The ongoing coronavirus (COVID-19) pandemic requires enormous production of facemasks and related personal protection materials, thereby increasing the amount of nondegradable plastic waste. The core material for facemasks is melt-blown polypropylene (PP) fiber. Each disposable facemask consumes ∼0.7 g of PP fibers, resulting in annual global consumption and disposal of more than 1 150 000 tons of PP fibers annually. Herein, we developed a laser-assisted melt-blown (LAMB) technique to manufacture PP nanofibers with a quality factor of 0.17 Pa −1 and significantly reduced the filter's weight. We demonstrated that a standard surgical facemask could be made with only 0.13 g of PP nanofibers, saving approximately 80% of the PP materials used in commercial facemasks. Theoretical analysis and modeling were also conducted to understand the LAMB process. Importantly, nanofibers can be easily scaled up for mass production by upgrading traditional melt blown line with scanning laser-assisted melt-blown (SLAMB).

Intelligent sensors have attracted substantial attention for various applications, including wearable electronics, artificial intelligence, healthcare monitoring, and human−machine interactions. However, there still remains a critical challenge in developing a multifunctional sensing system for complex signal detection and analysis in practical applications. Here, we develop a machine learning-combined flexible sensor for real-time tactile sensing and voice recognition through laser-induced graphitization. The intelligent sensor with a triboelectric layer can convert local pressure to an electrical signal through a contact electrification effect without external bias, which has a characteristic response behavior when exposed to various mechanical stimuli. With the special patterning design, a smart human−machine interaction controlling system composed of a digital arrayed touch panel is constructed to control electronic devices. Based on machine learning, the real-time monitoring and recognition of the changes of voice are achieved with high accuracy. The machine learning-empowered flexible sensor provides a promising platform for the development of flexible tactile sensing, real-time health detection, human−machine interaction, and intelligent wearable devices.

Gold nanorods (Au NRs) have attracted great attention owing to their significant role in catalysis, imaging, and photothermal therapy. The internal atomic structure control of metallic NRs has long been a challenge. In article number 2001101, Jianfeng Yan and co‐workers demonstrate the concept of internal atomic structures tailored with light, and Au NRs with various internal atomic structure are fabricated.

Metallic nanoparticles (NPs) play a significant role in nanocatalytic systems, which are important for clean energy conversion, storage, and utilization. Laser fabrication of metallic NPs relying on light−matter interactions provides many opportunities. It is essential to study the atomic structure transformation of nonactive monocrystalline metallic NPs for practical applications. The high-density stacking faults were fabricated in monocrystalline Au NPs through tuning the ultrafast laser-induced relaxation dynamics, and the thermal and dynamic stress effects on the atomic structure transformation were revealed. The atomic structure transformation mainly arises from the thermal effect, and the dynamic stress distribution induced by local energy deposition gives rise to the generation of stacking faults. Au NPs with abundant stacking faults show enhanced surface activity owing to their low coordination number. We suggest that this work expands the knowledge of laser-metallic nanomaterial interactions and provides a method for designing metallic NPs for a wide range of applications.

Micro/nano processing technologies have been extensively studied since micro/nano structures are used in different area such as microelectronics and microdevices. As a high-precision processing technology, ultrafast laser has been applied in the fabrication of different types of metallic micro/nano structures. However, the knowledge about the materials response of metals at atomic scale during laser processing is still necessary to explore. Herein, the femtosecond laser processing of metals from the atomic structure perspective is revealed. Three different layers named recast layer, high density dislocation layer, and unaffected layer are found after femtosecond laser irradiation. The recast layer is on the surface, which is generated from the resolidification of melting materials. The high density dislocation layer, consisting of dislocations and stacking faults, is observed beneath the irradiated surface. The dislocation layer is produced by the laser-induced stress wave, and the mechanical properties of irradiated surface are affected by the laser-induced subsurface dislocation layer. The unaffected layer is not affected by laser irradiation, and maintains the initial atomic structure. The research gives new information about the ultrafast laser processing of metals at atomic-level, which is helpful to the fabrication of functional micro/nano devices for wide applications.