Sandoval, David | Search NASA

DOE OSTI2024

Correlating concerted cations with oxygen redox in rechargeable batteries

Rechargeable batteries currently power much of our world, but with the increased demand for electric vehicles (EVs) capable of traveling hundreds of miles on a single charge, new paradigms are necessary for overcoming the limits of energy density, particularly in rechargeable batteries. The emergence of reversible anionic redox reactions presents a promising direction toward achieving this goal; however this process has both positive and negative effects on battery performance. While it often leads to higher capacity, anionic redox also causes several unfavorable effects such as voltage fade, voltage hysteresis, sluggish kinetics, and oxygen loss. However, the introduction of cations with topological chemistry tendencies has created an efficient pathway for achieving long-term oxygen redox with improved kinetics. The cations serve as pillars in the crystal structure and meanwhile can interact with oxygen in ways that affect the oxygen redox process through their impact on the electronic structure. This review delves into a detailed examination of the fundamental physical and chemical characteristics of oxygen redox and elucidates the crucial role that cations play in this process at the atomic and electronic scales. Furthermore, here we present a systematic summary of polycationic systems, with an emphasis on their electrochemical performance, in order to provide perspectives on the development of next-generation cathode materials.

25 ENERGY STORAGE↗

DOE OSTI2020

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C 3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C 3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R 2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R 2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R 2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr –1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

54 ENVIRONMENTAL SCIENCES↗