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Phyllosilicate minerals, due to their sheets structure and morphology, are known to cause anisotropy in bulk rock properties and make the bulk rock more compliant. Accurately characterizing the micromechanical behavior of phyllosilicate minerals from laboratory observations, which eventually translates to the bulk rock behavior, is still challenging due to their fine‐grained nature. Recent advances in atomistic simulations open the possibility of theoretically investigating such mineral mechanical behavior. We compare the elastic properties of biotites recovered by spherical nanoindentation with those predicted from density functional theory (DFT) simulations to investigate to what extent theoretical predictions reproduce actual phyllosilicate properties. Spherical nanoindentation was conducted using schist rocks from Poorman Formation, South Dakota, USA, to recover continuous indentation stress‐strain curves. Loading in the layer‐normal orientation shows an average indentation modulus () of about 35 GPa, while loading in the layer‐parallel orientation gives a higher average of about 95 GPa. To facilitate comparison, the elastic stiffness constants (c_ij) determined from DFT were converted to indentation modulus () using solutions proposed in this study. The majority of the nanoindentation modulus results are below the values inferred from the simulation results representing ideal defect‐free minerals. We suggest that crystal defects present at the nano‐scale, potentially ripplocations, are the dominant cause of the lower indentation modulus recovered from nanoindentation compared to those inferred from DFT simulations. Results highlight the importance of acknowledging the defects that exist down to the nano‐scale as it modifies the mechanical properties of phyllosilicates compared to its pure defect‐free form.

In situ growth of pyrochlore iridate thin films has been a long-standing challenge due to the low reactivity of Ir at low temperatures and the vaporization of volatile gas species such as IrO₃(g) and IrO₂(g) at high temperatures and highP_O2. To address this challenge, we combine thermodynamic analysis of the Pr-Ir-O₂system with experimental results from the conventional physical vapor deposition (PVD) technique of co-sputtering. Our results indicate that only high growth temperatures yield films with crystallinity sufficient for utilizing and tailoring the desired topological electronic properties and the in situ synthesis of Pr₂Ir₂O₇thin films is fettered by the inability to grow withP_O2on the order of 10 Torr at high temperatures, a limitation inherent to the PVD process. Thus, we suggest techniques capable of supplying high partial pressure of key species during deposition, in particular chemical vapor deposition (CVD), as a route to synthesis of Pr₂Ir₂O₇.

There is significant interest in approaches to the treatment of bacterial infections that block virulence without creating selective pressures that lead to resistance. Here, we report the development of an “anti‐virulence” strategy that exploits the activity of potent synthetic inhibitors of quorum sensing (QS) inStaphylococcus aureus. We identify peptide‐based inhibitors of QS that are resistant to sequestration or degradation by components of murine tissue and demonstrate that encapsulation of a lead inhibitor in degradable polymer microparticles provides materials that substantially inhibit QSin vitro. Using a murine abscess model, we show that this inhibitor attenuates methicillin‐resistantS. aureus(MRSA) skin infectionsin vivo, and that sustained release of the inhibitor from microparticles significantly improved outcomes compared to mice that received a single‐dose bolus. Our results present an effective and modular approach to controlling bacterial virulencein vivoand could advance the development of new strategies for skin infection control.

In this article, we review the mathematical foundations of convolutional neural nets (CNNs) with the goals of: (i) highlighting connections with techniques from statistics, signal processing, linear algebra, differential equations, and optimization, (ii) demystifying underlying computations, and (iii) identifying new types of applications. CNNs are powerful machine learning models that highlight features from grid data to make predictions (regression and classification). The grid data object can be represented as vectors (in 1D), matrices (in 2D), or tensors (in 3D or higher dimensions) and can incorporate multiple channels (thus providing high flexibility in the input data representation). CNNs highlight features from the grid data by performing convolution operations with different types of operators. The operators highlight different types of features (e.g., patterns, gradients, geometrical features) and are learned by using optimization techniques. In other words, CNNs seek to identify optimal operators that best map the input data to the output data. A common misconception is that CNNs are only capable of processing image or video data but their application scope is much wider; specifically, datasets encountered in diverse applications can be expressed as grid data. Here, we show how to apply CNNs to new types of applications such as optimal control, flow cytometry, multivariate process monitoring, and molecular simulations.

Above‐bandgap femtosecond optical excitation of a ferroelectric/dielectric BaTiO₃/CaTiO₃superlattice leads to structural responses that are a consequence of the screening of the strong electrostatic coupling between the component layers. Time‐resolved X‐ray free‐electron laser diffraction shows that the structural response to optical excitation includes a net lattice expansion of the superlattice consistent with depolarization‐field screening driven by the photoexcited charge carriers. The depolarization‐field‐screening‐driven expansion is separate from a photoacoustic pulse launched from the bottom electrode on which the superlattice is epitaxially grown. The distribution of diffracted intensity of superlattice X‐ray reflections indicates that the depolarization‐field‐screening‐induced strain includes a photoinduced expansion in the ferroelectric BaTiO₃and a contraction in CaTiO₃. The magnitude of expansion in BaTiO₃layers is larger than the contraction in CaTiO₃. The difference in the magnitude of depolarization‐field‐screening‐driven strain in the BaTiO₃and CaTiO₃components can arise from the contribution of the oxygen octahedral rotation patterns at the BaTiO₃/CaTiO₃interfaces to the polarization of CaTiO₃. The depolarization‐field‐screening‐driven polarization reduction in the CaTiO₃layers points to a new direction for the manipulation of polarization in the component layers of a strongly coupled ferroelectric/dielectric superlattice.

