类星体由超大质量黑洞的气体吸积供能，是读新宇宙中能量最高的天体之一。虽然类星体被认为由星系合并引发，学网并影响周围的自然周论气体，但对这两个过程的出版观测限制仍然很少。 研究组报道了一个红移z≈2.7的文导闻科主要合并系统，并证明一个星系中类星体的读新辐射直接改变了另一个星系的气体性质。该发现表明，学网这些星系质量巨大，自然周论质心相距只有几千秒差距，出版以550 km s-1的文导闻科速度彼此接近，它们正在形成恒星，读新并含有大量的学网%E3%80%90WhatsApp%20+86%2015855158769%E3%80%91makita%207.2-%2018v%20portable%20radio%20review分子质量。
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 asymmetry in the Moon’s mantle inferred from monthly tidal response
从每月潮汐响应推断月球地幔的热不对称性
▲ 作者：R. S. Park, A. Berne, A. S. Konopliv, J. T. Keane, I. Matsuyama, F. Nimmo, et al.
The 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 symmetric 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 asymmetric 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.
材料科学Material Science
Hidden states and dynamics of fractional fillings in twisted MoTe2 bilayers
扭角双层MoTe2中分数填充的隐藏态和动力学
▲ 作者：Yiping Wang, Jeongheon Choe, Eric Anderson, Weijie Li, Julian Ingham, Eric A. Arsenault, 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.
电化学将CO2还原为化学物质和燃料在可再生能源储存和碳回收方面具有很大的前景。虽然固体氧化物电解池中的高温CO2电还原具有工業價值，但當前催化劑在1 A cm-2的高電流密度和800℃及更高的溫度下能效低于70%，壽命僅為200 h。 研究组开发了一种使用Sm2O3掺杂CeO2封装的Co-Ni合金催化剂，在800℃高温、1 A cm-2的电流密度下，CO2转化为CO的能量效率为90%，寿命超过2000小时。其对CO的选择性约为100%，单程收率达90%。
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.
地球科学Earth Science
End-to-end data-driven weather prediction
端到端数据驱动的天气预报
▲ 作者：Anna Allen, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, 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
一种地球系统的基础模型
▲ 作者：Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Anna Allen, Johannes Brandstetter, et al.
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.
?
编译|未玖
Nature,自然周论 29 May 2025, Volume 641, Issue 8065
《自然》2025年5月29日，第641卷，出版8065期
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天文学Astronomy
Quasar radiation transforms the gas in a merging companion galaxy
类星体辐射改变合并伴星系气体性质
▲ 作者：Sergei Balashev, Pasquier Noterdaeme, Neeraj Gupta, Jens-Kristian Krogager, Fran?oise Combes, Sebastián López, et al.
▲链接：
https://www.nature.com/articles/s41586-025-08966-4
▲摘要：
类星体由超大质量黑洞的气体吸积供能，是读新宇宙中能量最高的天体之一。虽然类星体被认为由星系合并引发，学网并影响周围的自然周论气体，但对这两个过程的出版观测限制仍然很少。
研究组报道了一个红移z≈2.7的文导闻科主要合并系统，并证明一个星系中类星体的读新辐射直接改变了另一个星系的气体性质。该发现表明，学网这些星系质量巨大，自然周论质心相距只有几千秒差距，出版以550 km s-1的文导闻科速度彼此接近，它们正在形成恒星，读新并含有大量的学网%E3%80%90WhatsApp%20+86%2015855158769%E3%80%91makita%207.2-%2018v%20portable%20radio%20review分子质量。
然而，在类星体核的吸收中观测到的尘埃分子气体被高度激发，并被限制在密度约为105至106 cm?3、尺寸小于0.02 pc的小云团中，这比在干扰（非类星体）环境中观察到的要紧凑几个数量级。这也比目前通过高红移分子线发射可分辨的小约105倍。
研究组推断，无论在哪里暴露于类星体辐射，分子气体都会被破坏，留下幸存的致密云太小以至于无法产生新的恒星。该研究结果不仅强调了主要星系合并在触发类星体活动中的作用，还揭示了局部负反馈作为内部气体结构的深刻改变，这可能阻碍了恒星的形成。
▲ Abstract：
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 asymmetry in the Moon’s mantle inferred from monthly tidal response
从每月潮汐响应推断月球地幔的热不对称性
▲ 作者：R. S. Park, A. Berne, A. S. Konopliv, J. T. Keane, I. Matsuyama, F. Nimmo, et al.
▲链接：
https://www.nature.com/articles/s41586-025-08949-5
▲摘要：
月球因绕地球的偏心和倾斜轨道而经历了周期性的潮汐强迫。这种潮汐相互作用的响应驱动了月球重力场的时间变化，并且对卫星的内部结构很敏感。
研究组使用美国航空航天局GRAIL航天器的数据来恢复随时间变化的月球重力场，包括3级引力潮汐勒夫数k3。他们报告了k3 = 0.0163±0.0007的估计值，这比球对称卫星的估计值高出约72%。
当地幔的弹性剪切模量在近端和远端之间的变化约为2~3%时，才能解释如此大的k3，该观测证明了月球深部内部的横向非均质性。这种不对称结构表明，在近侧地幔中保存了大约100~200K的主要热异常，该异常在30~40亿年前形成了地表月海区域，并可能影响深月震的空间分布。
▲ Abstract：
The 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 symmetric 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 asymmetric 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.
