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In recent years, DNA-based biosensors have shown great potential as the candidate of the next generation biomedical detection device due to their robust chemical properties and customizable biosensing functions. Compared with the conventional biosensors, the DNA-based biosensors have advantages such as wider detection targets, more durable lifetime, and lower production cost. Additionally, the ingenious DNA structures can control the signal conduction near the biosensor surface, which could significantly improve the performance of biosensors. In order to show a big picture of the DNA biosensor’s advantages, this article reviews the background knowledge and recent advances of DNA-based biosensors, including the functional DNA strands-based biosensors, DNA hybridization-based biosensors, and DNA templated biosensors. Then, the challenges and future directions of DNA-based biosensors are discussed and proposed.

Atomic magnetometers based on the spin-exchange relaxation-free (SERF) regime have broad applications in bio-magnetic measurement due to their high sensitivity and miniaturized size. In this paper, we propose a SERF-based magnetometer using 1 × 2 polarization-maintaining fiber (PMF) with single-beam parameter optimization. The impacts of temperature, pumping laser power, and modulation amplitude on the magnetometer’s response signal at the SERF regime are examined. Moreover, through the simulation of zero-field resonance, the compensation accuracy is optimized. To further improve the compensation stability and accuracy, a novel finite state machine (FSM)-assisted iterative optimization magnetic field compensation algorithm is proposed. A pT-level compensation resolution with an error below 1.6% is achieved, which lays the foundation for the subsequent application of biomagnetic measurement arrays.

Flexible fiber-shaped strain sensors show tremendous potential in wearable health monitoring and human‒machine interactions due to their compatibility with everyday clothing. However, the conductive and sensitive materials generated by traditional manufacturing methods to fabricate fiber-shaped strain sensors, including sequential coating and solution extrusion, exhibit limited stretchability, resulting in a limited stretch range and potential interface delamination. To address this issue, we fabricate a fiber-shaped flexible capacitive strain sensor (FSFCSS) by direct ink writing technology. Through this technology, we print parallel helical Ag electrodes on the surface of TPU tube fibers and encapsulate them with a high dielectric material BTO@Ecoflex, endowing FSFCSS with excellent dual-mode sensing performance. The FSFCSS can sense dual-model strain, namely, axial tensile strain and radial expansion strain. For axial tensile strain sensing, FSFCSS exhibits a wide detection range of 178%, a significant sensitivity of 0.924, a low detection limit of 0.6%, a low hysteresis coefficient of 1.44%, and outstanding mechanical stability. For radial expansion strain sensing, FSFCSS demonstrates a sensitivity of 0.00086 mmHg−1 and exhibits excellent responsiveness to static and dynamic expansion strain. Furthermore, FSFCSS was combined with a portable data acquisition circuit board for the acquisition of physiological signals and human‒machine interaction in a wearable wireless sensing system. To measure blood pressure and heart rate, FSFCSS was combined with a printed RF coil in series to fabricate a wireless hemodynamic sensor. This work enables simultaneous application in wearable and implantable health monitoring, thereby advancing the development of smart textiles.

This review mainly studies the development status, limitations, and future directions of modular microfluidic systems. Microfluidic technology is an important tool platform for scientific research and plays an important role in various fields. With the continuous development of microfluidic applications, conventional monolithic microfluidic chips show more and more limitations. A modular microfluidic system is a system composed of interconnected, independent modular microfluidic chips, which are easy to use, highly customizable, and on-site deployable. In this paper, the current forms of modular microfluidic systems are classified and studied. The popular fabrication techniques for modular blocks, the major application scenarios of modular microfluidics, and the limitations of modular techniques are also discussed. Lastly, this review provides prospects for the future direction of modular microfluidic technologies.

The efficient capture of drug metabolites from aquatic environments has been recognized as an essential task for environmental protection. A methanol-modified ultra-fine magnetic biochar (CH3OH-OP-char/Fe3O4) was prepared from orange peel powder using ball milling, and its adsorption behaviors for ibuprofen and sulfamethoxazole were evaluated. The obtained materials were characterized by laser particle size analyzer, EA, ICP-OES, VSM, BET, TG-DTG, and FTIR. Furthermore, the experiments were conducted to study the vital operating parameters such as solution pH (2.0–11.0), contact time (0.5–240 min), initial drug concentration (0.5–100 mg/L), and temperatures (15–40°C) on the removal process. The results showed that the adsorption of IBP and sulfamethoxazole on CH3OH-OP-char/Fe3O4 was highly pH-dependent. Kinetic studies indicated that physisorption was the dominant adsorption mechanism, and film diffusion played a vital role in adsorption onto CH3OH-OP-char/Fe3O4. Equilibrium data were fitted well with the Langmuir isotherm model, implying monolayer adsorption. The adsorption process was spontaneous and endothermic due to the thermodynamic calculation, and high temperatures were favorable to the adsorption process.

