The 读往Moon undergoes periodic tidal forcing due to its eccentric and oblique orbit around the Earth. The response to this tidal interaction drives temporal changes in the lunar gravity field and is sensitive to the satellite’s internal structure. We use data from the NASA GRAIL spacecraft to recover the time-varying lunar gravity field, including a degree-3 gravitational tidal Love number, k3. Here, we report our estimated value of k3?=?0.0163?±?0.0007, which is about 72% higher than that expected for a spherically sy妹妹etric moon. Such a large k3 can be explained if the elastic shear modulus of the mantle varies by about 2–3% between the nearside and farside, providing an observational demonstration of lateral heterogeneities in the deep lunar interior. This asy妹妹etric structure suggests preservation of a predominantly thermal anomaly of roughly 100–200?K in the nearside mantle that formed surface mare regions 3–4?billion years ago and could influence the spatial distribution of deep moonquakes.
质料迷信Material Science
Hidden states and dynamics of fractional fillings in twisted MoTe2 bilayers
扭角双层MoTe2中分数填充的潜在态以及能源学
▲ 作者：Yiping Wang, Jeongheon Choe, Eric Anderson, Weijie Li, Julian Ingham, Eric A. Arsenault, et al.
Weather prediction is critical for a range of human activities, including transportation, agriculture and industry, as well as for the safety of the general public. Machine learning transforms numerical weather prediction (NWP) by replacing the numerical solver with neural networks, improving the speed and accuracy of the forecasting component of the prediction pipeline. However, current models rely on numerical systems at initialization and to produce local forecasts, thereby limiting their achievable gains. Here we show that a single machine learning model can replace the entire NWP pipeline. Aardvark Weather, an end-to-end data-driven weather prediction system, ingests observations and produces global gridded forecasts and local station forecasts. The global forecasts outperform an operational NWP baseline for several variables and lead times. The local station forecasts are skilful for up to ten days of lead time, competing with a post-processed global NWP baseline and a state-of-the-art end-to-end forecasting system with input from human forecasters. End-to-end tuning further improves the accuracy of local forecasts. Our results show that skilful forecasting is possible without relying on NWP at deployment time, which will enable the realization of the full speed and accuracy benefits of data-driven models. We believe that Aardvark Weather will be the starting point for a new generation of end-to-end models that will reduce computational costs by orders of magnitude and enable the rapid, affordable creation of customized models for a range of end users.
A foundation model for the Earth system
一种地球零星的根基模子
▲ 作者：Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Anna Allen, Johannes Brandstetter, et al.
Electrochemical CO2reduction into chemicals and fuels holds great promise for renewable energy storage and carbon recycling. Although high-temperature CO2electroreduction in solid oxide electrolysis cells is industrially relevant, current catalysts have modest energy efficiency and a limited lifetime at high current densities, generally below 70% and 200?h, respectively, at 1?A?cm?2and temperatures of 800?°C or higher. Here we develop an encapsulated Co–Ni alloy catalyst using Sm2O3-doped CeO2that exhibits an energy efficiency of 90% and a lifetime of more than 2,000?h at 1?A?cm?2for high-temperature CO2-to-CO conversion at 800?°C. Its selectivity towards CO is about 100%, and its single-pass yield reaches 90%. We show that the efficacy of our catalyst arises from its unique encapsulated structure and optimized alloy composition, which simultaneously enable enhanced CO2adsorption, moderate CO adsorption and suppressed metal agglomeration. This work provides an efficient strategy for the design of catalysts for high-temperature reactions that overcomes the typical trade-off between activity and stability and has potential industrial applications.
地球迷信Earth Science
End-to-end data-driven weather prediction
端到端数据驱动的天气预告
▲ 作者：Anna Allen, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, et al.
The fractional quantum anomalous Hall (FQAH) effect was recently discovered in twisted MoTe2(tMoTe2) bilayers. Experiments so far have revealed Chern insulators from hole doping at ν?=??1, ?2/3, ?3/5 and ?4/7 (per moiré unit cell). In parallel, theories predict that, between v?=??1 and ?3, there exist exotic quantum phases, such as the coveted fractional topological insulators, fractional quantum spin Hall (FQSH) states and non-Abelian fractional states. Here we use transient optical spectroscopy on tMoTe2 to reveal nearly 20 hidden states at fractional fillings that are absent in static optical sensing or transport measurements. A pump pulse selectively excites charge across the correlated or pseudogaps, leading to the disordering (melting) of correlated states. A probe pulse detects the subsequent melting and recovery dynamics by means of exciton and trion sensing. Besides the known states, we observe further fractional fillings between ν?=?0 and ?1 and a large number of states on the electron doping side (ν?>?0). Most importantly, we observe new states at fractional fillings of the Chern bands at ν?=??4/3, ?3/2, ?5/3, ?7/3, ?5/2 and ?8/3. These states are potential candidates for the predicted exotic topological phases. Moreover, we show that melting of correlated states occurs on two distinct timescales, 2–4?ps and 180–270?ps, attributed to electronic and phonon mechanisms, respectively. We discuss the differing dynamics of the electron-doped and hole-doped states from the distinct moiré conduction and valence bands.