Directed self-assembly of block copolymers (BCPs) enables nanofabrication at sub-10 nm dimensions, beyond the resolution of conventional lithography. However, directing the position, orientation, and long-range lateral order of BCP domains to produce technologically-useful patterns is a challenge. Here, we present a promising approach to direct assembly using spatial boundaries between planar, low-resolution regions on a surface with different composition. Pairs of boundaries are formed at the edges of isolated stripes on a background substrate. Vertical lamellae nucleate at and are pinned by chemical contrast at each stripe/substrate boundary, align parallel to boundaries, selectively propagate from boundaries into stripe interiors (whereas horizontal lamellae form on the background), and register to wide stripes to multiply the feature density. Ordered BCP line arrays with half-pitch of 6.4 nm are demonstrated on stripes >80 nm wide. Boundary-directed epitaxy provides an attractive path towards assembling, creating, and lithographically defining materials on sub-10 nm scales.

Glasses are generally assumed to be isotropic and there are no literature reports of elastic anisotropy for molecular glasses. However, as glasses formed by physical vapor deposition can be structurally anisotropic, it is of interest to investigate the elastic anisotropy in these materials. Micro‐Brillouin light spectroscopy is used in several experimental geometries to determine the elastic stiffness tensors of three glass films of itraconazole vapor‐deposited at substrate temperatures (T_sub) of 330, 315, and 290 K, respectively. Significant elastic anisotropy is observed and, in these glasses, the elastic anisotropy shows a strong correlation with the molecular orientation. The out‐of‐plane and in‐plane Young's moduli of the highT_sub(330 K) sample, which features a predominantly vertical molecular orientation, exhibit a high anisotropy ratio of 2.2. The observed elastic anisotropy is much larger than those previously observed in liquid crystals and even many crystalline solids.

The emergence of memristive behavior in amorphous–crystalline 2D oxide heterostructures, which are synthesized by atomic layer deposition (ALD) of a few‐nanometer amorphous Al₂O₃layers onto atomically thin single‐crystalline ZnO nanosheets, is demonstrated. The conduction mechanism is identified based on classic oxygen vacancy conductive channels. ZnO nanosheets provide a 2D host for oxygen vacancies, while the amorphous Al₂O₃facilitates the generation and stabilization of the oxygen vacancies. The conduction mechanism in the high‐resistance state follows Poole–Frenkel emission, and in the the low‐resistance state is fitted by the Mott–Gurney law. From the slope of the fitting curve, the mobility in the low‐resistance state is estimated to be ≈2400 cm²V⁻¹s⁻¹, which is the highest value reported in semiconductor oxides. When annealed at high temperature to eliminate oxygen vacancies, Al is doped into the ZnO nanosheet, and the memristive behavior disappears, further confirming the oxygen vacancies as being responsible for the memristive behavior. The 2D heterointerface offers opportunities for new design of high‐performance memristor devices.

The manifestation of acute kidney injury (AKI) is associated with poor patient outcomes, with treatment options limited to hydration or renal replacement therapies. The onset of AKI is often associated with a surfeit of reactive oxygen species. Here, it is shown that selenium‐doped carbon quantum dots (SeCQDs) have broad‐spectrum antioxidant properties and prominent renal accumulation in both healthy and AKI mice. Due to these properties, SeCQDs treat or prevent two clinically relevant cases of AKI induced in murine models by either rhabdomyolysis or cisplatin using only 1 or 50 µg per mouse, respectively. The attenuation of AKI in both models is confirmed by blood serum measurements, kidney tissue staining, and relevant biomarkers. The therapeutic efficacy of SeCQDs exceeds amifostine, a drug approved by the Food and Drug Administration that also acts by scavenging free radicals. The findings indicate that SeCQDs show great potential as a treatment option for AKI and possibly other ROS‐related diseases.

Two critical limitations of organic–inorganic lead halide perovskite materials for solar cells are their poor stability in humid environments and inclusion of toxic lead. In this study, high‐throughput density functional theory (DFT) methods are used to computationally model and screen 1845 halide perovskites in search of new materials without these limitations that are promising for solar cell applications. This study focuses on finding materials that are comprised of nontoxic elements, stable in a humid operating environment, and have an optimal bandgap for one of single junction, tandem Si‐perovskite, or quantum dot–based solar cells. Single junction materials are also screened on predicted single junction photovoltaic (PV) efficiencies exceeding 22.7%, which is the current highest reported PV efficiency for halide perovskites. Generally, these methods qualitatively reproduce the properties of known promising nontoxic halide perovskites that are either experimentally evaluated or predicted from theory. From a set of 1845 materials, 15 materials pass all screening criteria for single junction cell applications, 13 of which are not previously investigated, such as (CH₃NH₃)_0.75Cs_0.25SnI₃, ((NH₂)₂CH)Ag_0.5Sb_0.5Br₃, CsMn_0.875Fe_0.125I₃, ((CH₃)₂NH₂)Ag_0.5Bi_0.5I₃, and ((NH₂)₂CH)_0.5Rb_0.5SnI₃. These materials, together with others predicted in this study, may be promising candidate materials for stable, highly efficient, and nontoxic perovskite‐based solar cells.