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As the most desirable hydrogen production device, the highly efficient acidic proton exchange membrane water electrolyzers (PEMWE) are severely limited by the sluggish kinetics of oxygen evolution reaction (OER) at the anode. Rutile IrO2 is a commercial acid‐stable OER catalyst with poor activity and high cost, which has motivated the development of alternatives. However, hitherto most of the designed acidic OER catalysts have disadvantages of low activity or stability, which cannot meet the requirement of industrial applications. Thus, exploring suitable strategies to enhance the activity and stability of cost‐effective acidic OER catalysts is crucial for developing the PEMWE technique. In this review, the main OER mechanisms, different types of catalysts, and their activity and stability characteristics are summarized and discussed, and then possible strategies to improve activity and stability are proposed. Finally, the problems and prospects of such catalysts are generalized to shed some light on the future research of advanced catalysts for acidic OER. The highly efficient conversion process of acidic proton exchange membrane water electrolyzes, as the most desirable hydrogen production device, is limited by poor activity and stability of acid oxygen evolution reaction (OER) catalysts. Thus, this review is devoted to systematically analyzing and summarizing the reaction mechanism, the classification, the strategy for promoting activity and stability, and the challenges for acidic OER catalysts.

We investigate the response of outer radiation belt electron fluxes to different solar wind and geomagnetic indices using an interpretable machine learning method. We reconstruct the electron flux variation during 19 enhancement and 7 depletion events and demonstrate the feature attribution analysis called SHAP (SHapley Additive exPlanations) on the superposed epoch results for the first time. We find that the intensity and duration of the substorm sequence following an initial dropout determine the overall enhancement or depletion of electron fluxes, while the solar wind pressure drives the initial dropout in both types of events. Further statistical results from a data set with 71 events confirm this and show a significant correlation between the resulting flux levels and the average AL index, indicating that the observed “depletion” event can be more accurately described as a “non‐enhancement” event. Our novel SHAP‐Enhanced Superposed Epoch Analysis (SHESEA) method can offer insight in various physical systems. Plain Language Summary This study examines the responses of relativistic electrons in Earth's radiation belt to various solar wind and geomagnetic disturbances, identifying key influencing factors. We first adopt an explainable machine learning method to understand the importance of different features during 19 enhancement and 7 depletion events. Our results directly reveal that an increase in solar wind dynamic pressure contributes to a sudden decrease in electron fluxes. Additionally, we find that the strength and duration of subsequent substorms determine whether the electron flux increases or decreases. Guided by the importance of these features as determined by our machine learning model, we carry out a statistical analysis, showing a significant correlation between the flux level and the average AL index. Our method offers advantages over traditional superposed epoch analysis since it directly shows the determining factors. Key Points We use a machine learning feature attribution method to identify key drivers in radiation belt enhancement and depletion events The electron flux depletion, loss, or enhancement is driven by the competition between solar wind Psw and cumulative strength of substorms The average AL index following the pressure maximum has a significant correlation with the resulting flux level

The highly efficient energy conversion of the polymer‐electrolyte‐membrane fuel cell (PEMFC) is extremely limited by the sluggish oxygen reduction reaction (ORR) kinetics and poor electrochemical stability of catalysts. Hitherto, to replace costly Pt‐based catalysts, non‐noble‐metal ORR catalysts are developed, among which transition metal–heteroatoms–carbon (TM–H–C) materials present great potential for industrial applications due to their outstanding catalytic activity and low expense. However, their poor stability during testing in a two‐electrode system and their high complexity have become a big barrier for commercial applications. Thus, herein, to simplify the research, the typical Fe–N–C material with the relatively simple constitution and structure, is selected as a model catalyst for TM–H–C to explore and improve the stability of such a kind of catalysts. Then, different types of active sites (centers) and coordination in Fe–N–C are systematically summarized and discussed, and the possible attenuation mechanism and strategies are analyzed. Finally, some challenges faced by such catalysts and their prospects are proposed to shed some light on the future development trend of TM–H–C materials for advanced ORR catalysis. The poor stability is a huge limitation for the development of transition metal–heteroatoms–carbon (TM–H–C) as promising catalysts toward the oxygen reduction reaction. Accordingly, the stability of different active sites, possible deactivation mechanisms, and strategies to improve stability around classic Fe–N–C model are summarized and discussed, with an attempt to provide insight into reasonably constructing stable and efficient TM–H–C catalysts.

