Coulomb interaction in graphene: Relaxation rates and transport

We analyze the inelastic electron-electron scattering in undoped graphene within the Keldysh diagrammatic approach. We demonstrate that finite temperature strongly affects the screening properties of graphene, which, in turn, influences the inelastic scattering rates as compared to the zero-temperature case. Focussing on the clean regime, we calculate the quantum scattering rate which is relevant for dephasing of interference processes. We identify an hierarchy of regimes arising due to the interplay of a plasmon enhancement of the scattering and finite-temperature screening of the interaction. We further address the energy relaxation and transport scattering rates in graphene. We find a non-monotonic energy dependence of the inelastic relaxation rates in clean graphene which is attributed to the resonant excitation of plasmons. Finally, we discuss the temperature dependence of the conductivity at the Dirac point in the presence of both interaction and disorder. Our results complement the kinetic-equation and hydrodynamic approaches for the collision-limited conductivity of clean graphene and can be generalized to the treatment of physics of inelastic processes in strongly non-equilibrium setups.Comment: 28 pages, 16 figure

Quantum-chemical insights from deep tensor neural networks

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems

Autonomous robotic nanofabrication with reinforcement learning

The ability to handle single molecules as effectively as macroscopic building-blocks would enable the construction of complex supramolecular structures inaccessible to self-assembly. The fundamental challenges obstructing this goal are the uncontrolled variability and poor observability of atomic-scale conformations. Here, we present a strategy to work around both obstacles, and demonstrate autonomous robotic nanofabrication by manipulating single molecules. Our approach employs reinforcement learning (RL), which finds solution strategies even in the face of large uncertainty and sparse feedback. We demonstrate the potential of our RL approach by removing molecules autonomously with a scanning probe microscope from a supramolecular structure -- an exemplary task of subtractive manufacturing at the nanoscale. Our RL agent reaches an excellent performance, enabling us to automate a task which previously had to be performed by a human. We anticipate that our work opens the way towards autonomous agents for the robotic construction of functional supramolecular structures with speed, precision and perseverance beyond our current capabilities.Comment: 3 figure

In situ observation of compressive deformation of an interconnected network of zinc oxide tetrapods

Zinc oxide tetrapods have remarkable functional and mechanical properties with potential applications in different fields including nanoelectronic and optoelectronic sensing, functional composites and coatings, as well as energy harvesting and storage. Based on the 3D shape of these microparticles, they can be assembled into highly porous (up to 98%) macroscopic ceramic framework structures that can be utilized as a versatile template for the fabrication of other multi-scaled foam-like materials. Here we investigated the three-dimensional structure of low density interconnected zinc oxide tetrapod networks by high resolution X-ray computed tomography. In situ observations during mechanical loading show inhomogeneous development of anelastic strain (damage) during compression, and homogeneous elastic recovery on unloading. Individual tetrapods are observed to deform by arm rotation to accommodate strain

Biomimetic Carbon-Fiber Systems Engineering: A Modular Design Strategy to Generate Biofunctional Composites from Graphene and Carbon Nanofibers

electrical conductivity. It is additionally advantageous if such materials resembled the structural and biochemical features of the natural extracellular environment. Here we show a novel modular design strategy to engineer biomimetic carbon-fiber based scaffolds. Highly porous ceramic zinc oxide (ZnO) microstructures serve as 3D sacrificial templates and are infiltrated with carbon nanotube (CNT) or graphene dispersions. Once the CNTs and graphene uniformly coat the ZnO template, the ZnO is either removed by hydrolysis or converted into carbon by chemical vapor deposition (CVD). The resulting 3D carbon scaffolds are both hierarchically ordered and free-standing. The properties of the micro-fibrous scaffolds were tailored with a high porosity (up to 93 %), high Young’s modulus (~0.027 to ~22 MPa), and an electrical conductivity of (~0.1 to ~330 S/m), as well as different surface compositions. Cell viability and fibroblast proliferation rate and protein adsorption rate assays have shown that the generated scaffolds are biocompatible and have a high protein adsorption capacity (up to 77.32 ±6.95 mg/cm3), so that they not only are able to resemble the ECM structurally, but also biochemically. The scaffolds also allow for the successful growth and adhesion of fibroblast cells showing that we provide a novel, highly scalable modular design strategy to generate biocompatible carbon-fiber systems that mimic the extracellular matrix with the additional feature of conductivity.RA gratefully acknowledges partial project funding by the Deutsche Forschungsgemeinschaft under contract FOR1616. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. GrapheneCore2 785219. CS is supported by the European Research Council (ERC StG 336104 CELLINSPIRED, ERC PoC 768740 CHANNELMAT), by the German Research Foundation (RTG 2154, SFB 1261 project B7). MT acknowledges support from the German Academic Exchange Service (DAAD) through a research grant for doctoral candidates (91526555-57048249). We acknowledge funding from EPSRC grants EP/P02534X/1, ERC grant 319277 (Hetero2D) the Royal Academy of Engineering Enterprise Scheme, the Trinity College, Cambridge, and the Isaac Newton Trust

