Thermal Equations of State of Corundum and Rh[subscript 2]O[subscript 3] (II)-Type Al[subscript 2]O[
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Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.
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The phase distribution of lithiated LVO in thick (~500 μm) porous electrodes (TPEs) designed to facilitate both ion and electron transport was determined using synchrotron-based operando energy dispersive X-ray diffraction (EDXRD). Probing 3 positions in the TPE while cycling at a 1C rate revealed a homogeneous phase transition across the thickness of the electrode at the 1st and 95th cycles. Additionally, continuum modelling indicated uniform lithiation across the TPE in agreement with the EDXRD results and ascribed decreasing accessible active material to be the cause of loss in delivered capacity between the 1st and 95th cycles. The model was supported by the observation of significant particle fracture by SEM consistent with loss of electrical contact. Overall, the combination of operando EDXRD, continuum modeling, and ex situ measurements enabled a deeper understanding of lithium vanadium oxide transport properties under high rate extended cycling within a thick highly porous electrode architecture.
A free-standing film composed of bilayered vanadium oxide nanoflakes is for the first time synthesized using a new low-energy process.
Synchrotron X-ray scattering reveals that atomic-scale defect control manipulated by chemical substitution optimizes nanomagnetism and enables hyperthermia heating in core-shell iron-oxide nanocrystals.
Solid state UV-vis reflectance spectra reveal the distinct electronic structure of POM@MOF materials obtained by synthetic encapsulation of mono- and di-vanadium substituted Keggin polyoxotungstates in HKUST-1.
This manuscript examines the mechanism of strain-coupling in a multiferroic composite of mesoporous cobalt ferrite (CFO), conformally filled with lead zirconate titanate (PZT). We find that when the composites are electrically poled, remanent strain from the piezoelectric PZT layer can be transferred to the magnetostrictive CFO layer. X-ray diffraction shows that this strain transfer is greatest in the most porous samples, in agreement with magnetometry measurements, which show the greatest change in sample saturation magnetization in the most porous samples. In conclusion, strain analysis shows that porosity both accommodates greater lattice strain and mitigates the effects of substrate clamping in thin film strain-coupled composites.
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Efficient RNA capping by the SARS-CoV-2 methyltransferase requires metal ions and a unique four-residue insert.