The oxygenation of the Earth’s surface and its role in the evolution of life on our planet remains an enigmatic debate. Estimates from conflicting studies suggest that the Proterozoic atmosphere may contain between 0.002 and 2 wt% oxygen. The stability of oxygen levels, their distribution in past atmospheres and oceans, and their drivers are also points of contention. Such questions become challenging to address because unmetamorphosed and well-dated Proterozoic sedimentary rocks that record these signatures are uncommon. To address this, we provide new carbonate geochemistry data from the Mesoproterozoic Dook Creek Formation and the Mainoru Formation in the greater McArthur Basin, northern Australia. The depositional window for these successions is constrained by U–Pb geochronology of detrital zircon and in situ carbonate U–Pb age mapping. Our geochemical data (Ce/Ce*) show that the ca 1.6–1.5 Ga units here record variable redox conditions up stratigraphy. Notably, the change in basin-water oxygen conditions appear to not have been induced by biological processes. The heterogeneity is instead interpreted to have been directly driven by the connectivity between the basin to the open ocean and improved circulation with oxic, marine shallow waters.

Auxetic materials exhibit unique properties, such as enhanced energy absorption and increased shear stiffness, making them beneficial for various applications. In this study, we employ first-principles calculations to investigate the structural, mechanical, and electronic properties of pristine and terminated Mo2B2MBenes. Our findings reveal that some hexagonal Mo2B2(OH)2configurations present a remarkable negative Poisson's ratio, indicating auxetic behaviour. This mechanical response is attributed to its unique bridging coordination of hydroxyl groups. The analysis of their structural and electronic properties establishes a clear correlation between termination type, bond characteristics, and the resulting mechanical properties. Our theoretical results, therefore, provide valuable insights for the design of novel 2D auxetic materials.

The Bracco chromitites are hosted in the Mesozoic Ligurian Ophiolites (Italy) and provide key insights into the magmatic and post-magmatic (i.e. metamorphic and hydrothermal) evolution of gabbro-hosted chromitites in an oceanic mantle. Petrographic and mineralogical analyses reveal that the Bracco chromitites comprise cumulitic, massive to disseminated, layered chromitites overprinted by multi-stage alteration within altered olivine–clinopyroxene–anorthite cumulates. Detailed Cr–Al–Fe³⁺ systematics indicates that primary Cr- to Al-rich chromite, affected by metamorphic-hydrothermal processes under subgreenschist facies conditions, locally escaped recrystallization and metasomatic modification. Consequently, chromite cores preserve their primary magmatic compositions consistent with crystallization from aluminous melts produced by low-degree partial mantle melting at a mid-ocean ridge (MOR) setting. Metamorphic-hydrothermal alteration is marked by multi-stage ferrian chromite rims, whereas based on their Mg content the associated chlorite is classified as clinochlore. Chlorite geothermometry indicates alteration temperatures in the range of ~100-300 °C, consistent with oceanic serpentinization under prehnite-pumpellyite facies conditions. The hydrothermal fluids were oxidizing, enriched in SiO₂ and MnO, and circulated through fracture networks in the shallow oceanic lithosphere. Elevated MnO amounts in alteration rims suggest widespread Mn-enrichment in these fluids, potentially linking them to seafloor Mn deposits in the Ligurian Ophiolites. Together, these findings indicate that the Bracco chromitites, their gabbroic hosts, and associated lherzolitic mantle rocks were at least partially exposed at the Tethyan seafloor prior to their final emplacement during the Alpine orogenetic phase, where serpentinization promoted complex chromite alteration

The rainy season characteristics are directly modulated by atmospheric circulation and moisture transport dynamics. Focusing on the characteristics of the rainy season onset date (RSOD), this study aims to advance the understanding and prediction of climate change impacts on agricultural production and disaster mitigation strategies. Based on rainfall data from 66 meteorological stations in northeast China (NEC) from 1961 to 2020, this study determined the patterns of the RSOD in the region and established its mechanistic linkages with atmospheric circulation and water vapor transport mechanisms. This study identifies a climatic regime shift around 2000, with the RSOD transitioning from low to high interannual variability in NEC. Further analysis reveals a strong correlation between the RSOD and atmospheric circulation characteristics: cyclonic vorticity amplifies before the RSOD and dissipates afterward. Innovatively, this study reveals a significant transition in the water vapor transport paths during the early rainy season in NEC around 2000, shifting from eastern Mongolia–Sea of Japan to the northwestern Pacific region. Moreover, the advance or delay of the RSOD directly influences the water vapor transport intensity—an early (delayed) RSOD is associated with enhanced (weakened) water vapor transport. These findings provide a new perspective for predicting the RSOD in the context of climate change while providing critical theoretical underpinnings for optimizing agricultural strategies and enhancing disaster prevention protocols.

The concept of non‐Crystalline Metal–Organic Frameworks (MOFs) is both theoretically exciting and rich in potential applications. Since their conceptual introduction, research in this field has experienced significant growth. This review provides a comprehensive overview of the design, synthesis, and applications of non‐crystalline MOFs, highlighting the current state of the art. It examines the fundamental principles of non‐crystalline MOFs, the various synthetic approaches, and the nature of non‐crystalline MOFs. Additionally, the review outlines their pathway from the laboratory to industrial applications, emphasizing challenges and opportunities for further development.

