Floodplain lakes represent important aquatic ecosystems, and field-based estimates of their water budgets are difficult to obtain, especially over multiple years. We examine the hydrological fluxes for an Amazon floodplain lake connected to the Solimões River using a process-based hydrologic model. Water exchanges between the river and lake agree well with field estimates, including the timing of different hydrological phases. However, beyond available field data, modeling results show that the seven simulated years all differed from each other. These interannual differences were caused by the interplay between phases when water levels were rising with river-water flowing into the lake (RWRI), versus rising with lake-water flowing out to the river (RWLO). This exchange determines the river-water content in the lake (CL). Maximum CL occurred before river levels peaked because local catchment contributions can be sufficient to push lake-water out to the river, even as river levels rise. Numerical experiments show that the seasonal distribution of local rainfall, local catchment size, and interannual variability in both climate and river stage can contribute to differing dynamics of CL in a floodplain lake. Their impacts vary among phases: river-rise dominates the RWRI, whereas local hydrological processes dominate the RWLO and receding-water phases. Intermediate-to-long-term rainfall accumulation controls CL during the RWLO phase, whereas annual precipitation accumulation is important for CL during low water. Our model generalizes beyond limited available field studies and offers potential to better understand floodplain lakes in other areas and how regional versus local changes in climate may affect their hydrological dynamics.

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

With increasing battery demand comes a need for diversified Li sources beyond brines. Among all Li-bearing minerals, spodumene is most often used for its high Li content and natural abundance. However, the traditional approach to process spodumene is costly and energy-intensive, requiring the mineral be transformed from its natural α to β phase at >1000 °C. Acid leaching is then applied, followed by neutralization to precipitate Li₂CO₃. In this work, we report an alternative method to extract Li directly from α-spodumene, which is performed at lower temperatures and avoids the use of acids. It is shown that Li₂CO₃ is formed with >90% yield at 750 °C by reacting α-spodumene with Na₂CO₃ and Al₂O₃. The addition of Al₂O₃ is critical to reduce the amount of Li₂SiO₃ that forms when only Na₂CO₃ is used, instead providing increased thermodynamic driving force to form NaAlSiO₄ and Li₂CO₃ as the sole products. We find that this reaction is most effective at 4 h, after which volatility limits the yield. Following its extraction, Li₂CO₃ can be isolated by washing the sample using deionized water. This energy-saving and acid-free route to obtain Li₂CO₃ directly from spodumene can help meet the growing demand for Li.

Disordered rocksalt cathodes exhibit high specific capacities and high energy density; however, their low electronic conductivity poses a great challenge. Herein, we explored an aqueous-solution-based synthesis route that involves controlling the surface charges of Li1.2Mn0.6Ti0.2O1.8F0.2 (LMTOF) to be anchored by a few-layer reduced graphene oxide (rGO) for the first time. The uniform rGO wrapping on the surface of the LMTOF particles is achieved by electrostatic attraction between the negatively charged rGO and positively charged LMTOF particles. Although the initial specific capacity of rGO-LMTOF composite increased by 58 % compared to the pristine LMTOF, the composite experienced a severe capacity fade over cycling. The synthesis process in an aqueous medium resulted in Li+/H+ exchange and TM dissolution as evidenced from inductively coupled plasmon analysis and X-ray diffraction analysis. Therefore, this work suggests the search for alternative media or conditions for the synthesis of carbon-disordered rock salt cathode composite.