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Zero and low field nuclear magnetic resonance measurements have been performed on MAX phase samples (Cr1-x Mn x )2AC with A = Ge and Ga in order to obtain local microscopic information on the nature of magnetism in this system. Our results unambiguously provide evidence for the existence of long-range magnetic order in (Cr0.96Mn0.04)2GeC and for (Cr0.93Mn0.07)2GaC, but not for (Cr0.97Mn0.03)2GaC. We point to a possible dependence of long range magnetic order in these MAX phase compounds on the A atom.Graphene oxide (GO), a functional derivative of graphene, is a promising nanomaterial for a variety of optoelectronic applications as it exhibits fluorescence and maintains many of graphene's beneficial physical properties. although other graphene derivatives are chemically plausible and may serve to the benefit of the aforementioned applications, GO remains the one heavily used. the nature of optical behavior of other graphene derivatives has yet to be fully understood and studied. in this work we develop a variety of graphene derivatives and characterize their optical properties concomitantly suggesting a unified model for optical emission in graphene derivatives. in this process we examine the influence of different functional groups on the surface of graphene on its optoelectronic properties. mildly oxidized graphene (oxo-g1), nitrated graphene, arylated graphene, brominated graphene, and fluorinated graphene are obtained and characterized via TEM and EDX, FTIR and fluorescence spectroscopies with the latter indicating a potential band gap-derived fluorescence from each of the materials. this suggests that optical properties of graphene derivatives have minimal functional group dependence and are manifested by the localized environments within the flakes. this is confirmed by the hyperchem theoretical modeling of all aforementioned graphene derivatives indicating a similar electronic configuration for all, assessed by the pm3 semi-empirical approach. this work can further serve to describe and predict optical properties of similar graphene-based structures and promote graphene derivatives other than GO for utilization in research and industry.
Neonatal electroencephalography (EEG) source localization is highly prone to errors due to head modeling deficiencies. In this study, we investigated the effect of head model complexities on the accuracy of EEG source localization in full term neonates using a realistic volume conductor head model.

We performed numerical simulations to investigate source localization errors caused by cerebrospinal fluid (CSF) and fontanel exclusion and gray matter (GM)/white matter (WM) distinction using the finite element method.

Our results showed that the exclusion of CSF from the head model could cause significant localization errors mostly for sources closer to the inner surface of the skull. With a less pronounced effect compared to the CSF exclusion, the discrimination between GM and WM also widely affected all sources, especially those located in deeper structures. The exclusion of the fontanels from the head model led to source localization errors for sources located in areas beneath the fontanels. Our finding clearly shows that the CSF inclusion and GM/WM distinction in EEG inverse modeling can substantially reduce EEG source localization errors. Moreover, fontanels should be included in neonatal head models, particularly in source localization applications, in which sources of interest are located beneath or in vicinity of fontanels.

Our findings have practical implications for a better understanding of the impact of head model complexities on the accuracy of EEG source localization in neonates.
Our findings have practical implications for a better understanding of the impact of head model complexities on the accuracy of EEG source localization in neonates.The nanostructures produced by oblique-incidence broad beam ion bombardment of a solid surface are usually modelled by the anisotropic Kuramoto-Sivashinsky equation. This equation has five parameters, each of which depend on the target material and the ion species, energy, and angle of incidence. We have developed a deep learning model that uses a single image of the surface to estimate all five parameters in the equation of motion with root-mean-square errors that are under 3% of the parameter ranges used for training. This provides a tool that will allow experimentalists to quickly ascertain the parameters for a given sputtering experiment. It could also provide an independent check on other methods of estimating parameters such as atomistic simulations combined with the crater function formalism.Peptide assembly is an increasingly important field of study due to the versatility, tunability and vast design space of amino acid based biomolecular assemblies. Peptides can be precisely engineered to possess various useful properties such as the ability to form supramolecular assemblies, desired response to pH, or thermal stability. These peptide supramolecular assemblies have diverse morphologies including vesicles, nanotubes, nanorods and ribbons. Of specific interest is the domain of engineering peptides that aggregate into spherical nanostructures due to their encapsulation properties the ability to hold, transport and release chemical payloads in a controllable manner. This is invaluable to the fields of nanomedicine and targeted drug delivery. In this review, the state of the art in the domain of peptide-based vesicles and nanospheres is summarized. compound library inhibitor Specifically, an overview of the assembly of peptides into nanovesicles and nanospheres is provided. Both aromatic as well as aliphatic side chain amino acids are discussed. The domain of aromatic side chained amino acid residues is largely dominated by Phenylalanine based peptides and variants thereof. Tyrosine also demonstrates similar aggregation properties. Both experimentally and computationally driven approaches are discussed. The domain of aliphatic amino acid residues based vesicles and droplets is broader, and details multiple amino acid residues such as Alanine, Valine, Lysine, Glycine, Proline, and Aspartic Acid. Finally, a discussion on potential future directions is provided.
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