Designing efficient and stable water
splitting photocatalysts is
an intriguing challenge for energy conversion systems. We report on
the optimal fabrication of perfectly aligned nanotubes on trimetallic
Ti–Mo–Fe alloy with different compositions prepared
via the combination of metallurgical control and facile electrochemical
anodization in organic media. The X-ray diffraction (XRD) patterns
revealed the presence of composite oxides of anatase TiO2 and magnetite Fe3O4 with better stability
and crystallinity. With the optimal alloy composition Ti–(5.0
atom %) Mo–(5.0 atom %) Fe anodized for 16 h, enhanced conductivity,
improved photocatalytic performance, and remarkable stability were
achieved in comparison with Ti–(3.0 atom %) Mo–(1.0
atom %) Fe samples. Such optimized nanotube films attained an enhanced
photocatalytic activity of ∼0.272 mA/cm2 at 0.9
VSCE, which is approximately 4 times compared to the bare
TiO2 nanotubes fabricated under the same conditions (∼0.041
mA/cm2 at 0.9 VSCE). That was mainly correlated
with the emergence of Mo and Fe impurities within the lattice, providing
excess charge carriers. Meanwhile, the nanotubes showed outstanding
stability with a longer electron lifetime. Moreover, carrier density
variations, lower charge transfer resistance, and charge carriers
dynamics features were demonstrated via the Mott–Schottky and
electrochemical impedance analyses.
Valproic acid (VPA) is anti-epileptic and mood stabilizer drug that induces autism spectrum disease (ASD). However, VPA has several side effects; hepatic steatosis, hepatotoxicity, hemorrhagic pancreatitis, encephalopathy, bone marrow suppression and metabolic disorders such as obesity. VPA proved to be unavoidable and could not be excluded in epileptic pregnant women. Non-controlled epileptic attacks during pregnancy produce high risk of injury to both mother and fetus. However, VPA crosses the placenta and accumulate in the fetal circulation with higher concentration than that in the maternal blood, causing toxicity and teratogenicity. Gestational VPA treatment for a life-threatening epilepsy caused numerous defects in children, including neural tube defects, intellectual impairments and cognitive-behavioral impairments. 8.9% of children exposed to VPA in utero develop autistic features. VPA exposure in the first trimester of gestation represented the highest risk for the child to develop autism, showed classical signs of autism, and developmental and behavioral delays. The full mechanisms of VPA are not fully elicited. This review discusses canonical Wnt/β-Catenin pathways as possible mechanism involved in autism induction upon VPA use.
Polymer matrix composites exhibit nonlinear viscoelastic behavior over a wide range of temperatures and loading frequencies, which requires an elaborate experimental characterization campaign. Methods are now available to accelerate the characterization process and recover elastic modulus from storage modulus ( E′). However, these methods are limited to the linear viscoelastic region and need to be expanded to nonlinear viscoelastic problems to enable materials design. The present work aims to build a general machine learning based architecture to accelerate the characterization and materials design process for nonlinear viscoelastic materials using the E′ results. To expand outside the linear viscoelastic region, general relations of viscoelasticity are first developed so the master relation of E′ considering nonlinear viscoelasticity can be transformed to time domain relaxation function. The transform starts with building the master relation by optimizing the artificial neural network (ANN) formulation using Kriging model and genetic algorithm. Then the master relation is transformed to a relaxation function, which can be used to predict the stress response with a given strain history and to further extract the elastic modulus. The transform is tested on high density polyethylene matrix syntactic foams and the accuracy is found by comparing the predicted materials properties with those obtained from tensile tests. The good agreements indicate the transform can predict the elastic modulus under a wide range of temperatures and strain rates for any composition of the composite and can be used for material design problems.
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