Bioinspired surfaces with special wettability and adhesion have attracted great interest in both fundamental research and industry applications. Various kinds of special wetting surfaces have been constructed by adjusting the topographical structure and chemical composition. Here, recent progress of the artificial superhydrophobic surfaces with high contrast in solid/liquid adhesion has been reviewed, with a focus on the bioinspired construction and applications of one-dimensional (1D) TiO2-based surfaces. In addition, the significant applications related to artificial super-wetting/antiwetting TiO2-based structure surfaces with controllable adhesion are summarized, e.g., self-cleaning, friction reduction, anti-fogging/icing, microfluidic manipulation, fog/water collection, oil/water separation, anti-bioadhesion, and micro-templates for patterning. Finally, the current challenges and future prospects of this renascent and rapidly developing field, especially with regard to 1D TiO2-based surfaces with special wettability and adhesion, are proposed and discussed.

Artificial scent screening systems (known as electronic noses, E‐noses) have been researched extensively. A portable, automatic, and accurate, real‐time E‐nose requires both robust cross‐reactive sensing and fingerprint pattern recognition. Few E‐noses have been commercialized because they suffer from either sensing or pattern‐recognition issues. Here, cross‐reactive colorimetric barcode combinatorics and deep convolutional neural networks (DCNNs) are combined to form a system for monitoring meat freshness that concurrently provides scent fingerprint and fingerprint recognition. The barcodes—comprising 20 different types of porous nanocomposites of chitosan, dye, and cellulose acetate—form scent fingerprints that are identifiable by DCNN. A fully supervised DCNN trained using 3475 labeled barcode images predicts meat freshness with an overall accuracy of 98.5%. Incorporating DCNN into a smartphone application forms a simple platform for rapid barcode scanning and identification of food freshness in real time. The system is fast, accurate, and non‐destructive, enabling consumers and all stakeholders in the food supply chain to monitor food freshness.

Sensors and algorithms are two fundamental elements to construct intelligent systems. The recent progress in machine learning (ML) has produced great advancements in intelligent systems, owing to the powerful data analysis capability of ML algorithms. However, the performance of most systems is still hindered by sensing techniques that typically rely on rigid and bulky sensor devices, which cannot conform to irregularly curved and dynamic surfaces for high‐quality data acquisition. Skin‐like stretchable sensing technology with unique characteristics, such as high conformability, low modulus, and light weight, has been recently developed to solve this issue. Here, the recent progress in the fusion of emerging stretchable electronics and ML technology, for bioelectrical signal recognition, tactile perception, and multimodal integration is summarized, and the challenges and future developments are further discussed. These efforts aim to accelerate various perception and reasoning tasks for advanced intelligent applications, such as human–machine interfaces, healthcare, and robotics.

Transparent electrodes that form seamless contact and enable optical interrogation at the electrode–brain interface are potentially of high significance for neuroscience studies. Silk hydrogels can offer an ideal platform for transparent neural interfaces owing to their superior biocompatibility. However, conventional silk hydrogels are too weak and have difficulties integrating with highly conductive and stretchable electronics. Here, a transparent and stretchable hydrogel electrode based on poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) and PEGylated silk protein is reported. PEGylated silk protein with poly(ethylene glycol) diglycidyl ether (PEGDE) improves the Young's modulus to 1.51–10.73 MPa and the stretchability to ≈400% from conventional silk hydrogels (<10 kPa). The PEGylated silk also helps form a robust interface with PEDOT:PSS thin film, making the hydrogel electrode synergistically incorporate superior stretchability (≈260%), stable electrical performance (≈4 months), and a low sheet resistance (≈160 ± 56 Ω sq−1). Finally, the electrode facilitates efficient electrical recording, and stimulation with unobstructed optical interrogation and rat‐brain imaging are demonstrated. The highly transparent and stretchable hydrogel electrode offers a practical tool for neuroscience and paves the way for a harmonized tissue–electrode interface.

Coupling myoelectric and mechanical signals during voluntary muscle contraction is paramount in human-machine interactions. Spatiotemporal differences in the two signals intrinsically arise from the muscular excitation-contraction process; however, current methods fail to deliver local electromechanical coupling of the process. Here we present the locally coupled electromechanical interface based on a quadra-layered ionotronic hybrid (named as CoupOn) that mimics the transmembrane cytoadhesion architecture. CoupOn simultaneously monitors mechanical strains with a gauge factor of~34 and surface electromyogram with a signal-to-noise ratio of 32.2 dB. The resolved excitation-contraction signatures of forearm flexor muscles can recognize flexions of different fingers, hand grips of varying strength, and nervous and metabolic muscle fatigue. The orthogonal correlation of hand grip strength with speed is further exploited to manipulate robotic hands for recapitulating corresponding gesture dynamics. It can be envisioned that such locally coupled electromechanical interfaces would endow cyber-human interactions with unprecedented robustness and dexterity.