Fraunhofer Chalmers Research Centre for Industrial Mathematics
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No full textMicrostructure evolution and creep resistance of a Z-phase strengthened 12% Cr steel
No full text9–12% Cr steels are a family of steels, which are the backbones for today’s fossil fuel steam power plants, which provide more than 60% of electricity worldwide. By developing more creep and corrosion resistant 9–12% Cr steels, the operation temperature of 620°C could be increased to 650°C, which would reduce the environmental impact. For improved resistance to corrosion, a higher Cr content is needed. However, an increased Cr content results in formation of stable but big detrimental Z-phase precipitates. While Z-phase traditionally results in premature creep failures, a new alloy design strategy, called Z-phase strengthening, aims to make use of Z-phase particles as strengthening precipitates rather than detrimental particles.
Getting a better understanding of the correlation between microstructure evolution and creep resistance in these steels, could potentially lead to a solution to the conflict between creep and corrosion resistance at 650°C. In this study, two versions of the same 12% Cr steel have been evaluated, one version is tempered at 700°C and the other at 740°C. Investigation have been done with light optical microscopy (LOM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and energy dispersive X-ray spectroscopy (EDX). Results from these methods were correlated with results from mechanical testing and creep testing, and together they provided a better understanding of the evolution of nano-sized particles at high temperatures.
It was found that tempering with a lower temperature results in better creep properties. Mechanical properties are also improved by a lower tempering temperature, but eventually the values of hardness, tensile testing, and impact toughness for the two different versions coverage with ageing. M23C6 particles grow very slowly. Laves-phase is significantly affected by tempering temperature. A higher tempering temperature results in Z-phase particles that start to transform from MX particles after a shorter ageing time. The average particle size is smaller with a lower tempering temperature. A lower tempering temperature results in superior microstructure regarding nano-sized particles
New products from seafood side streams to stimulate a circular economy
No full textThe Paris Agreement implicates that society will take necessary action to prevent the global mean temperature to rise more than 1.5°C. Since 80% of greenhouse gas emissions from agriculture originate from livestock, a protein shift is needed. This project has investigated the possibilities to implement proteins recovered from solid and liquid seafood processing side streams in different types of food products including fish ball, fish sausage, fish soup and mayonnaise. The proteins were recovered from fish processing by-products using the pH-shift method and shrimp processing water using flocculation. Effect of using the alternative proteins on proximate composition (protein, moisture, fat and mineral content), color, texture and sensorial properties of the products were evaluated. The fish soups were found as the least promising products, due to the low protein content and low scores in the sensory analysis. The results showed that 50% substitution of salmon protein isolate in fish balls and fish sausages with fish mince had no negative impact on the products quality but problems with a complete replacement may arise, especially regarding texture and color of the products. The mayonnaises containing both fish and shrimp proteins showed promising properties compared to the samples with egg protein. It was concluded that more studies of products with isolate are needed. Taste tests, different ratios, of mince and isolate, and repeats of already performed tests and products would provide useful future knowledge
Synthesis of dihydropyranones from chromene aldehydes via an oxidative NHC-catalyzed kinetic resolution
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No full textProviding solutions for designing an optimal planning scheme is extremely important
in transportation industry. A potential solution requires the development of
models that are able to accurately predict the remaining travel time of trucks operating
in a transport mission. Unfortunately this is not a trivial task as a transport
mission is influenced by a variety of stochastic and impossible to predict factors.
This study develops a variety of machine learning approaches and benchmark their
ability to predict arrival times. In particular Support Vector Regression, Artificial
Neural Networks, Gradient Boosting, Random Forest and Stacked Generalization
models were developed for the aforesaid task. The proposed models are trained
and evaluated using GPS and weather data for a transport mission between Malmö
and Göteborg. The main objectives of the study are finding the variables that influence
the total travel time of a vehicle and are optimal to be used as inputs to
the prediction models, comparing the performance of different machine learning approaches
and identifying the optimal approach among the proposed models. Study
results verified that machine learning approaches have the ability to predict the arrival
times of trucks. Even though all methods outperformed a historic data based
model, results showed that the Random Forest and Stacked Generalization methods
outperformed the other machine learning models in terms of Root Mean Square
Error and Mean Absolute Percentage Error. In addition it was found that utilizing
appropriate features as inputs to the prediction models dramatically increased the
performance of the algorithms
Epigenetic modulation with the CRISPR/dCas9 system Induction of DNA methylation with the effector domain dnmt3a3L fused to the dCas9 protein
No full textEpigenetics play a crucial role in development, cell differentiation and molecular cell stability, and aberrations in the epigenetic pattern can result in diseases such as cancer. Thus, epigenetic mechanisms are important to study, and methods for modulating the epigenetic pattern are central for this area of research. One epigenetic modification is DNA methylation, which is regulated by the enzyme family of DNA methyltransferases. This thesis presents the first part of method development project using the CRISPR/dCas9 system to induce methylation in the MGMT promoter in primary patient-derived cancer stem cells. We first evaluated a two-vector based system with the sgRNA scaffold and the dCas9 protein on separate plasmids. Transfected cells showed low viability and the plasmid needed to be purified thoroughly in order to achieve high cell viability. However, the PKG promoter, which was first used in the CRISPR/dCas9 system, showed inefficient expression in the investigated cells. The CMV promoter on the other hand showed efficient expression. For further studies, a new one-based vector system was designed where dCas9 and the selective marker are expressed under the CMV promoter
Development of a growth-sensitive malonyl-CoA platform strain
No full textThe transition from a fossil reliance society towards full reliance on sustainable alternatives is a difficult challenge. The development of high-performant microbial cell factories, which efficiency convert renewable carbon sources to generate industrially relevant compounds, are to date not economically compatible with the current less sustainable alternatives. Through metabolic reprogramming, the production of compounds at industrial levels could be obtained by increasing cofactors and substrates levels. In this thesis, we developed a growth-sensitive malonyl-CoA platform strain, able to select for an enrichment of the highly relevant precursor, malonyl-CoA. Through coupling it with a CRISPR-dCas9 gRNA library we performed a biosensor assisted laboratory evolution (BALE). Three targets, previously described to significantly increase the downstream product of malonylCoA, 3-Hydroxypropionic acid, were selected after 24 hours and more targets will be characterized