材料科学Material Science
Hidden states and dynamics of fractional fillings in twisted MoTe2 bilayers
扭角双层MoTe2中分数填充的隐藏态和动力学
▲ 作者：Yiping Wang, Jeongheon Choe, Eric Anderson, Weijie Li, Julian Ingham, Eric A. Arsenault, et al.
▲链接：
https://www.nature.com/articles/s41586-025-08954-8
▲摘要：
近年來人們在扭角双层MoTe2（tMoTe2）中发现了分数量子反常霍尔（FQAH）效应。到目前为止，实验已发现了在ν =?1、?2/3、?3/5和?4/7（每莫尔晶胞）处空穴掺杂的陈氏绝缘体。同时，理论预测，在v =?1和?3之间存在奇异量子相，如极难获得的分数拓扑绝缘体、分数量子自旋霍尔（FQSH）态和非阿贝尔分数态。
研究组使用瞬态光学光谱对tMoTe2进行分析，揭示了在静态光学传感或传输测量中不存在的分数填充处的近20个隐藏态。泵浦脉冲选择性地激发相关或赝能隙上的电荷，导致相关态的无序（熔化）。探针脉冲通过激子和三激子感应来检测随后的熔化和恢复动力学。
除了已知的状态外，研究组还观察到ν=0和?1之间的分数填充和电子掺杂侧（ν>0）的大量态。最重要的是，还在ν=?4/3、?3/2、?5/3、?7/3、?5/2和?8/3的陈氏带分数填充处观察到新的态。这些态是预测奇异拓扑相的潜在候选者。
此外，研究组发现关联态的熔化发生在2~4 ps和180~270 ps两个不同的时间尺度上，这分别归因于电子和声子机制。他们从不同的莫尔导带和价带讨论了电子掺杂态和空穴掺杂态的不同动力学。
▲ Abstract：
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.
化学Chemistry
Encapsulated Co–Ni alloy boosts high-temperature CO2electroreduction
封装Co-Ni合金促进高温CO2电还原
▲ 作者：Wenchao Ma, Jordi Morales-Vidal, Jiaming Tian, Meng-Ting Liu, Seongmin Jin, Wenhao Ren, et al.
▲链接：
https://www.nature.com/articles/s41586-025-08978-0
▲摘要：
电化学将CO2还原为化学物质和燃料在可再生能源储存和碳回收方面具有很大的前景。虽然固体氧化物电解池中的高温CO2电还原具有工業價值，但當前催化劑在1 A cm-2的高電流密度和800℃及更高的溫度下能效低于70%，壽命僅為200 h。
研究组开发了一种使用Sm2O3掺杂CeO2封装的Co-Ni合金催化剂，在800℃高温、1 A cm-2的电流密度下，CO2转化为CO的能量效率为90%，寿命超过2000小时。其对CO的选择性约为100%，单程收率达90%。
研究结果表明，该催化剂的能效源于其独特的封装结构和优化的合金组成，能够同时增强CO2吸附、适度CO吸附和抑制金属团聚。这项工作为高温反应催化剂的设计提供了一种有效的策略，克服了活性和稳定性之间的传统权衡，具有潜在的工业应用价值。
▲ Abstract：
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.
地球科学Earth Science
End-to-end data-driven weather prediction
端到端数据驱动的天气预报
▲ 作者：Anna Allen, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, et al.
▲链接：
https://www.nature.com/articles/s41586-025-08897-0
▲摘要：
天气预报对包括交通、农业、工业以及公众安全的一系列人类活动至关重要。机器学习通过用神经网络取代数值求解器来变革数值天气预报（NWP），提高了预测通道中预报组件的速度和准确性。然而，当前模型在初始化时依赖于数值系统并产生局部预报，从而限制了其可实现的收益。
研究組展示了單個機器學習模型可取代整個NWP通道。Aardvark Weather是一個端到端数据驱动的天气预报系統，它攝取觀測數據并生成全球網格化預報和本地站點預報。在幾個變量和提前期方面，全球預測的表現優于運營NWP基線。當地氣象站的預報在長達十天的提前期內都很熟練，可媲美后處理的全球NWP基線以及由人類預報員輸入的最先進的端到端預報系統。端到端調優進一步提高了本地預測的準確性。
该研究结果表明，在部署时不依赖NWP，也可以进行熟练的预测，这实现了数据驱动模型的全部速度优势和准确性优势。研究组希冀，Aardvark Weather将成为新一代端到端模型的起点，这将大幅降低计算成本，并为一系列终端用户快速、经济地创建定制模型。
▲ Abstract：
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
一种地球系统的基础模型
▲ 作者：Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Anna Allen, Johannes Brandstetter, et al.
▲链接：
https://www.nature.com/articles/s41586-025-09005-y
▲摘要：
可靠预测地球系统对于减轻自然灾害和支持人类进步至关重要。传统的数值模型虽然功能强大，但计算成本极高。人工智能（AI）的最新进展在提高预测性能和效率方面颇有前景，但AI在诸多地球系统领域的应用潜力尚未得到充分开发。
研究组介绍了Aurora，这是一个基于一百多万小时多样化地球物理数据训练的大规模基础模型。Aurora在预测空气质量、海浪、热带气旋路径和高分辨率天气方面均优于业务预测，计算成本大幅降低。
由于能够以适中的成本对各种应用场景进行微调，Aurora代表了向精准高效的地球系统预测大众化迈出的重要一步。这些结果凸显了AI在环境预测方面的变革潜力，并为更广泛地获取高质量气候和天气信息铺平了道路。
▲ 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.
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