Abstract The phosphates, apatite and merrillite, are accessory phases in all martian meteorites. Although apatite is commonly used to assess volatile content and speciation in martian meteorites, merrillite is at least twice as abundant in most samples, but poorly understood. Given that shergottites are divided into enriched, intermediate, and depleted subgroups based on bulk differences in light rare earth element (LREE) abundance and isotopic compositions, an understanding of phosphate mineral behavior is essential to deciphering the petrogenetic differences between these groups because they are the main REE‐bearing phases. This study examines 10 enriched shergottites, six intermediate shergottites, and four depleted shergottites to investigate systematic variations in phosphate mineralogy and geochemistry. Two nakhlites, a chassignite, ALH 84001, and two pairs of NWA 7034 were also examined to cover all martian meteorite types known to date. Fourteen of the shergottites were previously classified into enriched, intermediate, and depleted subgroups based on bulk rock REE trends and La/Yb ratios. The remaining six shergottites had not been subgrouped during classification. All samples were elementally mapped using the XFM beamline at the Australian Synchrotron, which provided the relative abundance of merrillite, apatite, K‐feldspar, and maskelynite within each sample (the same can be achieved with electron microprobe or SEM). We show that it is possible to classify shergottites from a single representative thin section using apatite to merrillite ratios (A10/M, where A10 is apatite abundance × 10) and K‐feldspar to phosphate ratios (K10/P, where K10 is K‐feldspar abundance × 10). Enriched shergottites typically have A10/M of 1.08 to 8.72 and K10/P of 1.85 to 13.34; intermediate shergottites have A10/M ranging from 0.5 to 0.96 and K10/P of 0.36 to 0.94; and depleted shergottites have A10/M ranging from 0.26 to 0.42 and K10/P of 0.09 to 0.39. Calculating these ratios thus provides a quick and straightforward method of chemically classifying shergottites that avoids the need to destroy samples for bulk rock REE analysis.

Canopy temperature is an important indicator of plants’ water status. The so-called PeriodiCT model was developed to forecast canopy temperature using ambient weather variables, providing a powerful tool for planning crop irrigation scheduling. As this model requires observed data in its parameter training before implementing the forecast, it is important to understand the data requirements in the model training such that accurate forecasts are attained. In this work, we conduct a comprehensive assessment of the PeriodiCT model in terms of sample size requirement and predictabilities across sensors in a field and across seasons for the full model and sub-models. The results show that (1) 5 days’ observations are sufficient for the full model and sub-models to achieve very high predictability, with a minimum coefficient of efficiency of 0.844 for the full model and 0.840 for the sub-model using only air temperature. The predictability decreases in the following order: full model, sub-model without radiation S, with air temperature Ta and vapor pressure VP, and with only Ta. The predictions perform reasonably well even when only one day’s observations are used. (2) The predictability into the future is very stable as the prediction steps increase. (3) The predictabilities of the full and sub-models when using a trained model from one sensor for another sensor perform comparatively well, with a minimum coefficient of efficiency of 0.719 for the full model and 0.635 for the sub-model using only air temperature. (4) The predictabilities of the sub-models without solar radiation when using trained models from one season for another season perform comparatively well, with a minimum coefficient of efficiency of 0.866 for the full model and 0.764 for the sub-model using only air temperature, although the cross-season performances are not as good as the cross-sensor performances. The importance of the predictors is in the order of air temperature, vapor pressure, wind speed, and solar radiation, while vapor pressure and wind speed have similar contributions, and solar radiation has only a marginal contribution.

Community detection plays a crucial role in understanding the structural organization of complex networks. Previous methods, particularly those from statistical physics, primarily focus on the analysis of mesoscopic network structures and often struggle to integrate fine-grained node similarities. To address this limitation, we propose a low-complexity framework that integrates machine learning to embed micro-level node-pair similarities into mesoscopic community structures. By leveraging ensemble learning models, our approach enhances both structural coherence and detection accuracy. Experimental evaluations on artificial and real-world networks demonstrate that our framework consistently outperforms conventional methods, as well as state-of-the-art embedding-based and learning-based approaches, achieving higher modularity and improved accuracy in normalized mutual information and adjusted rand index. Notably, even in the complete absence of ground-truth community information, our approach still achieves substantial improvements in algorithmic accuracy based on the principles of statistical-physics methods. When ground-truth labels are available, it yields the most accurate detection results, effectively recovering real-world community structures while minimizing misclassifications. To further explain the performance of our framework, we analyze the correlation between node-pair similarity and evaluation metrics. The results reveal a strong and statistically significant correlation, underscoring the critical role of node-pair similarity in enhancing detection accuracy. Overall, our findings highlight the synergy between machine learning and statistical physics, demonstrating how machine learning techniques can enhance network analysis and uncover complex structural patterns.