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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 C3 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 C3 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 R2 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 R2 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).

Stomatal opening in plant leaves is regulated through a balance of carbon and water exchange under different environmental conditions. Accurate estimation of stomatal regulation is crucial for understanding how plants respond to changing environmental conditions, particularly under climate change. A new generation of optimality‐based modelling schemes determines instantaneous stomatal responses from a balance of trade‐offs between carbon gains and hydraulic costs, but most such schemes do not account for biochemical acclimation in response to drought. Here, we compare the performance of six instantaneous stomatal optimisation models with and without accounting for photosynthetic acclimation. Using experimental data from 37 plant species, we found that accounting for photosynthetic acclimation improves the prediction of carbon assimilation in a majority of the tested models. Photosynthetic acclimation contributed significantly to the reduction of photosynthesis under drought conditions in all tested models. Drought effects on photosynthesis could not accurately be explained by the hydraulic impairment functions embedded in the stomatal models alone, indicating that photosynthetic acclimation must be considered to improve estimates of carbon assimilation during drought. Summary statement: Accounting for photosynthetic acclimation improves the predictions of carbon assimilation in all the stomatal optimisation models evaluated. The influence of drought on photosynthesis cannot be fully explained by the hydraulic impairment function of the stomatal models alone. [ABSTRACT FROM AUTHOR]

The first example of a general asymmetric nitrosocarbonyl hetero-ene reaction is described. The procedure uses a copper-catalyzed aerobic oxidation of a commercially available chiral nitrosocarbonyl precursor (EleNOr) and is operationally simple. The transformation is both high yielding and highly diastereoselective for a range of silyl enol ether derivatives. A variety of synthetically useful postfunctionalization reactions are presented along with a mechanistic rationale that can be used as a predictive model for future asymmetric reactions with nitrosocarbonyl intermediates.

The current representation of key processes in land surface models (LSMs) for estimating water and energy balances still relies heavily on empirical equations that require calibration oriented to site-specific characteristics. When multiple parameters are used, different combinations of parameter values can produce equally acceptable results, leading to a risk of obtaining "the right answers for the wrong reasons", compromising the reproducibility of the simulations and limiting the ecological interpretability of the results. To address this problem and reduce the need for free parameters, here we present novel formulations based on first principles to calculate key components of water and energy balances, extending the already parsimonious SPLASH model v.1.0 (, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent. These quantities are derived from readily available meteorological data such as near-surface air temperature, precipitation, and solar radiation, as well as soil physical properties. Whenever empirical formulations were required, e.g., pedotransfer functions and albedo–snow cover relationships, we selected and optimized the best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs. [ABSTRACT FROM AUTHOR]

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, 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. [ABSTRACT FROM AUTHOR]

A copper-catalyzed aminooxylation of β-ketoesters using transient nitrosoformate intermediates is reported. The transformation is highly practical, efficient and highlights the ambident reactivity of nitrosocarbonyl compounds through a rare example of a nitrosocarbonyl aldol reaction. Along with a broad substrate scope, the reaction conditions that help control the regiochemistry are explored and the use of N-carbamate protected hydroxylamine is showcased by subsequent one-pot annulation reactions. © 2013 The Royal Society of Chemistry.

The current representation of key processes in Land Surface Models for estimating water and energy balances still relies heavily on empirical equations that require site-specific calibration. When multiple parameters are used, different combinations of parameter values can produce equally acceptable results leading to a risk of obtaining "right answers for wrong reasons", compromising the reproducibility of the simulations and limiting the ecological interpretability of the results. To reduce the need for free parameters, here we present novel formulations based on first-principles to calculate key components of water and energy balances, extending the already parsimonious SPLASH v.1.0 model (Davis et al. 2017, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow and snow-water equivalent. These quantities are derived from readily meteorological data such as near-surface air temperature, precipitation and solar radiation, and soil physical properties. Whenever empirical formulations were required, we selected and optimized the best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs. [ABSTRACT FROM AUTHOR]