A deep learning approach for complex microstructure inference.

Automated, reliable, and objective microstructure inference from micrographs is essential for a comprehensive understanding of process-microstructure-property relations and tailored materials development. However, such inference, with the increasing complexity of microstructures, requires advanced segmentation methodologies. While deep learning offers new opportunities, an intuition about the required data quality/quantity and a methodological guideline for microstructure quantification is still missing.

Effects of Y- and La-doping on the magnetic ordering, Kondo effect, and spin dynamics in CeRuAl.

The influence of Y- and La-substitution for Ce on the competing Kondo effect and magnetic ordering, as well as on spin dynamics in the Kondo semiconductor CeRuAlhave been investigated by means of thermal, electronic, and magnetic properties. The parent compound CeRuAlis known to be a controversial antiferromagnet with high magnetic ordering temperature= 27 K. A small negative chemical pressure caused by La-doping results rapid suppression ofand spin gap energy Δ, compared to a small positive pressure caused by Y-doping.

Particle encapsulation techniques for atom probe tomography of precipitates in microalloyed steels.

Atom probe tomography (APT) provides sub-nm resolution in the analysis of complex industrial steels. It can resolve the carbonitride precipitates in Nb-Ti microalloyed high-strength low-alloy (HSLA) steels that strongly affect material performance and illuminate the complex precipitation sequence before and during the thermo-mechanical controlled process (TMCP). However, the precipitate concentration is low in HSLA steels during austenite conditioning, especially at temperatures > 850 °C, so that the probability of detecting precipitates via APT is below 5%.

Numerical Convergence Analysis of the Frank-Kamenetskii Equation.

This work investigates the convergence dynamics of a numerical scheme employed for the approximation and solution of the Frank-Kamenetskii partial differential equation. A framework for computing the critical Frank-Kamenetskii parameter to arbitrary accuracy is presented and used in the subsequent numerical simulations. The numerical method employed is a Crank-Nicolson type implicit scheme coupled with a fourth order spatial discretisation as well as a Newton-Raphson update step which allows for the nonlinear source term to be treated implicitly.

Comment on "Atmospheric chemistry of iodine anions: elementary reactions of I, IO and IO with ozone studied in the gas-phase at 300 K using an ion trap" Teiwes et al., Phys. Chem. Chem. Phys., 2018, 20, 20608.

This Comment describes an analytical solution for the set of differential equations solved numerically in the original article by Teiwes et al.