition of limited patient’s motion intentions, with the purpose of controlling preconfigured gestures of a hand exoskeleton for grasping objects. These methods exhibit a marked shortfall when encountering scenarios that are unexpected or not designed in advance, such as non-preprogrammed hand movements and object manipulation tasks. To tackle this issue, large language model (LLM) and speech recognition technology are employed in this study to allow the patient to control a hand exoskeleton at will. In particular, two LLMs are tailored to formulate codes of either generating non-preprogrammed gestures or dealing with unencountered objects. Additionally, an incremental learning framework is proposed to enable patients to perform both predefined and non-predefined operation tasks by integrating a natural language parser with the two LLM-based learners. The natural language parser can directly control the hand exoskeleton to perform predefined operations tasks from prestored command set, while the LLM-based learners can incrementally expand the control command set so as to enhance adaptability of the hand exoskeleton to complex activities over daily use. This study is a pioneering work in the field of hand exoskeletons, which will revolutionize the way to control hand exoskeletons. Furthermore, the proposed framework can be easily generalized to any other robots by modifying the prompt of customized LLMs, which provides a new idea to achieve autonomous learning in robotics. Note to Practitioners —The motivation of this article is to tackle the challenge of intention recognition for performing activities of daily living (ADLs) by stroke patients using a multi-degree of freedom hand exoskeleton. Existing methods for intention recognition so far can only be used for several tasks that are predefined in advance, thus none of them allow patients to control the hand exoskeleton completely at will. To surpass this limitation, an LLM-enabled incremental learning framework that integrates a hand exoskeleton controller with Large Language Model (LLM) is proposed and validated in this study. The framework offers patients an intuitive interface via voice interaction and enables patients to perform not only predefined operation tasks by the hand exoskeleton controller but also non-predefined ones that can be learned from the LLM. As a result, the hand exoskeleton controller continues to learn from the LLM, therefore is gradually able to perform all tasks in daily life. This pioneering study paves a new way in building patient-controlled hand exoskeletons with autonomous intelligence that can deal with non-predefined operation tasks in unstructured environments.

The in-phase bias modulation (IPBM) mode of scanning ion conductance microscopy (SICM), which has advantages of both the dc mode and the traditional ac mode, is immune to low-frequency external electronic interference and potential drift while maintaining a high scanning speed. However, the IPBM mode still suffers from a low-signal-to-noise ratio (SNR). In this paper, we propose a capacitance compensation (CC) method to solve the problem in the IPBM mode of SICM. After CC, the signal level is significantly increased while the noise level remains unchanged. The increased SNR not only improves the image quality but also allows the system to work at a higher modulation frequency, thus increasing potential for fast scan. Experimental results verify the effectiveness of the IPBM mode of SICM with CC.

This Letter reports a phase modulation (PM) mode of scanning ion conductance microscopy. In this mode, an AC current is directly generated by an AC voltage between the electrodes. The portion of the AC current in phase with the AC voltage, which is the current through the resistance path, is modulated by the tip-sample distance. It can be used as the input of feedback control to drive the scanner in Z direction. The PM mode, taking the advantages of both DC mode and traditional AC mode, is less prone to electronic noise and DC drift but maintains high scanning speed. The effectiveness of the PM mode has been proven by experiments.

Mechanosensitive ion channels play important roles for sensing and responding to the mechanical stimuli signals in living life. Here we report the development of a mechanostimulated patch-clamp system for simultaneous recording of external stimuli and acquisition of cellular physiological responses. This system integrates a custom-designed planar patch-clamp system with a robot-assisted atomic force microscope (AFM) system. The former, with a microfluidic channel, can realize not only recording electrical signals but also exchanging intracellular solution; while the latter, enhanced by robotic techniques (local scan force feedback, augmented reality vision feedback), can generate force stimuli with controllable patterns and magnitudes under the operator's real-time monitoring. To verify the performance of the developed system, we first measured the whole-cell current of the voltage-gated potassium ion channel Kv1.1 expressed on Human Embryonic Kidney (HEK293) cells and then recorded the mechanosensitive ion channel current in a mouse neuroblastoma cell line (Neuro2 A) in the whole-cell configuration during the AFM indenting on the membrane surface; finally, confirmed the ability to exchange intracellular solution by delivering propidium iodide into the captured cell through intracellular solution. The results prove the effectiveness of the system.