Combined experimental and numerical study on the effective grain growth dynamics in highly anisotropic systems: Application to barium titanate

Abnormal grain growth is a commonly observed phenomenon in barium titanate. It is usually associated with grain boundaries of different mobility and energy present in the microstructure. The influence of interfaces with variable mobility and energy on grain growth is investigated by a combined experimental and numerical approach in a transition region where growth behaviour strongly deviates from Arrhenius behaviour. Abnormal growth occurs between 1275 and 1325 °C, with normal grain growth occurring above and below this temperature range. The overall grain growth rate of the small matrix grains in the transition region is found to increase nonlinearly with inverse temperature between the high- and low-temperature states. A similar behaviour is found in simulations using a 3-D mesoscale grain growth model under the assumption of fractions of grain boundaries being in the high- or low-temperature state. The transition at the grain boundary is in agreement with the complexion model. Additionally, the simulation is used to map the nucleation probability for abnormal grains in the transition region as a function of combined energy and mobility advantages. The energy advantage of the grain boundaries is found to be of greater importance for the nucleation of abnormal grains compared to results from mean field models.     

Optimized state estimation by application of machine learning

The requirements concerning the technical availability as part of the overall equipment effectiveness increase constantly in production nowadays. Unplanned downtimes have to be prevented via efficient methods. Predictive, condition-based maintenance represents a valuable approach for fulfilling these demands, but precise models for state estimation are missing. From the manufacturers’ point of view the challenge consists in wear models with the capability of specifying the correct component’s state as well as providing reliable failure forecasts. Unfortunately, nowadays creation of wear models is based on specific stress tests or design of experiments from the manufacturer. The integration of the production phase or even data feedback and user knowledge does not take place. New potential is promised by cross-cutting technologies from ICT like cloud technologies—in general virtual platform concepts—or approaches of machine learning as enabling technologies. The objective of this paper is to adopt existing algorithms to the new application of condition monitoring in order to evaluate the applicability for automated training of robust wear models. In that context the most commonly used algorithms are described and the reader gets an impression what challenges have to be met when dealing with machine learning. A selection of about ten algorithms with 45 variants is evaluated for four different features within a packaging machine. In the outlook the embedding of the trained model in a cloud architecture is presented.     

Generic Representation of Solid-Object Geometry for Genetic Search

Abstract: This paper examines the first stage of using genetic algorithms in design—how to encode designs as chromosomes. Generic representations capable of describing the geometry of any solid object to eliminate the unnecessary duplication of work at this stage are explored. A suitable low-parameter, spatial-partitioning representation is then proposed, using partitions consisting of six-sided polyhedra intersected by planes. Finally, the coding of the representation is examined, with the conclusion that allelic coding with control genes and user-definable, fixed-value alleles would provide the most flexible system.     

Hydrogenation of 3,4-epoxy-1-butene over Cu–Pd/SiO2 catalysts prepared by electroless deposition

Electroless deposition has been used to prepare Cu–Pd/SiO₂ bimetallic catalysts wherein initial Cu coverages are limited only to the pre-existing Pd surface. Cu loading on the Pd surface can be systematically varied by modification of deposition kinetic parameters. In this case deposition time was used as the kinetic variable for the preparation of a series of Cu–Pd catalysts. These materials have been characterized using atomic absorption, CO chemisorption, and FT-IR (adsorption of CO), and then evaluated for the hydrogenation of 3,4-epoxy-1-butene, a functionalized olefin having many potential reaction pathways. Catalyst performance and characterization results suggest that Cu is not distributed in a monodisperse manner on the Pd surface, indicating the existence of autocatalytic deposition of Cu on Cu sites. The FT-IR results suggest that although CO adsorption on all sites is suppressed by Cu addition, initial Cu deposition occurs more readily on certain sites. The bimetallic Cu–Pd sites that are formed exhibit unusually high activity for EpB conversion and formation of unsaturated alcohols and aldehydes. This bimetallic effect on catalyst activity and selectivity is best explained, not by the existence of either ligand or ensemble effects, but rather by the bifunctional nature of the Cu–Pd sites present on the surface of these catalysts.     

Modification with Organometallic Compounds Improves Crossing of the Blood–Brain Barrier of [Leu5]‐Enkephalin Derivatives in an In Vitro Model System

Enkephalin peptides are thought to be suitable vectors for the passage of the blood–brain barrier (BBB). Modifications that do not alter the amino acid sequence are often used to improve the permeation through living membrane systems. As a new type of modification we introduce organometallic compounds, in particular ferrocene carboxylic acid. Derivatives of [Leu⁵]enkephalin were synthesised and labelled with organometallic compounds by using solid‐phase synthesis techniques. All new metal–peptide bioconjugates were comprehensively characterised by HPLC, NMR spectroscopy and mass spectrometry and found to be at least 95 % pure. For the first time, permeation coefficients in a BBB model for organometal–peptide derivatives were determined in this work. The uptake and localisation of fluorescein‐labelled enkephalins was monitored by fluorescence microscopy on three cancer cell lines. Octanol/H₂O partition coefficients of the compounds were measured by HPLC. The introduction of the organometallic moiety enhances the uptake into cells and the permeation coefficient of [Leu⁵]‐enkephalin. This could be due to an increase in lipophilicity caused by the organometallic label. The metal–peptide conjugates were found to be nontoxic up to mM concentrations. The low cytotoxicity encourages further experiments that could take advantage of the selectivity of enkephalin derivatives for opioid receptors.     

Protein design to understand peptide ligand recognition by tetratricopeptide repeat proteins

Protein design aims to understand the fundamentals of protein structure by creating novel proteins with pre-specified folds. An equally important goal is to understand protein function by creating novel proteins with pre-specified activities. Here we describe the design and characterization of a tetratricopeptide (TPR) protein, which binds to the C-terminal peptide of the eukaryotic chaperone Hsp90. The design emphasizes the importance of both direct, short-range protein-peptide interactions and of long-range electrostatic optimization. We demonstrate that the designed protein binds specifically to the desired peptide and discriminates between it and the similar C-terminal peptide of Hsp70.     

Sorbent injection into a slipstream baghouse for mercury control: Project summary

A project led by the Energy and Environmental Research Center to test and demonstrate sorbent injection as a cost-effective mercury control technology for utilities burning lignites has shown effective mercury capture under a range of operating conditions. Screening, parametric, and long-term tests were carried out at a slipstream facility representing an electrostatic precipitator–activated carbon injection–fabric filter configuration (called a TOXECON™ in the United States). Screening tests of sorbent injection evaluated nine different sorbents, including both treated and standard activated carbon, to compare mercury capture as a function of sorbent injection rate. Parametric tests evaluated several variables including air-to-cloth (A/C) ratio, flue gas temperature, cleaning frequency, and dust loading to determine the effect on mercury control and systems operation. Long-term tests (approximately 2 months in duration) evaluated the sustainability of systems operation.