Controlling surface wettability is essential for various applications such as water harvesting, oil/water separation, and smart skin technologies. Inspired by natural surfaces like lotus leaves and springtails, superhydrophobic materials exhibit water contact angles (CA) > 150°, low sliding angles (SA < 10°), and minimal CA hysteresis. This state is typically achieved via surface structuring and low surface energy coatings, often governed by the Cassie–Baxter model. However, this metastable state can irreversibly transition to the Wenzel state under external energy. Reversible and switchable wettability is attainable by integrating stimuli-responsive materials. This review categorizes different external stimuli capable of modulating wettability: mechanical deformation, temperature, plasma treatment, light (UV), electron input, magnetic fields, solvent interaction, gas exposure, pH sensitivity, counterion exchange, chemical reactions. These strategies offer tailored control over surface-liquid interactions, enabling smart interfaces.

Topological defects, which play crucial roles in systems ranging from morphogenesis to energy guidance and information storage, are of fundamental interest across many branches of physics. Programmable control of such defects through external stimuli enables the active manipulation of material properties, thereby facilitating the precise tailoring of their functional behaviors and responses. This review systematically elucidates the central role of topological defects in directing the evolution of liquid crystal (LC) ordered structures under external stimuli and across phase transitions. We first analyze the stimulus-responsive dynamics of defects in nematic LCs, focusing on light-induced reconfiguration, electric-field-driven transformation, and their guidance of collective behavior in active colloids and bacterial systems. We then explore the defect-mediated order evolution during the nematic-smectic A phase transition, detailing how nematic defects transform into functional smectic architectures. Moreover, the self-assembly strategies, morphological transitions, and theoretical modeling of two typical textures of smectic LCs, focal conic domains (FCDs) and oily streaks (OSs), as well as their deformation forms, are systematically exhibited; applications of FCDs and OSs in photonics, micro/nanofabrication, and particle assembly are demonstrated. By integrating recent advances in defect programming, phase transition regulation, and responsive materials design, this review establishes a unified framework for the dynamic control of topological-defect-featured ordered structures across scales, bridging fundamental topology with functional innovation. These significant progresses extend our knowledge of self-assembled architectures and make a solid step forward in the sciences and technologies of smart materials and corresponding applications.

In the current era of the booming development of artificial intelligence and artificial robotic technology, perception devices that can precisely respond to external stimuli by emulating biological senses are of great significance. As an emerging two-dimensional material, MXene shows huge potential in the field of responsive materials (including sensors and actuators) due to its unique properties. This review first analyzes the structure, basic characteristics, and synthesis methods of MXene. Subsequently, it reviews the research progress of MXene in the field of sensors. These sensors can accurately feedback external stimuli by changing electrical signals. These research results, similar to the human sensory system, can perceive environmental stimuli such as temperature, light, humidity, and so on. Then, it reviews the applications of MXene-based actuators. Similarly, they can respond to external stimuli, but they feedback external stimuli through changes in mechanical signals (deformation). Further, it reviews the actuators integrated with sensing functions, which can output sensing electrical signals while deforming in response to external stimuli. Based on the demands of artificial intelligence and bionic robots, the research on MXene-based responsive devices has broad prospects. Especially in the fields of healthcare and biomedical engineering, it is expected to bring revolutionary breakthroughs.

Real-world environments are dynamic, variable, and often unpredictable, where temperature, pressure, and chemical composition fluctuate beyond designed limits. Such complexity challenges materials to maintain reliable behavior under coupled physical and chemical fields. Room-temperature liquid metals (RTLMs) show excellent adaptability originating from metallic and fluid. The coexistence of high conductivity, deformability, and reconfigurable interfaces enables stable performance across unstable or fluctuating environments. This review focuses on RTLM-based material systems under unstructured conditions, analyzing how their physicochemical characteristics, including interfacial dynamics, oxidation tolerance, and recoverable morphology, govern environmental adaptability, mechanical resilience, self-healing, and long-term stability. Five representative unstructured scenarios are discussed, including in vivo, underwater, open-air, space, and high-radiation environments, illustrating how RTLMs achieve multifunctions including sensing, actuation, thermal regulation, and shielding under non-ideal conditions. The outlook highlights remaining challenges in interfacial control, material standardization, and scalable integration. RTLMs provide a material foundation for robust and adaptive systems capable of sustained operation in complex real-world environments.

