Detection of small molecules or proteins of living cells provides an exceptional opportunity to study genetic variations and functions, cellular behaviors, and various diseases including cancer and microbial infections. Our aim in this review is to give an overview of selected research activities related to nucleic acid-based aptamer techniques that have been reported in the past two decades. Limitations of aptamers and possible approaches to overcome these limitations are also discussed.

Flexible pressure sensors are essential components for wearable smart devices and intelligent systems. Significant progress has been made in this area, reporting on excellent sensor performance and fascinating sensor functionalities. Nevertheless, geometrical and morphological engineering of pressure sensors is usually neglected, which, however, is significant for practical application. Here, we present a digitized manufacturing methodology to construct a new class of iontronic pressure sensors with optionally defined configurations and widely modulated performance. These pressure sensors are composed of self-defined electrode patterns prepared by a screen printing method and highly tunable pressure-sensitive microstructures fabricated using 3D printed templates. Importantly, the iontronic pressure sensors employ an iontronic capacitive sensing mechanism based on mechanically regulating the electrical double layer at the electrolyte/electrode interfaces. The resultant pressure sensors exhibit high sensitivity (58 kPa⁻¹), fast response/recovery time (45 ms/75 ms), low detectability (6.64 Pa), and good repeatability (2000 cycles). Moreover, our pressure sensors show remarkable tunability and adaptability in device configuration and performance, which is challenging to achieve via conventional manufacturing processes. The promising applications of these iontronic pressure sensors in monitoring various human physiological activities, fabricating flexible electronic skin, and resolving the force variation during manipulation of an object with a robotic hand are successfully demonstrated.

Wearable touch sensors, which can convert force or pressure signals into quantitative electronic signals, have emerged as essential smart sensing devices and play an important role in various cutting-edge fields, including wearable health monitoring, soft robots, electronic skin, artificial prosthetics, AR/VR, and the Internet of Things. Flexible touch sensors have made significant advancements, while the construction of novel touch sensors by mimicking the unique properties of biological materials and biogenetic structures always remains a hot research topic and significant technological pathway. This review provides a comprehensive summary of the research status of wearable touch sensors constructed by imitating the material and structural characteristics in nature and summarizes the scientific challenges and development tendencies of this aspect. First, the research status for constructing flexible touch sensors based on biomimetic materials is summarized, including hydrogel materials, self-healing materials, and other bio-inspired or biomimetic materials with extraordinary properties. Then, the design and fabrication of flexible touch sensors based on bionic structures for performance enhancement are fully discussed. These bionic structures include special structures in plants, special structures in insects/animals, and special structures in the human body. Moreover, a summary of the current issues and future prospects for developing wearable sensors based on bio-inspired materials and structures is discussed.

In order to realize energy saving and high-efficiency production in the CNC turning process of parts, a novel hybrid approach for cutting parameters' optimization considering processing energy and efficiency is proposed. Through experimental fitting and orthogonal experiments, the no-load power and cutting power functions of the CNC lathe were obtained, and a multi-objective optimization model of energy saving and high efficiency was established. The model takes the minimum energy consumption and the fastest processing time as the goal, and comprehensively considers the actual constraints under the processing conditions of the lathe. An improved multi-objective teaching-learning-based optimization (TLBO) algorithm is designed to solve the multi-objective model, a self-study method is added in the learning stage to improve the global search ability of the algorithm, and a minimum partial optimization strategy is added in the teaching stage to ensure the balance of multi-objective optimization results. Finally, the proposed model and algorithm are verified via a typical case, the cutting parameter decision model is established by using analytic hierarchical process (AHP), and the Pareto front solution of cutting parameter optimization is obtained, which objectively selects a better combination of turning parameters. The results show that this method can guide the operator to select cutting parameters from qualitative random to quantitative, and provides theoretical guidance for enterprises implementing energy saving and high-efficiency production.

We present a method, Transient Induced Molecular Electronic Spectroscopy (TIMES), to detect protein-ligand interactions without any protein engineering or chemical modification. We developed a physics model for the TIMES signal and mathematically formulated the problem to attain physical insight of protein-ligand interactions without any disturbances by molecular probes, fluorescent labels, or immobilization of molecules. To demonstrate the functionality of this method, we have used the TIMES signals to find the dissociation constants for the affinity of reactions, the shear-stress dependent adsorption time of molecules on surface, and other interesting features of protein-ligand interaction in native conditions. As a unique tool, TIMES offers a simple and effective method to investigate fundamental protein chemistry and drug discoveries.