Supported metal catalysts play a crucial role in the modern industry. Constructing strong metal-support interactions (SMSI) is an effective means of regulating the interfacial properties of noble metal-based supported catalysts. Here, we propose a new strategy of ultrafast laser-induced SMSI that can be constructed on a CeO2-supported Pt system by confining electric field in localized interface. The nanoconfined field essentially boosts the formation of surface defects and metastable CeOx migration. The SMSI is evidenced by covering Pt nanoparticles with the CeOx thin overlayer and suppression of CO adsorption. The overlayer is permeable to the reactant molecules. Owing to the SMSI, the resulting Pt/CeO2 catalyst exhibits enhanced activity and stability for CO oxidation. This strategy of constructing SMSI can be extended not only to other noble metal systems (such as Au/TiO2, Pd/TiO2, and Pt/TiO2) but also on non-reducible oxide supports (such as Pt/Al2O3, Au/MgO, and Pt/SiO2), providing a universal way to engineer and develop high-performance supported noble metal catalysts.

Background: Hemophagocytic lymphohistiocytosis (HLH) is a relatively rare and life-threatening disorder. Early mortality remains significantly high among patients with HLH. Our aim was to investigate clinical features and risk factors associated with 7-day and 30-day mortality among pediatric HLH patients. We retrospectively collected medical records of patients with discharge diagnosis of HLH between August 2014 and October 2018 from a tertiary children's hospital in China. The main outcome measures were the 7-day and 30-day outcome after hospital admission. The associations between symptoms, concomitant diagnoses, laboratory test results, and the risk of 7-day and 30-day mortality were examined. Results: Among 160 pediatric HLH patients, 18 (11.3%) patients were deceased within 7 days after admission, and 46 (28.8%) patients were deceased within 30 days. The identified strong risk factors (OR > 10 and p < 0.05) for 30-day mortality were myocardial damage, severe pneumonia, respiratory failure, coagulopathy, gastrointestinal disorder, and multiple organ dysfunction syndrome (MODS). Factors strongly associated with 7-day mortality were sepsis, myocardial damage, shock, and respiratory failure. All patients deceased within 7 days developed hepatic dysfunction, coagulopathy, and MODS. Conclusions: The identified risk factors could help to stratify patients with high risk of early death, and need to be considered in the development of treatment protocols. As early mortality of HLH remains high, studies are needed to investigate how to initiate adequate HLH-directed treatment strategies for patients at higher risk of early death.

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.

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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.