Scaling laws characterize diverse complex systems in a broad range of fields, including physics, biology, finance, and social science. The human language is another example of a complex system of words organization. Studies on written texts have shown that scaling laws characterize the occurrence frequency of words, words rank, and the growth of distinct words with increasing text length. However, these studies have mainly concentrated on the western linguistic systems, and the laws that govern the lexical organization, structure and dynamics of the Chinese language remain not well understood. Here we study a database of Chinese and English language books. We report that three distinct scaling laws characterize words organization in the Chinese language. We find that these scaling laws have different exponents and crossover behaviors compared to English texts, indicating different words organization and dynamics of words in the process of text growth. We propose a stochastic feedback model of words organization and text growth, which successfully accounts for the empirically observed scaling laws with their corresponding scaling exponents and characteristic crossover regimes. Further, by varying key model parameters, we reproduce differences in the organization and scaling laws of words between the Chinese and English language. We also identify functional relationships between model parameters and the empirically observed scaling exponents, thus providing new insights into the words organization and growth dynamics in the Chinese and English language.

Lithium and sodium (Na) mixed polyanion solid electrolytes for all-solid-state batteries display some of the highest ionic conductivities reported to date. However, the effect of polyanion mixing on the ion-transport properties is still not fully understood. Here, we focus on Na 1+x Zr 2 Si x P 3−x O 12 (0 ≤ x ≤ 3) NASICON electrolyte to elucidate the role of polyanion mixing on the Na-ion transport properties. Although NASICON is a widely investigated system, transport properties derived from experiments or theory vary by orders of magnitude. We use more than 2000 distinct ab initio-based kinetic Monte Carlo simulations to map the compositional space of NASICON over various time ranges, spatial resolutions and temperatures. Via electrochemical impedance spectroscopy measurements on samples with different sodium content, we find that the highest ionic conductivity (i.e., about 0.165 S cm –1 at 473 K) is experimentally achieved in Na 3.4 Zr 2 Si 2.4 P 0.6 O 12 , in line with simulations (i.e., about 0.170 S cm –1 at 473 K). The theoretical studies indicate that doped NASICON compounds (especially those with a silicon content x ≥ 2.4) can improve the Na-ion mobility compared to undoped NASICON compositions. Battery solid-state electrolytes rely on mixed polyanion networks to attain high ionic conductivities. Here, the authors investigate the effect of polyanion mixing on the solid-state electrolyte ion conductivity via theoretical calculations and electrochemical measurements.

Inkjet technology is a commendable tool in many applications including graphics printing, bioengineering and micro-electromechanical systems (MEMS). Droplet stability is a key factor influencing inkjet performance. The stability can be analysed using dimensionless numbers that usually combine thermophysical properties and system dimensions. In this paper, a drop-on-demand (DOD) inkjet experimental system is established. A numerical model is developed to investigate the influence of the operating conditions on droplet stability, including nozzle dimensions, driving parameters (the pulse amplitude and width used to drive droplet formation) and fluid properties. The results indicate that the stability can be improved by decreasing the pulse amplitude and width, decreasing the fluid density and viscosity or increasing the nozzle diameter and fluid surface tension. Based on case analysis and modelling, a dimensionless number ( $Z$ ), the reciprocal of the Ohnesorge number, is numerically determined for a stable droplet to lie in a range between 4 and 8. To explicitly combine the driving parameters, a new stability criterion, $Pj$ , is further proposed. A general rule taking into account both $Pj$ and $Z$ is proposed for choosing appropriate driving parameters to eject stable droplets for a known nozzle and fluid, which is further validated by experiments.

In 1892, J.L. Wolff proposed that bone could respond to mechanical and biophysical stimuli as a dynamic organ. This theory presents a unique opportunity for investigations on bone and its potential to aid in tissue repair. Routine activities such as exercise or machinery application can exert mechanical loads on bone. Previous research has demonstrated that mechanical loading can affect the differentiation and development of mesenchymal tissue. However, the extent to which mechanical stimulation can help repair or generate bone tissue and the related mechanisms remain unclear. Four key cell types in bone tissue, including osteoblasts, osteoclasts, bone lining cells, and osteocytes, play critical roles in responding to mechanical stimuli, while other cell lineages such as myocytes, platelets, fibroblasts, endothelial cells, and chondrocytes also exhibit mechanosensitivity. Mechanical loading can regulate the biological functions of bone tissue through the mechanosensor of bone cells intraosseously, making it a potential target for fracture healing and bone regeneration. This review aims to clarify these issues and explain bone remodeling, structure dynamics, and mechano-transduction processes in response to mechanical loading. Loading of different magnitudes, frequencies, and types, such as dynamic versus static loads, are analyzed to determine the effects of mechanical stimulation on bone tissue structure and cellular function. Finally, the importance of vascularization in nutrient supply for bone healing and regeneration was further discussed.