Interpolated sequences and critical LL-values of modular forms

Recently, Zagier expressed an interpolated version of the Ap\'ery numbers for $\zeta(3)$ in terms of a critical $L$-value of a modular form of weight 4. We extend this evaluation in two directions. We first prove that interpolations of Zagier's six sporadic sequences are essentially critical $L$-values of modular forms of weight 3. We then establish an infinite family of evaluations between interpolations of leading coefficients of Brown's cellular integrals and critical $L$-values of modular forms of odd weight.Comment: 23 pages, to appear in Proceedings for the KMPB conference: Elliptic Integrals, Elliptic Functions and Modular Forms in Quantum Field Theor

Ethanol Sensing Performances of Zinc-doped Copper Oxide Nano-crystallite Layers

The synthesis via chemical solutions (aqueous) (SCS) wet route is a low-temperature and cost-effective growth technique of high crystalline quality oxide semiconductors films. Here we report on morphology, chemical composition, structure and ethanol sensing performances of a device prototype based on zincdoped copper oxide nanocrystallite layer. By thermal annealing in electrical furnace for 30 min at temperatures higher than 550 ˚C, as-deposited zinc doped Cu2O samples are converted to tenorite, ZnxCu1-xOy, (x=1.3wt%) that demonstrate higher ethanol response than sensor structures based on samples treated at 450 ˚C. In case of the specimens after post-growth treatment at 650 ˚C was found an ethanol gas response of about 79 % and 91 % to concentrations of 100 ppm and 500 ppm, respectively, at operating temperature of 400 ˚C in air

Quantization and Compressive Sensing

Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This book chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, we consider several forms of scalar quantizers, namely uniform, non-uniform, and 1-bit. We provide performance bounds and fundamental analysis, as well as practical quantizer designs and reconstruction algorithms that account for quantization. Furthermore, we provide an overview of Sigma-Delta ($\Sigma\Delta$) quantization in the compressed sensing context, and also discuss implementation issues, recovery algorithms and performance bounds. As we demonstrate, proper accounting for quantization and careful quantizer design has significant impact in the performance of a compressive acquisition system.Comment: 35 pages, 20 figures, to appear in Springer book "Compressed Sensing and Its Applications", 201

The H1 Forward Proton Spectrometer at HERA

The forward proton spectrometer is part of the H1 detector at the HERA collider. Protons with energies above 500 GeV and polar angles below 1 mrad can be detected by this spectrometer. The main detector components are scintillating fiber detectors read out by position-sensitive photo-multipliers. These detectors are housed in so-called Roman Pots which allow them to be moved close to the circulating proton beam. Four Roman Pot stations are located at distances between 60 m and 90 m from the interaction point.Comment: 20 pages, 10 figures, submitted to Nucl.Instr.and Method

Desynchronizing effect of high-frequency stimulation in a generic cortical network model

Transcranial Electrical Stimulation (TCES) and Deep Brain Stimulation (DBS) are two different applications of electrical current to the brain used in different areas of medicine. Both have a similar frequency dependence of their efficiency, with the most pronounced effects around 100Hz. We apply superthreshold electrical stimulation, specifically depolarizing DC current, interrupted at different frequencies, to a simple model of a population of cortical neurons which uses phenomenological descriptions of neurons by Izhikevich and synaptic connections on a similar level of sophistication. With this model, we are able to reproduce the optimal desynchronization around 100Hz, as well as to predict the full frequency dependence of the efficiency of desynchronization, and thereby to give a possible explanation for the action mechanism of TCES.Comment: 9 pages, figs included. Accepted for publication in Cognitive Neurodynamic