Quantum spin liquids in frustrated spin-1 diamond antiferromagnets

Motivated by the recent synthesis of the spin-1 A-site spinel NiRh$_{\text 2}$O$_{\text 4}$, we investigate the classical to quantum crossover of a frustrated $J_1$-$J_2$ Heisenberg model on the diamond lattice upon varying the spin length $S$. Applying a recently developed pseudospin functional renormalization group (pf-FRG) approach for arbitrary spin-$S$ magnets, we find that systems with $S \geq 3/2$ reside in the classical regime where the low-temperature physics is dominated by the formation of coplanar spirals and a thermal (order-by-disorder) transition. For smaller local moments $S$=1 or $S$=1/2 we find that the system evades a thermal ordering transition and forms a quantum spiral spin liquid where the fluctuations are restricted to characteristic momentum-space surfaces. For the tetragonal phase of NiRh$_{\text 2}$O$_{\text 4}$, a modified $J_1$-$J_2^-$-$J_2^\perp$ exchange model is found to favor a conventionally ordered N\'eel state (for arbitrary spin $S$) even in the presence of a strong local single-ion spin anisotropy and it requires additional sources of frustration to explain the experimentally observed absence of a thermal ordering transition.Comment: 11 pages, 14 figure

Modification of polyampholytic poly(dehydroalanine): strategies and utilization in hybrid nanomaterials applied as smart dispersants, sensors and in photocatalysis

This thesis describes strategies towards the preparation of tailor-made polyampholytes based on polydehydroalanine (PDha). PDha, carrying amino and carboxylic moieties in each repeat unit, is characterized by a high charge density, stimuli-responsive behavior, tunable charge and the interaction with ions, small molecules and nanoparticles. Therefore, it is a promising water-soluble material to be implemented in hybrid materials and was applied as a smart dispersant, sensor or in photocatalysis. Within this thesis, PDha was modified via different synthetic routes and a straight-forward strategy was developed to obtain graft copolymers through the post-polymerization modification in water. This allowed the attachment of various side chains to tune the resulting polymer properties and solution behavior. In this regard, amphiphilic as well as double hydrophilic PDha-based graft copolymers were obtained. The amphiphilic polyampholytes were used as reversible, pH-responsive dispersants for carbon nanotubes. PDha-based graft copolymers containing hydrophilic poly(ethylene glycol) side chains were found to be suitable templates for the preparation of Au and Ag nanoparticles, as well as their nanoalloys. Here, the tunable overall charge of the PDha backbone was exploited to determine the resulting alloy composition. The attachment of N-isopropyl acryl amide gave triple stimuli-responsive polyampholytes (pH, T and metal ions). The interplay of these triggers was investigated and the polymer was used as a sensor for heavy metal cations. At last, a novel application field for polyampholytes was explored and double acidic graft copolymers (sulfonic and phosphonic acid side chains) were used as soft matrices for the light-driven hydrogen evolution from water. Therefore, various photocatalytically active components were hosted and linked by the polymeric matrix. The prepared multi-component hybrid materials showed an enhanced photocatalytic activity and high turnover numbers

Tunable X-ray source by Thomson scattering during laser-wakefield acceleration

We report results on all-optical Thomson scattering intercepting the acceleration process in a laser wakefield accelerator. We show that the pulse collision position can be detected using transverse shadowgraphy which also facilitates alignment. As the electron beam energy is evolving inside the accelerator, the emitted spectrum changes with the scattering position. Such a configuration could be employed as accelerator diagnostic as well as reliable setup to generate x-rays with tunable energy

Modulation of Tight Junction Structure and Function by Kinases and Phosphatases Targeting Occludin

Tight junctions (TJs) typically represent the most apical contacts in epithelial and endothelial cell layers where they play an essential role in the separation of extracellular or luminal spaces from underlying tissues in the body. Depending on the protein composition, TJs define the barrier characteristics and in addition maintain cell polarity. Two major families of integral membrane proteins form the typical TJ strand network, the tight junction-associated MARVEL protein (TAMP) family members occludin, tricellulin, and MarvelD3 as well as a specific set of claudins. Occludin was the first identified member of these tetraspanins and is now widely accepted as a regulator of TJ assembly and function. Therefore, occludin itself has to be tightly regulated. Phosphorylation of occludin appears to be of central importance in this context. Here we want to summarize current knowledge on the kinases and phosphatases directly modifying occludin, and their role in the regulation of TJ structure, function, and dynamics

Forward-Mode Automatic Differentiation of Compiled Programs

Algorithmic differentiation (AD) is a set of techniques that provide partial derivatives of computer-implemented functions. Such a function can be supplied to state-of-the-art AD tools via its source code, or via an intermediate representation produced while compiling its source code. We present the novel AD tool Derivgrind, which augments the machine code of compiled programs with forward-mode AD logic. Derivgrind leverages the Valgrind instrumentation framework for a structured access to the machine code, and a shadow memory tool to store dot values. Access to the source code is required at most for the files in which input and output variables are defined. Derivgrind's versatility comes at the price of scaling the run-time by a factor between 30 and 75, measured on a benchmark based on a numerical solver for a partial differential equation. Results of our extensive regression test suite indicate that Derivgrind produces correct results on GCC- and Clang-compiled programs, including a Python interpreter, with a small number of exceptions. While we provide a list of scenarios that Derivgrind does not handle correctly, nearly all of them are academic counterexamples or originate from highly optimized math libraries. As long as differentiating those is avoided, Derivgrind can be applied to an unprecedentedly wide range of cross-language or partially closed-source software with little integration efforts.Comment: 21 pages, 3 figures, 3 tables, 5 listing