Food safety has garnered global attention, necessitating advanced methods for the quick and accurate detection of contaminants. Sensors, notable for their ease of use, high sensitivity, and fast analysis, are prominent. Two-dimensional (2D) nanomaterials have been employed to improve sensor performance. Particularly, black phosphorus (BP) stands out with its multifunctional capabilities, attributed to unique layered structure, ultra-high charge mobility, easy surface functionalization, enhanced optical absorption, and tunable direct bandgap. These characteristics suggest that BP could significantly enhance sensor selectivity, sensitivity, and response speed for contaminant detection. Despite numerous studies on BP-based sensors in food safety, few reviews have been comprehensively summarized. Moreover, challenges in BP\'s preparation and stability restrict its wider use. This paper reviews recent research on BP\'s role in food safety, covering preparation, passivation, and applications. Through analysis of challenges and prospects, this review aims to provide insightful guidance for upcoming research in this area.

In recent years, there has been a significant increase in the prevalence of electronic wearables, among which flexible magnetoelectronic skin has emerged as a key component. This technology is part of the rapidly progressing field of flexible wearable electronics, which has facilitated a new human perceptual development known as the magnetic sense. However, the magnetoelectronic skin is limited due to its low sensitivity and substantial field limitations as a wearable electronic device for sensing minor magnetic fields. Additionally, achieving efficient and non-destructive delamination in flexible magnetic sensors remains a significant challenge, hindering their development. In this study, we demonstrate a novel magnetoelectronic touchless interactive device that utilizes a flexible giant magnetoresistive sensor array. The flexible magnetic sensor array was developed through an electrochemical delamination process, and the resultant ultra-thin flexible electronic system possessed both ultra-thin and non-destructive characteristics. The flexible magnetic sensor is capable of achieving a bending angle of up to 90 degrees, maintaining its performance integrity even after multiple repetitive bending cycles. Our study also provides demonstrations of non-contact interaction and pressure sensing. This research is anticipated to significantly contribute to the advancement of high-performance flexible magnetic sensors and catalyze the development of more sophisticated magnetic electronic skins.

Food safety and authenticity analysis play a pivotal role in guaranteeing food quality, safeguarding public health, and upholding consumer trust. In recent years, significant social progress has presented fresh challenges in the realm of food analysis, underscoring the imperative requirement to devise innovative and expedient approaches for conducting on-site assessments. Consequently, cellulose paper-based devices (PADs) have come into the spotlight due to their characteristics of microchannels and inherent capillary action. This review summarizes the recent advances in cellulose PADs in various food products, comprising various fabrication strategies, detection methods such as mass spectrometry and multi-mode detection, sampling and processing considerations, as well as applications in screening food safety factors and assessing food authenticity developed in the past 3 years. According to the above studies, cellulose PADs face challenges such as limited sample processing, inadequate multiplexing capabilities, and the requirement for workflow integration, while emerging innovations, comprising the use of simplified sample pretreatment techniques, the integration of advanced nanomaterials, and advanced instruments such as portable mass spectrometer and the innovation of multimodal detection methods, offer potential solutions and are highlighted as promising directions. This review underscores the significant potential of cellulose PADs in facilitating decentralized, cost-effective, and simplified testing methodologies to maintain food safety standards. With the progression of interdisciplinary research, cellulose PADs are expected to become essential platforms for on-site food safety and authentication analysis, thereby significantly enhancing global food safety for consumers.

Conductive hydrogels, known for their flexibility, biocompatibility, and conductivity, have found extensive applications in fields such as healthcare, environmental monitoring, and soft robotics. Recent advancements in 3D printing technologies have transformed the fabrication of conductive hydrogels, creating new opportunities for sensing applications. This review provides a comprehensive overview of the advancements in the fabrication and application of 3D-printed conductive hydrogel sensors. First, the basic principles and fabrication techniques of conductive hydrogels are briefly reviewed. We then explore various 3D printing methods for conductive hydrogels, discussing their respective strengths and limitations. The review also summarizes the applications of 3D-printed conductive hydrogel-based sensors. In addition, perspectives on 3D-printed conductive hydrogel sensors are highlighted. This review aims to equip researchers and engineers with insights into the current landscape of 3D-printed conductive hydrogel sensors and to inspire future innovations in this promising field.

Living organisms in nature possess diverse and vibrant structural colors generated from their intrinsic surface micro/nanostructures. These intricate micro/nanostructures can be harnessed to develop a new generation of colorful materials for various fields such as photonics, information storage, display, and sensing. Recent advancements in the fabrication of photonic crystals have enabled the preparation of structurally colored materials with customized geometries using 3D printing technologies. Here, a comprehensive review of the historical development of fabrication methods for photonic crystals is provided. Diverse 3D printing approaches along with the underlying mechanisms, as well as the regulation methods adopted to generate photonic crystals with structural color, are discussed. This review aims to offer the readers an overview of the state-of-the-art 3D printing techniques for photonic crystals, present a guide and considerations to fabricate photonic crystals leveraging different 3D printing methods.