4D printing is a cutting-edge additive manufacturing technology that introduces the time dimension, allowing printed objects to have dynamic, changeable properties, functions or shapes. Compared to traditional 3D printing, 4D printing can create complex geometric structures and respond to stimuli such as temperature, humidity, light, or magnetic fields. These characteristics make it highly promising for applications in fields such as aerospace, biomedicine, and soft robotics. However, performance testing and analysis for 4D printing technologies have not yet been standardized. Herein, an overview of 4D printing performance testing methods are presented. Initially, the molding methods of 4D printing are discussed. Next, the shape memory recovery testing methods for bending deformation models, tensile models and compression models are reviewed. Through analyzing the model designs and experimental methods, the methods for measuring key performance parameters such as shape memory recovery rate, fixation rate, and shape memory recovery force are summarized. Finally, the development directions of 4D printing are highlighted, including the exploration of new materials, the development of multifunctional structures, and the establishment of more standardized testing methods to promote its deeper application and technological improvement across various fields.

Among smart materials, Liquid Crystalline Elastomers (LCEs) combine programmable well-defined deformations with wireless control. To date, the successful fabrication of LCEs through 3D printing techniques, such as direct ink writing (DIW), requires precise control over the ink formulation, mesogen alignment, and curing processes, to get devices with uniform molecular orientations, and with optimized actuation performance. Here, we present a simple synthetic approach leading to a ten-of-gram-scale ink production suitable for low-cost DIW 3D printing of LCEs. The novel ink, containing a push-pull azobenzene directly linked to the polymer backbone, enabled 4D printing of fast responsive photo-mechanical actuators with programmable and reversible deformation. Our centimeter-scale LCE structures present active tensions twitches comparable to those of cardiac muscles, both in terms of magnitude (kPa range) and timescale (tens to hundreds of milliseconds). An all-round actuation characterization is also developed and reported. As a proof-of-concept demonstrator, an optical beam steerer was developed demonstrating a high control of the beam diffraction angle as a function of the control beam light power.

Antimicrobial resistance has evolved into one of the most serious threats to global public health, yet generalizable routes for refining random peptide mixtures (RPMs) into defined, stimuli-responsive, and low-cost antimicrobial formulations remain limited. Here, a refinement framework is presented. It centers on machine learning and co-assembly that converts broad-spectrum RPMs into interpretable antimicrobial peptide cocktails without exhaustive screening. Specifically, starting from a 10-mer Arg/Leu RPM (RL0.5), its antimicrobial activity and self-assembly are quantified, and machine learning is used to prioritize key functional peptides. Leveraging synergistic and co-assembly behaviors, an optimal combination (AEP) is selected. The resulting defined formulation, RL10, achieves a fourfold increase in in vitro activity against Escherichia coli and exhibits a reduced critical aggregation concentration relative to the starting RPM. Overall, this study presents a practical path from complex, low-cost precursors to efficient, co-assembling antimicrobial cocktails, and summarizes explainable design rules that support engineering and industrialization.

Film-based fluorescent sensors (FFSs), an “IUPAC Top Ten Emerging Technology in Chemistry 2022,” are attracting growing interest for applications in public safety, environmental monitoring, and disease diagnosis, driven by their high sensitivity, rapid response, and facile integration. The core of FFSs lies in their engineered sensing films, where efficient molecular channels within the film's architecture dictate key performance metrics like response/recovery speed and signal-to-noise ratio. Thus, the choice of fluorophores and fabrication techniques is paramount for controlling the film's internal structure. This review surveys innovative strategies for constructing porous sensing films, focusing on: using nonplanar fluorophores to build intrinsic pores; self-assembling low-molecular weight gelators into 3D gel networks; incorporating framework materials for ordered, low-defect topological structures; and applying interfacial polymerization to create high-surface-area nanofilms. We then explore how these high-performance FFSs detect targets from explosives and volatile organic compounds to chemical residues and humidity. The review concludes with an analysis of current challenges and a perspective on future paths, providing a roadmap to advance FFSs from laboratory to real-world use.