Tracing the Flu Symptom Progression via a Smart Face Mask

Recent Advances in Next-Generation Textiles

Textiles have played a pivotal role in human development, evolving from basic fibers into sophisticated, multifunctional materials. Advances in material science, nanotechnology, and electronics have propelled next-generation textiles beyond traditional functionalities, unlocking innovative possibilities for diverse applications. Thermal management textiles incorporate ultralight, ultrathin insulating layers and adaptive cooling technologies, optimizing temperature regulation in dynamic and extreme environments. Moisture management textiles utilize advanced structures for unidirectional transport and breathable membranes, ensuring exceptional comfort in activewear and outdoor gear. Protective textiles exhibit enhanced features, including antimicrobial, antiviral, anti-toxic gas, heat-resistant, and radiation-shielding capabilities, providing high-performance solutions for healthcare, defense, and hazardous industries. Interactive textiles integrate sensors for monitoring physical, chemical, and electrophysiological parameters, enabling real-time data collection and responses to various environmental and user-generated stimuli. Energy textiles leverage triboelectric, piezoelectric, and hygroelectric effects to improve energy harvesting and storage in wearable devices. Luminous display textiles, including electroluminescent and fiber optic systems, enable dynamic visual applications in fashion and communication. These advancements position next-generation textiles at the forefront of materials science, significantly expanding their potential across a wide range of applications.

Fully Integrated Passive Wireless Sensor with Mechanical-Electrical Double-Gradient for Multifunctional Healthcare Monitoring

Accurate, effective, and continuous monitoring of pressure, moisture, and temperature is essential for routine health assessments and professional patient care. In this study, we present a fully integrated multiparameter passive wireless sensor (MWS) that employs a mechanical-electrical dual-gradient structure design. The unique gradient porous structure endows the MWS with significant advantages in terms of detection dimensions (pressure, moisture, and temperature), sensitivity, and stability. Compared to single mechanical gradient designs, the sensor demonstrates 2.6 times higher pressure sensitivity and a 5-tier moisture detection capability. By bridging the technology gap between high-precision multiparameter sensing, wireless communication, and energy management, the MWS is capable of measuring multiple physiological parameters, including breath, ballistocardiograph, moisture, and temperature at multiple points, providing real-time assessments of the physiological state of the subjects. This work offers valuable quantitative insights for caregivers and paves the way for significant advancements in personal healthcare management.

Interfacial Polarization Strategy to Electroactive Poly(lactic acid) Nanofibers for Humidity-Resistant Respiratory Protection and Machine Learning-Assisted Monitoring

The advancement of intelligent and biodegradable respiratory protection equipment is pivotal in the realm of human health engineering. Despite significant progress, achieving a balance between efficient filtration and intelligent monitoring remains a great challenge, especially under conditions of high relative humidity (RH) and high airflow rate (AR). Herein, we proposed an interfacial stereocomplexation (ISC) strategy to facilitate intensive interfacial polarization for poly(lactic acid) (PLA) nanofibrous membranes, which were customized for machine learning-assisted respiratory diagnosis. Theoretical principles underlying the facilitated formation of the electroactive phase and aligned PLA chains were quantitatively depicted in the ISC-PLA nanofibers, contributing to the increased dielectric constant and surface potential (as high as 2.2 and 5.1 kV, respectively). Benefiting from the respiration-driven triboelectric mechanisms, the ISC-PLA demonstrated a high PM0.3 filtration efficiency of over 99% with an ultralow pressure drop (75 Pa), even in challenging circumstances (95 ± 5% RH, AR of 85 L/min). Furthermore, we implemented the ISC-PLA with multifunction respiratory monitoring (response time of 0.56 s and recovery time of 0.25 s) and wireless transmission technology, yielding a high recognition rate of 83% for personal breath states. This innovation has practical implications for health management and theoretical advancements in respiratory protection equipment.

Advances in Machine Learning for Wearable Sensors

Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare. To this end, supervised learning and unsupervised learning algorithms have emerged as powerful tools, allowing for the detection of complex patterns and relationships in large, high-dimensional data sets. In this Review, we aim to delineate the latest advancements in machine learning for wearable sensors, focusing on key developments in algorithmic techniques, applications, and the challenges intrinsic to this evolving landscape. Additionally, we highlight the potential of machine-learning approaches to enhance the accuracy, reliability, and interpretability of wearable sensor data and discuss the opportunities and limitations of this emerging field. Ultimately, our work aims to provide a roadmap for future research endeavors in this exciting and rapidly evolving area.

Flexible Metasurfaces for Multifunctional Interfaces

Optical metasurfaces, capable of manipulating the properties of light with a thickness at the subwavelength scale, have been the subject of extensive investigation in recent decades. This research has been mainly driven by their potential to overcome the limitations of traditional, bulky optical devices. However, most existing optical metasurfaces are confined to planar and rigid designs, functions, and technologies, which greatly impede their evolution toward practical applications that often involve complex surfaces. The disconnect between two-dimensional (2D) planar structures and three-dimensional (3D) curved surfaces is becoming increasingly pronounced. In the past two decades, the emergence of flexible electronics has ushered in an emerging era for metasurfaces. This review delves into this cutting-edge field, with a focus on both flexible and conformal design and fabrication techniques. Initially, we reflect on the milestones and trajectories in modern research of optical metasurfaces, complemented by a brief overview of their theoretical underpinnings and primary classifications. We then showcase four advanced applications of optical metasurfaces, emphasizing their promising prospects and relevance in areas such as imaging, biosensing, cloaking, and multifunctionality. Subsequently, we explore three key trends in optical metasurfaces, including mechanically reconfigurable metasurfaces, digitally controlled metasurfaces, and conformal metasurfaces. Finally, we summarize our insights on the ongoing challenges and opportunities in this field.

Artificial Intelligence in battling infectious diseases: A transformative role

It is widely acknowledged that infectious diseases have wrought immense havoc on human society, being regarded as adversaries from which humanity cannot elude. In recent years, the advancement of Artificial Intelligence (AI) technology has ushered in a revolutionary era in the realm of infectious disease prevention and control. This evolution encompasses early warning of outbreaks, contact tracing, infection diagnosis, drug discovery, and the facilitation of drug design, alongside other facets of epidemic management. This article presents an overview of the utilization of AI systems in the field of infectious diseases, with a specific focus on their role during the COVID-19 pandemic. The article also highlights the contemporary challenges that AI confronts within this domain and posits strategies for their mitigation. There exists an imperative to further harness the potential applications of AI across multiple domains to augment its capacity in effectively addressing future disease outbreaks.