Synthesis and luminescence properties of β-NaRE0.95Eu0.05F4 (RE¼Y, Lu)

Illariia A. Razumkova,Alexander E. Sedykh,Yuriy G. Denisenko,Klaus Müller-Buschbaum 한국공업화학회 2020 Journal of Industrial and Engineering Chemistry Vol.92 No.-

On the base of β-NaYF4 and β-NaLuF4 compounds, optimal synthesis methods were selected to obtainhighly crystalline Eu3+-doped compounds in the hexagonal (β) structure. The compounds β-NaY0.95Eu0.05F4 and β-NaLu0.95Eu0.05F4 were synthesized by hydrothermal synthesis. The concentrationof Na2EDTA introduced into the reaction system as a modifier affects the shape and size of the productfrom elongated hexagonal prisms to quartz-like crystals. The thermal behavior of the compounds hasbeen studied; the temperature of the polymorphic transition from hexagonal to cubic structure is 680 Cfor β-NaY0.95Eu0.05F4 and 624 C for β-NaY0.95Eu0.05F4, the melting point of α-NaRE0.95Eu0.05F4 is about900 C. Under excitation with UV-light of l = 393 nm, β-NaY0.95Eu0.05F4 and β-NaLu0.95Eu0.05F4 show theemission lines of direct Eu3+ f-f transitions, with the presence of the emission from higher Eu3+ excitedstates (up to 5H3) in both samples, which is responsible for the more orange color of emission instead ofthe usual red for Eu3+. Emission decay times are slightly longer for β-NaY0.95Eu0.05F4 than for β-NaLu0.95Eu0.05F4, which is the result of a bigger unit cell for the former, leading to longer interatomicdistances between Eu3+ ions in the lattice. The luminescence mechanisms for the doped lanthanide ionswere thoroughly analyzed.

RANGE EXTENDER MODULE TRANSMISSION TOPOLOGY STUDY

Konrad Herold,Marius Böhmer,Rene Savelsberg,Alexander Müller,Jan Schröter,Jan Karthaus,Un-Jae Seo,Georg Jacbos,Kay Hameyer,Jakob Andert 한국자동차공학회 2018 International journal of automotive technology Vol.19 No.5

Range extender modules are one option to compensate for short drive ranges of electric vehicles. The close interaction of combustion engine and generator poses new challenges in development. A key requirement for range extender systems is to be light and virtually imperceptible in operation. High-speed electrical machines aim at increasing power density. However, their introduction in a range extender requires a gearbox. The combustion engine torque fluctuations can lead to rattle in the gearbox. The rattle can be overcome by a dual mass flywheel. An interdisciplinary model is developed and used to analyse three different range extender systems: one with a low speed generator without gearbox, one with a high-speed generator, and one with a high-speed generator and a dual mass flywheel. The efficiency was found to be higher for the system with a low speed generator, whereas the power density and the costs are beneficial for the high-speed concept. A dual mass flywheel eliminates the changes of torque direction in the gearbox. It reduces the speed fluctuations of the gearbox and generator by over 90 % compared to the low speed setup. But it increases rolling moment and subsequently chassis excitation compared to a setup with only a gearbox.

Treatment of 4T1 metastatic breast cancer with combined hypofractionated irradiation and autologous T-cell infusion.

Filatenkov, Alexander,Baker, Jeanette,M?ller, Antonia M,Ahn, G-One,Kohrt, Holbrook,Dutt, Suparna,Jensen, Kent,Dejbakhsh-Jones, Sussan,Negrin, Robert S,Shizuru, Judith A,Engleman, Edgar G,Strober, Samu Academic Press 2014 Radiation research Vol.182 No.2

