The cathode-electrolyte interphase plays a pivotal role in determining the usable capacity and cycling stability of electrochemical cells, yet it is overshadowed by its counterpart, the solid-electrolyte interphase. This is primarily due to the prevalence of side reactions, particularly at low potentials on the negative electrode, especially in state-of-the-art Li-ion batteries where the charge cutoff voltage is limited. However, as the quest for high-energy battery technologies intensifies, there is a pressing need to advance the study of cathode-electrolyte interphase properties. Here, we present a comprehensive approach to analyse the cathode-electrolyte interphase in battery systems. We underscore the importance of employing model cathode materials and coin cell protocols to establish baseline performance. Additionally, we delve into the factors behind the inconsistent and occasionally controversial findings related to the cathode-electrolyte interphase. In conclusion, we also address the challenges and opportunities in characterizing and simulating the cathode-electrolyte interphase, offering potential solutions to enhance its relevance to real-world applications.

25 ENERGY STORAGE↗

DOE OSTI2024

Data for EMSL Project 50414 from September 2024

Explore the source record for details and available documents.

Valiev, Marat↗

DOE OSTI2024

STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024