Previous statistical studies have described the distributions and properties of whistler‐mode waves in Jupiter's magnetosphere, but explaining these wave distributions requires modeling wave propagation from their generation near the magnetic equator. In this letter, we conduct ray tracing of whistler‐mode waves based on realistic Jovian magnetic field and density models. The ray tracing results generally agree with the statistical wave distributions based on Juno measurements. The modeled ray paths show that high‐frequency waves generated near the equator are confined within 20° magnetic latitude due to Landau damping, low‐frequency waves can propagate to higher latitudes and lower M‐shells, with changing wave normal angles, and a portion of low‐frequency waves could propagate to high M shells at high latitudes. Our modeling results provide a theoretical interpretation of whistler‐mode wave distributions and properties, providing essential insights for future radiation belt models at Jupiter. Plain Language Summary Scientists have recently been paying more attention to “whistler‐mode waves” in Jupiter's magnetosphere, as these waves play a key role in the movement of high‐energy electrons within Jupiter's radiation belts. A recent study by Ma, Li, Zhang, Kang, et al. (2024), https://doi.org/10.1029/2024gl111882, using data from NASA's Juno spacecraft, provides detailed insights into these waves, especially at frequencies lower than the “equatorial electron gyrofrequency” in Jupiter's magnetosphere. The study uncovers new information on how these waves propagate through Jupiter's magnetic fields, especially in relation to their origin and angles of inclination relative to the background magnetic field. In the present study, we use computer models to trace how these waves propagate through Jupiter's magnetosphere, based on realistic magnetic field and density conditions. Our models reveal that the waves observed at higher latitudes and farther from Jupiter likely originate near the equator at lower frequencies and evolve as they propagate. This interpretation aligns with the findings from the Juno spacecraft and helps explain how these waves propagate within Jupiter's magnetosphere. Key Points Realistic ray tracing is conducted for Jovian whistler mode waves and results are able to reproduce statistical observations High‐frequency waves originated from equator are confined within 20° latitude and separated from high‐latitude waves originated elsewhere Low‐frequency waves maintain high wave power from the equator to high latitudes and can propagate to low M with varying wave normal angles

Using particle and wave measurements from the Van Allen Probes, a 2‐D Fokker‐Planck simulation model driven by the time‐integrated auroral index (AL) value is developed. Simulations for a large sample of 186 storm‐time events are conducted, demonstrating that the AL‐driven model can reproduce flux enhancement of the MeV electrons. More importantly, the relativistic electron flux enhancement is determined by the sustained strong substorm activity. Enhanced substorm activity results in increased chorus wave intensity and reduced background electron density, which creates the required condition for local electron acceleration by chorus waves to MeV energies. The appearance of higher energy electrons in radiation belts requires a higher level of cumulative AL activity after the storm commencement, which acts as a type of switch, turning on progressively higher energies for longer and more intense substorms, at critical thresholds. Plain Language Summary The Earth's radiation belts are filled with high‐energy electrons (100s keV–10 MeV) that can damage satellites and impact the expanding human presence in space. The flux of these electrons can change by several orders of magnitude in less than a day. Thus, predicting and understanding their dynamics is crucial. We have developed a physical model driven by the integral auroral index (AL), which influences the amplitude of plasma waves and the density of the background plasma. We simulated a large sample of 186 events from 2012 to 2018 for the first time. The results show that this model not only simulates the acceleration of electrons at high energy levels but also reveals that sustained strong substorms act as a switch. High energy electrons are accelerated only when the intensity of sustained substorms exceeds a certain threshold. Key Points An AL‐driven radiation belt model reproduces the observed MeV electron fluxes for 186 storm‐time events with good performance Post‐storm flux enhancement requires sufficient integrated substorm strength which increases wave activity and decreases plasma density Accumulated substorm activity is shown to act as a switch, turning on progressively higher energies at certain critical thresholds

Nanotopographical cues of bone implant surface has direct influences on various cell types during the establishment of osseointegration, a prerequisite of implant bear-loading. Given the important roles of monocyte/macrophage lineage cells in bone regeneration and remodeling, the regulation of nanotopographies on macrophages and osteoclasts has arisen considerable attentions recently. However, compared to osteoblastic cells, how nanotopographies regulate macrophages and osteoclasts has not been properly summarized. In this review, the roles and interactions of macrophages, osteoclasts and osteoblasts at different stages of bone healing is firstly presented. Then, the diversity and preparation methods of nanotopographies are summarized. Special attentions are paid to the regulation characterizations of nanotopographies on macrophages polarization and osteoclast differentiation, as well as the focal adhesion-cytoskeleton mediated mechanism. Finally, an outlook is indicated of coordinating nanotopographies, macrophages and osteoclasts to achieve better osseointegration. These comprehensive discussions may not only help to guide the optimization of bone implant surface nanostructures, but also provide an enlightenment to the osteoimmune response to external implant.