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Recent advances of plasmonic nanoparticles include fascinating developments in the fields of energy, catalyst chemistry, optics, biotechnology, and medicine. The plasmonic photothermal properties of metallic nanoparticles are of enormous interest in biomedical fields because of their strong and tunable optical response and the capability to manipulate the photothermal effect by an external light source. To date, most biomedical applications using photothermal nanoparticles have focused on photothermal therapy; however, to fully realize the potential of these particles for clinical and other applications, the fundamental properties of photothermal nanoparticles need to be better understood and controlled, and the photothermal effect‐based diagnosis, treatment, and theranostics should be thoroughly explored. This Progress Report summarizes recent advances in the understanding and applications of plasmonic photothermal nanoparticles, particularly for sensing, imaging, therapy, and drug delivery, and discusses the future directions of these fields. Photothermally active plasmonic nanoparticles are of great interest in biomedical science due to their tunable resonance wavelength, high spatiotemporal resolution, photothermal therapeutic potential, and remote‐controllability by an external light source. Fundamentals in the design, synthesis, and properties of photothermal nanomaterials and the recent key advances in their biomedical applications, including in biosensors, imaging, therapy, drug delivery, and theranostics, are summarized and discussed.

The reversible and cooperative activation process, which includes electron transfer from surrounding redox mediators, the reversible valence change of cofactors and macroscopic functional/structural change, is one of the most important characteristics of biological enzymes, and has frequently been used in the design of homogeneous catalysts. However, there are virtually no reports on industrially important heterogeneous catalysts with these enzyme-like characteristics. Here, we report on the design and synthesis of highly active TiO2 photocatalysts incorporating site-specific single copper atoms (Cu/TiO2) that exhibit a reversible and cooperative photoactivation process. Our atomic-level design and synthetic strategy provide a platform that facilitates valence control of co-catalyst copper atoms, reversible modulation of the macroscopic optoelectronic properties of TiO2 and enhancement of photocatalytic hydrogen generation activity, extending the boundaries of conventional heterogeneous catalysts.Reversible and cooperative activation processes are important characteristics of biological enzymes and can be used in designing catalysts. Highly active TiO2 photocatalysts incorporated with site-specific single copper atoms are now shown to exhibit such a photoactivation process.

Maximum atom efficiency as well as distinct chemoselectivity is expected for electrocatalysis on atomically dispersed (or single site) metal centres, but its realization remains challenging so far, because carbon, as the most widely used electrocatalyst support, cannot effectively stabilize them. Here we report that a sulfur-doped zeolite-templated carbon, simultaneously exhibiting large sulfur content (17 wt% S), as well as a unique carbon structure (that is, highly curved three-dimensional networks of graphene nanoribbons), can stabilize a relatively high loading of platinum (5 wt%) in the form of highly dispersed species including site isolated atoms. In the oxygen reduction reaction, this catalyst does not follow a conventional four-electron pathway producing H 2 O, but selectively produces H 2 O 2 even over extended times without significant degradation of the activity. Thus, this approach constitutes a potentially promising route for producing important fine chemical H 2 O 2 , and also offers opportunities for tuning the selectivity of other electrochemical reactions on various metal catalysts. Atomically dispersed metal catalysts display high atom efficiency for electrocatalytic processes. Here, the authors report that sulfur-doped zeolite-templated carbon stabilizes highly dispersed platinum species, predominantly as single-atom centres, and probe its oxygen reduction selectivity.

Beyond great advances in initial activity, Fe–N–C catalysts face the next challenge of stability issue in acidic medium that must be overcome to replace Pt in fuel cell cathode. However, the complex phenomena in fuel cells and consequential difficulty in understanding deactivation mechanisms of Fe–N–C cathodes impede solutions for prolonged stability. Here we show time-resolved changes in active site density and turnover frequency of Fe–N–C along with concurrent decrease in oxygen reduction reaction current in a temperature/gas controllable gas-diffusion electrode flow cell. Operando diagnosis of Fe leaching identifies a strong dependence of site density changes on operating parameters and draws a lifetime-dependent stability diagram that reveals a shift in the prime degradation mechanism during operation. A proof-of-concept strategy with site-isolated Pt ions as a non-catalytic stabilizer, supported by theoretical calculations, demonstrates enhanced fuel cell stability with reduced Fe dissolution, offering design principles for durable Fe–N–C catalysts. Inexpensive Fe–N–C single-atom catalysts are a promising solution to replace costly Pt-based cathode catalysts in fuel cells, but they typically suffer from low durability. Now, the degradation mechanisms of Fe–N–C catalysts are identified under operando conditions as a function of time, and potential solutions are proposed.

Toward tailored electrocatalysis, significant attention has been directed to the electrode-electrolyte interface. The electric double layer provides a crucial microenvironment for electrochemical reactions. However, its atomic-scale structure remains unresolved, particularly for non-dilute electrolyte concentrations relevant to practical systems. A notable example is the camel-to-bell shape transition in the capacitance curve, where two peaks merge as the concentration increases, which is still poorly understood at the molecular level. Herein, using all-atom simulations, we elucidate the electric double layer structures and their phase transitions which give rise to capacitance peaks. The predicted transition potentials match the experimental peak positions. We observe collective water reorientation in the cathodic region and anion surface condensation in the anodic region, which are further validated by in situ spectroscopy. Finally, we construct an electric double layer structural phase diagram to provide detailed insight into the electric double layer microenvironment. This work presents a valuable framework for design of improved interfaces. The atomic structure of the electric double layer at practical concentrations remains unsolved. Here, authors elucidate its structure and phase transitions using all-atom simulations, which reproduce the camel-to-bell shape transition in the capacitance curve.