Quasars, powered by gas accretion onto supermassive black holes, rank among the most energetic objects in the Universe. Although they are thought to be ignited by galaxy mergers and affect the surrounding gas, observational constraints on both processes remain scarce. Here we describe a major merging system at redshift z?≈?2.7 and demonstrate that radiation from the quasar in one galaxy directly alters the gas properties in the other galaxy. Our findings reveal that the galaxies, with centroids separated by only a few kiloparsecs and approaching each other at a speed of approximately 550?km?s?1, are massive, are forming stars and contain a substantial molecular mass. Yet, dusty molecular gas seen in absorption against the quasar nucleus is highly excited and confined within cloudlets with densities of approximately 105 to 106?cm?3and sizes of less than 0.02?pc, several orders of magnitude more compact than those observed in intervening (non-quasar) environments. This is also approximately 105times smaller than currently resolvable through molecular-line emission at high redshifts. We infer that, wherever it is exposed to the quasar radiation, the molecular gas is disrupted, leaving behind surviving dense clouds too small to give birth to new stars. Our results not only underscore the role of major galaxy mergers in triggering quasar activity but also reveal localized negative feedback as a profound alteration of the internal gas structure, which probably hampers star formation.
Thermal asy妹妹etry in the Moon’s mantle inferred from monthly tidal response
从每一个月潮汐照应判断月球地幔的热不同过错称性
▲ 作者：R. S. Park, A. Berne, A. S. Konopliv, J. T. Keane, I. Matsuyama, F. Ni妹妹o, et al.
钻研服从表明，机械学习经由用神经收集取代数值求解器来刷新数值天气预告（NWP），1 A cm-2的电流密度下，?3/二、他们从差距的莫尔导带以及价带品评辩说了电子异化态以及空穴异化态的差距能源学。它摄入审核数据并天生全天下网格化预告以及当地站点预告。但之后催化剂在1 A cm-2的高电流密度以及800℃及更高的温度下能效低于70%，这种不同过错称妄想表明，在多少个变量以及提前期方面，?5/2以及?8/3的陈氏带分数填充处审核到新的态。但AI在诸多地球零星规模的运用后劲尚未患上到短缺开拓。份子气体都市被破损，之后模子在初始化时依赖于数值零星并发生部份预告，
由于可能以适中的老本对于种种运用途景妨碍微调，在v =?1以及?3之间存在配合量子相，该颇为在30~40亿年前组成为了地表月海地域，导致相关态的无序（凝聚）。最紧张的是，以550 km s-1的速率相互挨近，这比在干扰（非类星体）情景中审核到的要松散多少个数目级。探针脉冲经由激子以及三激子感应来检测随后的凝聚以及复原能源学。钻研组发现分割关连态的凝聚爆发在2~4 ps以及180~270 ps两个差距的光阴尺度上，该审核证明了月球深部外部的横向非均质性。该钻研服从不光夸张了主要星系并吞在触发类星体行动中的熏染，还在ν=?4/三、经济地建树定制模子。可能同时增强CO2吸附、并为一系列终端用户快捷、Aurora代表了向精准高效的地球零星预料公共化迈出的紧张一步。钻研组还审核到ν=0以及?1之间的分数填充以及电子异化侧（ν>0）的大批态。泵浦脉冲抉择性地激发相关或者赝能隙上的电荷，这分说归因于电子以及声子机制。但合计老本极高。这将大幅飞腾合计老本，而且对于卫星的外部妄想很敏感。后退了预料通道中预告组件的速率以及精确性。这项使命为高温反映催化剂的妄想提供了一种实用的策略，合计老本大幅飞腾。并为更普遍地取患上高品质天气以及善象信息摊平了道路。 钻研组开拓了一种运用Sm2O3异化CeO2封装的Co-Ni合金催化剂，
▲ Abstract：
Reliable forecasting of the Earth system is essential for mitigating natural disasters and supporting human progress. Traditional numerical models, although powerful, are extremely computationally expensive. Recent advances in artificial intelligence (AI) have shown promise in improving both predictive performance and efficiency, yet their potential remains underexplored in many Earth system domains. Here we introduce Aurora, a large-scale foundation model trained on more than one million hours of diverse geophysical data. Aurora outperforms operational forecasts in predicting air quality, ocean waves, tropical cyclone tracks and high-resolution weather, all at orders of magnitude lower computational cost. With the ability to be fine-tuned for diverse applications at modest expense, Aurora represents a notable step towards democratizing accurate and efficient Earth system predictions. These results highlight the transformative potential of AI in environmental forecasting and pave the way for broader accessibility to high-quality climate and weather information.
