Addressing the global malnutrition crisis requires precise and timely diagnostics of plant stresses to enhance the quality and yield of nutrient-rich crops, such as tomatoes. Soft wearable sensors offer a promising approach by continuously monitoring plant physiology. However, challenges remain in identifying direct physiological indicators of plant stresses, hindering the development of accurate diagnostic models for predicting symptom progression. Here, we introduce a machine-learning-powered spectral-dominant multimodal soft wearable system (MapS-Wear) for precise, long-term, and early-stage diagnosis of stresses in tomatoes. MapS-Wear continuously tracks leaf surrounding temperature, humidity, and unique in-situ transmission spectra, which are critical stress-related indicators. The machine learning framework processes these multimodal data to predict gradual stress progression and diagnose nutrient deficiencies in plants over 10 days earlier than conventional computer vision methods. Moreover, MapS-Wears enables portable and large-scale screening of grafted tomato varieties in greenhouses, accelerating the identification of compatible grafting combinations. This demonstration highlights the potential for high-throughput plant phenotyping and yield improvement.

Cross-scale self-similar hierarchical micro–nano structures in living systems often provide unique features on surfaces and serve as inspiration sources for artificial materials or devices. For instance, a highly self-similar structure often has a higher fractal dimension and, consequently, a larger active surface area; hence, it would have a super surface performance compared to its peer. However, artificial self-similar surfaces with hierarchical micro–nano structures and their application development have not yet received enough attention. Here, by introducing solvent-assisted UV-lasering, we establish an elegant approach to fabricate self-similar hierarchical micro–nano structures on silicon. The self-similar structure exhibits a super hydrophilicity, a high light absorbance (>90%) in an ultra-broad spectrum (200–2500 nm), and an extraordinarily high efficiency in heat transfer. Through further combinations with other techniques, such surfaces can be used for capillary assembling soft electronics, surface self-cleaning, and so on. Furthermore, such an approach can be transferred to other materials with minor modifications. For instance, by doping carbon in polymer matrix, a silicone surface with hierarchical micro–nano structures can be obtained. By selectively patterning such hierarchical structures, we obtained an ultra-high sensitivity bending sensor. We believe that such a fabrication technique of self-similar hierarchical micro–nano structures may encourage researchers to deeply explore the unique features of functional surfaces with such structures and to further discover their potentials in various applications in diverse directions.

Frequent outbreaks of respiratory infectious diseases, driven by diverse pathogens, have long posed significant threats to public health, economic productivity, and societal stability. Respiratory infectious diseases are highly contagious, characterized by short incubation periods, diverse symptoms, multiple transmission routes, susceptibility to mutations, and distinct seasonality, contributing to their propensity for outbreaks. The absence of effective antiviral treatments and the heightened vulnerability of individuals with weakened immune systems make them more susceptible to infection, with severe cases potentially leading to complications or death. This situation becomes particularly concerning during peak seasons, such as influenza outbreaks. Therefore, early detection, diagnosis, and treatment are critical, alongside the prevention of cross-infection, ensuring patient safety, and controlling healthcare costs. To address these challenges, this review aims to identify a comprehensive, rapid, safe, and cost-effective diagnostic approach for respiratory infectious diseases. This approach is framed within the existing hierarchical healthcare system, focusing on establishing diagnostic capabilities at hospitals, community, and home levels to effectively tackle the above issues. In addition to PCR and isothermal amplification, the review also explores emerging molecular diagnostic strategies that may better address the evolving needs of respiratory disease diagnostics. A key focus is the transition from amplification technologies to amplification-free biosensing approaches, with particular attention given to their potential for home-based testing. This shift seeks to overcome the limitations of conventional amplification methods, particularly in decentralized and home diagnostics, offering a promising solution to enhance diagnostic speed and safety during outbreaks. In the future, with the integration of AI technologies into molecular amplification technologies, biosensors, and various application levels, the inclusively economic, rapid, and safe respiratory disease diagnosis solutions will be further optimized, and their accessibility will become more widespread.

Distinguished by its exceptional sensitivity and specificity, Polymerase Chain Reaction (PCR) is a pivotal technology for pathogen detection. However, traditional PCR instruments that employ thermoelectric cooling (TEC) are often constrained by cost, efficiency, and performance variability resulting from the fluctuations in ambient temperature. Here, we present a thermal cycler that utilizes electromagnetic induction heating at 50 kHz and anti-freezing water cooling with a velocity of 0.06 m/s to facilitate rapid heating and cooling of the PCR reaction chamber, significantly enhancing heat transfer efficiency. A multi-physics theoretical heat transfer model, developed using the digital twin approach, enables precise temperature control through advanced algorithms. Experimental results reveal average heating and cooling rates of 14.92 °C/s and 13.39 °C/s, respectively, significantly exceeding those of conventional methods. Compared to commercial PCR instruments, the proposed system further optimizes cost, efficiency, and practicality. Finally, PCR experiments were successfully performed using cDNA (Hepatitis B virus) at various concentrations.

Being abundant as natural intelligence, plants have attracted huge attention from researchers. Soft film sensors present a novel and promising approach to connect plants with artificial devices, helping us to investigate plants’ intelligence further. Here, recent developments for micro/nano soft film sensors that can be used for establishing intelligent plant systems are summarized, including essential materials, fabrications, and application scenarios. Conductive metals, nanomaterials, and polymers are discussed as basic materials for active layers and substrates of soft film sensors. The corresponding fabrication techniques, such as laser machining, printing, coating, and vapor deposition, have also been surveyed and discussed. Moreover, by combining soft film sensors with plants, applications for intelligent plant systems are also investigated, including plant physiology detection and plant-hybrid systems. Finally, the existing challenges and future opportunities are prospected.

When combined with diverse sensors, soft robots significantly improve their functionalities and intelligence levels. However, most of the existing soft sensors require complex signal analysis devices or algorithms, which severely increase the complexity of soft robot systems. Here, based on the unique fluidic property of liquid metal, we propose a magnet-controllable soft microswitch that can be well-integrated into a soft robot system, e.g., a soft gripper to help it facilely detect and precisely grab objects. The microswitch consists of a flexible soft beam electrode and a fixed electrode, forming a soft microsystem. By tuning the cohesion force of the liquid metal between the electrodes, the microswitch can convert its states between an individual and a self-locking state. The microswitch can achieve a reasonable rapid response (~12 ms) and high switching frequency (~95 Hz). Furthermore, soft microswitches can be customized into logic units and also coupled to control a digital tube showing various numbers. Our work provides a new simple soft sensor unit that may enhance the intelligence of soft systems.