<P>The goal of this study was to determine whether a combination of local tumor irradiation and autologous T-cell transplantation can effectively treat metastatic 4T1 breast cancer in mice. BALB/c mice were injected subcutaneously with luciferase-labeled 4T1 breast tumor cells and allowed to grow for 21 days, at which time metastases appeared in the lungs. Primary tumors were treated at that time with 3 daily fractions of 20 Gy of radiation each. Although this approach could eradicate primary tumors, tumors in the lungs grew progressively. We attempted to improve efficacy of the radiation by adding autologous T-cell infusions. Accordingly, T cells were purified from the spleens of tumor-bearing mice after completion of irradiation and cryopreserved. Cyclophosphamide was administered thereafter to induce lymphodepletion, followed by T-cell infusion. Although the addition of cyclophosphamide to irradiation did not improve survival or reduce tumor progression, the combination of radiation, cyclophosphamide and autologous T-cell infusion induced durable remissions and markedly improved survival. We conclude that the combination of radiation and autologous T-cell infusion is an effective treatment for metastatic 4T1 breast cancer.</P>

Engineering a short, aldolase-based pathway for (<i>R</i>)-1,3-butanediol production in <i>Escherichia coli</i>

Nemr, Kayla,Mü,ller, Jonas E.N.,Joo, Jeong Chan,Gawand, Pratish,Choudhary, Ruhi,Mendonca, Burton,Lu, Shuyi,Yu, Xiuyan,Yakunin, Alexander F.,Mahadevan, Radhakrishnan Elsevier 2018 Metabolic engineering Vol.48 No.-

<P><B>Abstract</B></P> <P>Microbial processes can produce a wide range of compounds; however, producing complex and long chain hydrocarbons remains a challenge. Aldol condensation offers a direct route to synthesize these challenging chemistries and can be catalyzed by microbes using aldolases. Deoxyribose-5-phosphate aldolase (DERA) condenses aldehydes and/or ketones to β -hydroxyaldehydes, which can be further converted to value-added chemicals such as a precursor to cholesterol-lowering drugs. Here, we implement a short, aldolase-based pathway in <I>Escherichia coli</I> to produce (<I>R</I>)-1,3-BDO from glucose, an essential component of pharmaceutical products and cosmetics. First, we expressed a three step heterologous pathway from pyruvate to produce 0.3 g/L of (<I>R</I>)-1,3-BDO with a yield of 11.2 mg/g of glucose in wild-type <I>E. coli</I> K12 MG1655. We used a systems metabolic engineering approach to improve (<I>R</I>)-1,3-BDO titer and yield by: 1) identifying and reducing major by-products: ethanol, acetoin, and 2,3-butanediol; 2) increasing pathway flux through DERA to reduce accumulation of toxic acetaldehyde. We then implemented a two-stage fermentation process to improve (<I>R</I>)-1,3-BDO titer by 8-fold to 2.4 g/L and yield by 5-fold to 56 mg/g of glucose ( 11 % of maximum theoretical yield) in strain BD24, by controlling pH to 7 and higher dissolved oxygen level. Furthermore, this study highlights the potential of the aldolase chemistry to synthesize diverse products directly from renewable resources in microbes.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Platform for non-natural chemicals developed using aldol condensation. </LI> <LI> Modular pathway design demonstrated in E. coli for (R)-1,3-BDO production. </LI> <LI> Carbon flux optimized by blocking pyruvate and acetaldehyde-consuming pathways. </LI> <LI> Final (R)-1,3-BDO production: 2.4 g/L and 11% of maximum theoretical yield. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Explaining nonlinear classification decisions with deep Taylor decomposition

Montavon, Gré,goire,Lapuschkin, Sebastian,Binder, Alexander,Samek, Wojciech,Mü,ller, Klaus-Robert Elsevier 2017 Pattern recognition Vol.65 No.-

<P><B>Abstract</B></P> <P>Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems such as image recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes due to their multilayer nonlinear structure. In this paper we introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures. Our method called deep Taylor decomposition efficiently utilizes the structure of the network by backpropagating the explanations from the output to the input layer. We evaluate the proposed method empirically on the MNIST and ILSVRC data sets.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A novel method to explain nonlinear classification decisions in terms of input variables is introduced. </LI> <LI> The method is based on Taylor expansions and decomposes the output of a deep neural network in terms of input variables. </LI> <LI> The resulting deep Taylor decomposition can be applied directly to existing neural networks without retraining. </LI> <LI> The method is tested on two large-scale neural networks for image classification: BVLC CaffeNet and GoogleNet. </LI> </UL> </P>