Establishing a maintenance-free current sensing network across the entire power grid to facilitate wide-area online monitoring is crucial for realizing a smart grid. However, distribution networks (DNs) frequently lack effective real-time current monitoring owing to the complexity of load types, extensive line distribution, and numerous branches. In this study, we propose a high-precision, self-powered online current monitoring system that integrates a TMR sensors array module, a main control module, a current transformer (CT) power harvesting module, and current online monitoring software. The TMR sensors array module boasts a measurement range of 0–300 A and a high sensitivity of 25.38 mV/A. To address wire eccentricity errors in array sensors, we develop a neural network-based correction algorithm, which identifies wire positions and applies correction coefficients, achieving high accuracy with an average error of 1.23%. Current data are wirelessly transmitted to software terminals via 4G communication for remote monitoring. Furthermore, the CT power harvesting module converts magnetic energy from the power grid into electrical energy, ensuring that the system is self-powered. Validation through continuous 24-h monitoring of DNs demonstrates the system’s high precision and stability. This work presents an effective solution for high-accuracy online current monitoring in DNs.

In the construction and operation of smart grids, real-time monitoring of electrical signals is crucial for achieving efficient and stable power transmission, so it is necessary to develop current and voltage sensors with high stability, mass manufacturing and light weight. This study presents a current and voltage sensor based on fully printed technology for electrical signal monitoring of transmission lines. The current sensor is supported and insulated by polyimide, and successfully fabricates the 3D induction coil through screen printing and high-precision inkjet printing processes, achieving a sensitivity of 0.00823 mV/A and a linearity of 0.999 in 0–60 A. The voltage sensor is made of polyimide film as the substrate, and a pair of silver sensing electrodes are prepared by screen printing process, achieving a sensitivity of 0.00369 μA/V and a linearity of 0.999 in 0–1200 V, with stable output over a continuous operation of 24 h. The overall size of the current and voltage sensor is 1.5 cm × 2 cm, the weight is 1.8 g, the cost is about USD 0.462, and it has the advantages of low cost, lightweight, good linearity, high stability, simple structure, and scalable preparation. This work provides a new sensor fabrication method for current and voltage monitoring in transmission lines.

It is widely accepted that the abnormal concentrations of different inflammation biomarkers can be used for the early diagnosis of cardiovascular disease (CVD). Currently, many reported strategies, which require extra report tags or bulky detection equipment, are not portable enough for onsite inflammation biomarker detection. In this work, a fiber-based surface plasmon resonance (SPR) biosensor decorated with DNA aptamers, which were specific to two typical inflammation biomarkers, C-reactive protein (CRP) and cardiac troponin I (cTn-I), was developed. By optimizing the surface concentration of the DNA aptamer, the proposed sensor could achieve a limit of detection (LOD) of 1.7 nM (0.204 μg/mL) and 2.5 nM (57.5 ng/mL) to CRP and cTn-I, respectively. Additionally, this biosensor could also be used to detect other biomarkers by immobilizing corresponding specific DNA aptamers. Integrated with a miniaturized spectral analysis device, the proposed sensor could be applied for constructing a portable instrument to provide the point of care testing (POCT) for CVD patients.

Reverse iontophoresis (RI) is a promising non-invasive, wearable technology for the transdermal extraction of interstitial fluid (ISF), which contains rich biomarkers relevant to health status. Despite the advancement of wearable sensors, this technology is still restricted for accurate non-invasive biomarkers detection. The main challenge lies in the instability of ISF extraction during RI. We found that this instability is primarily caused by the skin surface pH variations because of the interaction between the RI-induced H + movement and the skin recovery ability. Here, we investigated how the skin surface pH affected RI, theoretically and experimentally; and developed a wearable device and a calibration method to enable accurate non-invasive ISF glucose detection, accordingly. The result showed that glucose prediction accuracy was markedly improved, with mean absolute relative difference (MARD) decreased from 34.44% to 14.78% across both healthy and diabetic volunteers. Reverse iontophoresis offers non-invasive extraction of interstitial fluid but it has proven hard to be used for glucose monitoring. Here, the authors establish the mechanism by which skin surface pH modulates RI through zeta potential changes of keratin, supported by theoretical analysis and numerical simulations

Respiratory signals are basic indicators of human life and health that are used as effective biomarkers to detect respiratory diseases in clinics, including cardiopulmonary function, breathing disorders, and breathing system infections. Therefore, it is necessary to continuously measure respiratory signals. However, there is still a lack of effective portable electronic devices designed to meet the needs of daily respiratory monitoring. This study presents an intelligent, portable, and wireless respiratory monitoring system for real-time evaluation of human respiratory behaviors. The system consists of a triboelectric respiratory sensor; circuit board hardware for data acquisition, preprocessing, and wireless transmission; a machine learning algorithm for enhancing recognition accuracy; and a mobile terminal app. The triboelectric sensor—fabricated by the screen-printing method—is lightweight, non-invasive, and biocompatible. It provides a clear response to the frequency and intensity of respiratory airflow. The portable circuit board is reusable and cost-effective. The decision tree model algorithm is used to identify the respiratory signals with an average accuracy of 97.2%. The real-time signal and statistical results can be uploaded to a server network and displayed on various mobile terminals for body health warnings and advice. This work promotes the development of wearable health monitoring systems.