Graphite carbon nitride (g-C3N4) is a two-dimensional conjugated polymer with a unique energy band structure similar to graphene. Due to its outstanding analytical advantages, such as relatively small band gap (2.7 eV), low-cost synthesis, high thermal stability, excellent photocatalytic ability, and good biocompatibility, g-C3N4 has attracted the interest of researchers and industry, especially in the medical field. This paper summarizes the latest research on g-C3N4-based composites in various biomedical applications, including therapy, diagnostic imaging, biosensors, antibacterial, and wearable devices. In addition, the application prospects and possible challenges of g-C3N4 in nanomedicine are also discussed in detail. This review is expected to inspire emerging biomedical applications based on g-C3N4.

Soft robots have morphological characteristics that make them preferred candidates, over their traditionally rigid counterparts, for executing physical interaction tasks with the environment. Therefore, equipping them with force sensing is essential for ensuring safety, enhancing their controllability, and adding autonomy. At the same time, it is necessary to preserve their inherent flexibility when integrating sensory units. Soft-fluidic actuators (SFAs) with hydraulic actuation address some of the challenges posed by the compressibility of pneumatic actuation while maintaining system compliance. This research further investigates the feasibility of utilizing the incompressible actuation fluid as the means of actuation and of multiaxial sensing. We have developed a hyperelastic model for the actuation pressure, acting as a baseline pressure. Any disparities from the baseline have been mapped to external forces, using the principle of pressure-based fluidic soft sensor. Computed tomography imaging has been used to examine inner deformation and validate the analytically derived actuation-pressure model. The induced stresses within the SFA are examined using COMSOL simulations, contributing to the development of a calibration algorithm, which accounts for geometric and cross-sectional nonlinearities and maps pressure variations with tip forces. Two force types (concentrated and distributed) acting on our SFA under different configurations are examined, using two experimental setups described as \"Point Load\" and \"Distributed Force.\" The force sensing algorithm achieves high accuracy with a maximum absolute error of 0.32N for forces with a magnitude of up to 6N.

The advent of internet of things (IoT) technology has ushered in a new dawn for the digital realm, offering innovative avenues for real-time surveillance and assessment of the operational conditions of intricate mechanical systems. Nowadays, mechanical system monitoring technologies are extensively utilized in various sectors, such as rotating and reciprocating machinery, expansive bridges, and intricate aircraft. Nevertheless, in comparison to standard mechanical frameworks, large amusement facilities, which constitute the primary manned electromechanical installations in amusement parks and scenic locales, showcase a myriad of structural designs and multiple failure patterns. The predominant method for fault diagnosis still relies on offline manual evaluations and intermittent testing of vital elements. This practice heavily depends on the inspectors\' expertise and proficiency for effective detection. Moreover, periodic inspections cannot provide immediate feedback on the safety status of crucial components, they lack preemptive warnings for potential malfunctions, and fail to elevate safety measures during equipment operation. Hence, developing an equipment monitoring system grounded in IoT technology and sensor networks is paramount, especially considering the structural nuances and risk profiles of large amusement facilities. This study aims to develop customized operational status monitoring sensors and an IoT platform for large roller coasters, encompassing the design and fabrication of sensors and IoT platforms and data acquisition and processing. The ultimate objective is to enable timely warnings when monitoring signals deviate from normal ranges or violate relevant standards, thereby facilitating the prompt identification of potential safety hazards and equipment faults.

As sessile organisms, plants cannot survive in harmful environments, such as those characterized by drought, flood, heat, cold, nutrient deficiency, and salt or toxic metal stress. These stressors impair plant growth and development, leading to decreased crop productivity. To induce an appropriate response to abiotic stresses, plants must sense the pertinent stressor at an early stage to initiate precise signal transduction. Here, we provide an overview of recent progress in our understanding of the molecular mechanisms underlying plant abiotic stress sensing. Numerous biomolecules have been found to participate in the process of abiotic stress sensing and function as abiotic stress sensors in plants. Based on their molecular structure, these biomolecules can be divided into four groups: Ca2+-permeable channels, receptor-like kinases (RLKs), sphingolipids, and other proteins. This improved knowledge can be used to identify key molecular targets for engineering stress-resilient crops in the field.

The accurate analysis of human dynamic behavior is very important for overcoming the limitations of movement diversity and behavioral adaptability. In this paper, a wearable device-based human dynamic behavior recognition method is proposed. The method collects acceleration and angular velocity data through a six-axis sensor to identify information containing specific behavior characteristics in a time series. A human movement data acquisition platform, the DMP attitude solution algorithm, and the threshold algorithm are used for processing. In this experiment, ten volunteers wore wearable sensors on their bilateral forearms, upper arms, thighs, calves, and waist, and movement data for standing, walking, and jumping were collected in school corridors and laboratory environments to verify the effectiveness of this wearable human movement recognition method. The results show that the recognition accuracy for standing, walking, and jumping reaches 98.33%, 96.67%, and 94.60%, respectively, and the average recognition rate is 96.53%. Compared with similar methods, this method not only improves the recognition accuracy but also simplifies the recognition algorithm and effectively saves computing resources. This research is expected to provide a new perspective for the recognition of human dynamic behavior and promote the wider application of wearable technology in the field of daily living assistance and health management.