With the advancement of corporate globalization, an increasing number of small and medium-sized enterprises (SMEs) have leveraged globalized resources to achieve accelerated growth mode that significantly depart from the traditional gradual development trajectories of large enterprises. Notably, the emergence and evolution of born-global (BG) firms have attracted substantial scholarly attention in international business research. This paper studies the innovation and valuation of Chinese born-global (BG) firms, based on dynamic capabilities theory and resource-based view, explores the determinant factors of becoming BG firms, and explores an empirical analysis of changes in the value of BG firms. This paper utilizes the OLS model and panel model, as well as Heckman two-stage, propensity score matching (PSM), and heterogeneity analyzes. We conducted some empirical tests on financial data from 2007 to 2021. The empirical results show that the implementation of the BG mode by enterprises contributes to the growth of corporate value and innovation plays a positive moderating role in this relationship. In addition, the determinant factors for a company to adopt the BG mode are total assets, ownership, and the rate of the largest shareholder. Heterogeneity analysis indicates greater impact on private, foreign, and eastern regional firms. The Heckman two-stage selection model effectively addressed the identification requirements for exclusion restriction variables, while the PSM methodology demonstrated improved covariate balance distributions across matched groups. This dual approach collectively mitigated endogeneity concerns and enhanced the robustness of estimation outcomes. Finally, this study provides business managers with a valuation model for enterprise internationalization, which helps small and medium-sized enterprises choose BG mode to start the internationalization process in their initial stage. Furthermore, this study has significantly enriched the existing literature concerning innovation, corporate value, and equity characteristics of BG firms, while establishing novel theoretical perspectives and methodological avenues for subsequent research investigations.

This paper highlights the significance of safety and reliability in modern industries, particularly in sectors like petroleum and LNG, where safety valves play a critical role in ensuring system safety under extreme conditions. To enhance the reliability of these valves, this study aims to develop a deep learning-based prognostics and health management (PHM) model. Past empirical methods have limitations, driving the need for data-driven prediction models. The proposed model monitors safety valve performance, detects anomalies in real time, and prevents accidents caused by system failures. The research focuses on collecting sensor data, analyzing trends for lifespan prediction and normal operation, and integrating data for anomaly detection. This study compares related research and existing models, presents detailed results, and discusses future research directions. Ultimately, this research contributes to the safe operation and anomaly detection of pilot-operated cryogenic safety valves in industrial settings.

A bstract We examine the discovery potential for double Higgs production at the high luminosity LHC in the final state with two b -tagged jets, two leptons and missing transverse momentum. Although this dilepton final state has been considered a difficult channel due to the large backgrounds, we argue that it is possible to obtain sizable signal significance, by adopting a deep learning framework making full use of the relevant kinematics along with the jet images from the Higgs decay. For the relevant number of signal events we obtain a substantial increase in signal sensitivity over existing analyses. We discuss relative improvements at each stage and the correlations among the different input variables for the neutral network. The proposed method can be easily generalized to the semi-leptonic channel of double Higgs production, as well as to other processes with similar final states.

Fire spread models (FSMs) are used to reproduce fire behavior and can simulate fire propagation over landscapes. As wildfires have emerged into a global phenomenon with far-reaching impacts on the natural and built environments, FSM simulations provide crucial information to better understand and predict fire behavior in various landscapes. In this study, we tested Cell2Fire, a recently developed cellular automata-based FSM, against benchmarking models used in the U.S., Canada, and Chile. We experimented on synthetically generated landscapes (homogeneous and heterogeneous mix of fuels), applying Cell2Fire for the first time on U.S. landscapes, and found a high level of agreement between Cell2Fire and existing FSMs. However, FSMs may not always produce realistic simulations. In response, we used two optimization methods to improve the simulation’s accuracy. First, we adopted a multi-objective optimization algorithm that scales the elliptical shape of the Cell2Fire’s output based on rate of spread (ROS) and eccentricity. Second, we optimized four adjustment factors related to the fire spread (head ROS, back ROS, flank ROS, and eccentricity) using blackbox optimization (i.e., derivative-free optimization), minimizing the discrepancy of the output with respect to real burn data. We assessed the effectiveness of the optimization on the 2001 Dogrib Fire in Alberta, Canada and found that the optimized Cell2Fire result more accurately predicted the real burn in comparison to Prometheus (standard Canadian FSM), increasing F1-score from 0.74 to 0.83. Further, Cell2Fire exhibited better computational efficiency, with simulation runtime increasing linearly compared to Prometheus’ runtime increasing exponentially. From these results, users can adjust Cell2Fire and simulate more realistic burns and surpass the capabilities of benchmark FSMs, integrating local or custom-made FSM data to expand the simulator’s application.

We report an experimental examination of non-axisymmetric (3D) magnetic fields for the control of Alfvén eigenmodes (AEs) in KSTAR. Application of the phase-sweeping n = 1 3D magnetic field identifies the effective 3D field phase and threshold amplitude for suppression of toroidal AEs. Such observations indicate that at least two conditions on the 3D field phase and amplitude should be satisfied for the AE suppression. The phase window of AE suppression is largely resonant and thereby overlapped with that of mode locking, while the threshold of mode locking is slightly higher than that of AE suppression, which implies a narrow 3D configuration window for AE suppression. Numerical analyses on the AE stability and fast ion phase-space transport suggest that the key mechanism of the AE suppression is the reduction of the AE drive through redistribution of fast ion phase-space distribution by strong resonant interactions of the fast ions with the 3D magnetic field.