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Fiber‐based multimodal sensors with electrical/optical signals are highly desired for next‐generation wearable electronics. Despite the remarkable progress in this area, achieving large‐scale knittable, washable, and self‐healing performance in fiber‐based multimodal sensors simultaneously remains a great challenge. Here, a smart fiber capable of exhibiting piezoresistive/luminescent properties based on an H‐bonding connected multilayered core–shell nanostructure is developed. The core principle of this design involves constructing strong interfacial interactions between the fiber layers, which results in a sensor with high sensitivity (gauge factor = 12383500), exceptional water resistance, and robust self‐healing properties (tensile strength 30.9 MPa, healing efficiency 72.9%). Unlike traditional fiber‐based sensors where elaborate nanostructures are prone to shedding during knitting, this strategy enables the fiber sensors with excellent knittability to be patterned in the fabric, improving both optical and electrical sensitivities. This work is anticipated to make a significant contribution to the further development of wearable electronic products and visual human–computer interaction electronic devices. [ABSTRACT FROM AUTHOR]

Rodent-infested bald spots are crucial indicators of rodent infestation in grasslands. Leveraging Unmanned Aerial Vehicle (UAV) remote sensing technology for discerning detrimental bald spots among plateau pikas has significant implications for assessing associated ecological hazards. Based on UAV-visible light imagery, we classified and recognized the characteristics of plateau pika habitats with five supervised classification algorithms, i.e., minimum distance classification (MinD), maximum likelihood classification (ML), support vector machine classification (SVM), Mahalanobis distance classification (MD), and neural network classification (NN). The accuracy of the five methods was evaluated using a confusion matrix. Results showed that NN and SVM exhibited superior performance than other methods in identifying and classifying features indicative of plateau pika habitats. The mapping accuracy of NN for grassland and bald spots was 98.1% and 98.5%, respectively, with corresponding user accuracy was 98.8% and 97.7%. The overall model accuracy was 98.3%, with a Kappa coefficient of 0.97, reflecting minimal misclassification and omission errors. Through practical verification, NN exhibited good stability. In conclusion, the neural network method was suitable for identifying rodent-damaged bald spots within alpine meadows. [ABSTRACT FROM AUTHOR]

Cellulose nanocrystal (CNC) based optical devices with adjustable schemochrome have attracted immense interest. However, most of the previously reported structural colored CNC‐based materials can only achieve simple stress‐induced color change, which have difficulty achieving multimode control of complex patterning that can be accurately identified. Here, inspired by the nanostructure‐based color‐changing mechanism of neon tetra, this study presents a pressure/temperature dual‐responsive CNC‐based schemochrome hydrogel with adjustable dynamic chiral nematic structure. By incorporating abundant interfacial noncovalent interactions, dynamic correlations between adjustable helical pitch of the vertically stacked cholesteric liquid crystalline (LC) phase and responsiveness of flexible thermosensitive substrate are established, which further enable wide‐range optical characteristic (12°–213° in HSV color model and 421–734 nm in the UV–Vis spectra) and identifiable visualized patterning. The resultant hydrogels are applied in proof‐of‐concept demonstrations of on‐demand schemochrome patterning, including customizable patterned dual‐encryption label, smart digital display, temperature monitor, and intelligent recognition/control system. This study envisages that the bioinspired construction of structural colored nanomaterials will have promising applications in smart responsive photonic equipment including smart display, anticounterfeiting, and intelligent control systems. [ABSTRACT FROM AUTHOR]

Artificial materials with biomimic self‐healing ability are fascinating, however, the balance between mechanical properties and self‐healing performance is always a challenge. Here, a robust, highly stretchable self‐healing elastomer with dynamic reversible multi‐networks based on polyurethane matrix and cyclodextrin‐assembled nanosheets is proposed. The introduction of cyclodextrin nano‐assemblies with abundant surface hydroxyl groups not only forms multiple interfacial hydrogen bonding but also enables a strain‐induced reversible crystalline physical network owing to the special nanoconfined effect. The formation and dissociation of a dynamic crystalline physical network under stretching–releasing cycles skillfully balance the contradiction between mechanical robustness and self‐healing ability. The resulting nanocomposites exhibit ultra‐robust tensile strength (40.5 MPa), super toughness (274.7 MJ m−3), high stretchability (1696%), and desired healing efficiency (95.5%), which can lift a weight ≈ 100 000 times their own weight. This study provides a new approach to the development of mechanically robust self‐healing materials for engineering applications such as artificial muscles and healable robots. [ABSTRACT FROM AUTHOR]