325,193 research outputs found
New feature in the Auger peak of adsorbed oxygen
No full textRecent joint theoretical and experimental investigations of Auger core-core-valence spectra of alkali adatoms on simple metals have revealed that such technique is capable to ascertain contributions from different adsorption environments in the signal [M.I. Trioni, S. Caravati, G.P. Brivio, L. Floreano, F. Bruno, A. Morgante, Phys. Rev. Lett. 93 (2004) 206802]. Consequently, to verify if such an effect is present also for other chemical species, we study theoretically the KLV transition of oxygen either as a bulk impurity or as an adsorbate in/on Al and Ag (jellium-like). We make use of the Fermi golden rule in which the matrix elements of the interaction are calculated within DFT. We verify that the relevant physical quantity of this phenomenon is the excited local density of states (LDOS), calculated within a region centered on the core ionized atom. The Auger rate for oxygen in Ag bulk displays a single asymmetric peak, while for adsorbed oxygen a second smaller feature at lower energies, and very close to the first one, appears. This unexpected result follows from the removal of the degeneracy of the m quantum number of the 2p states of oxygen at the surface. It is only displayed on the electronically less dense metal (Ag), but not on Al
On vector bundles over reducible curves with a node
No full textLet C be a curve with two smooth components and a single node, and let UC(w, r, X) be the moduli space of w-semistable classes of depth one sheaves on C having rank r on both components and Euler characteristic X. In this paper, under suitable assumptions,we produce a projective bundle over the product of the moduli spaces of semistable vector bundles of rank r on each component and we show that it is birational to an irreducible component of UC(w, r, X). Then we prove the rationality of the closed subset containing vector bundles with given fixed determinant
Electron transfer with core-level excitations at hybrid interfaces
No full textElectron core-level spectroscopies have emerged as effective tools to investigate several aspects of the hybrid interface between organic molecules and a substrate. In particular, resonant photoemission spectroscopy can measure interfacial electron transfer times down to the femtosecond timescale. Furthermore, the strong perturbation induced by the core hole opens up the several questions on how the properties of the interface are modified, calling for a theoretical description of the core-excited system.
We adopt a theoretical framework based on density-functional theory (DFT), where the excitation is introduced explicitly in the core-level occupation of an atom in a molecule, to investigate the electronic structure and electron transfer from/to organic molecules adsorbed on metal, semimetal, and semiconducting substrates.
The perturbing potential lowers the energy of the molecular orbitals. Focusing on the lowest-unoccupied (LUMO), a filling of the core-excited LUMO* by substrate electrons may occur within the core-hole lifetime, as found for molecules on metals where the adsorption angle is also shown to influence the electron transfer rate [1,2]. In the case of a semimetal graphene substrate, a spin-polarized LUMO* pinned at the Fermi level can be determined for physisorbed molecules. In that case electron transfer would be suppressed given the low density of states of unsupported graphene at that energy, but still possible for graphene supported on a metal [3]. For molecules adsorbed on a semiconductor, the LUMO* may form a bound exciton in the gap [4]. Here, we found especially interesting to consider the influence of thermal motion on the energy-level alignment and the absorption coefficient [5,6].
References
[1] D. Cvetko, G. Fratesi, G. Kladnik, A. Cossaro, G.P. Brivio, L. Venkataraman, and A. Morgante, submitted.
[2] A. Baby, G. Fratesi, S.R. Vaidya, L.L. Patera, C. Africh, L. Floreano, G.P. Brivio, J. Phys. Chem. C 119 (2015) 3624.
[3] A. Ravikumar, A. Baby, H. Lin, G.P. Brivio, and G. Fratesi, Scientific Reports 6 (2016) 24603.
[4] G. Fratesi, C. Motta, M. I. Trioni, G. P. Brivio, and D. Sánchez-Portal, J. Phys. Chem. C 118 (2014) 8775
[5] H. Lin, G. Fratesi, S. Selçuk, G.P. Brivio, and A. Selloni, J. Phys. Chem. C, 120 (2016) 3899.
[6] M. Muller, D. Sànchez-Portal, H. Lin, G. Fratesi, G.P. Brivio, and A. Selloni, in preparation
Coherent systems and BGN extensions on nodal reducible curves
No full textLet (C,w) be a polarized nodal reducible curve. In this paper, we consider coherent systems of type (r,d,k) on C with k < r. We prove that the moduli spaces of -stable coherent systems stabilize for large α and we generalize several results known for the irreducible case when we choose a good polarization. Then, we study in detail the components of moduli spaces containing coherent systems arising from locally free sheaves
Nodal curves and polarizations with good properties
No full textIn this paper we deal with polarizations on a nodal curve C with smooth components. Our aim is to study and characterize a class of polarizations, which we call “good”, for which depth one sheaves on C reflect some properties that hold for vector bundles on smooth curves. We will concentrate, in particular, on the relation between the w̲-stability of OC and the goodness of w̲. We prove that these two concepts agree when C is of compact type and we conjecture that the same should hold for all nodal curves
Distributed Storage for the Provision of Ancillary Services to the Main Grid: Project PRESTO
No full textAbstractThis paper presents the three-year PRESTO research project (2013-2015). PRESTO is a self-funded project developed by the Department of Energy of Politecnico di Milano in cooperation with FIAMM Storage, Elvi Energy and MCM Energy Lab (an Italian spin-off). Within the project, experimental tests and numerical simulations were performed in order to evaluate the effectiveness of an Energy Storage System (ESS) in the provision of ancillary services to the main grid. This paper focuses specifically on the experimental and numerical analyses carried out in the project to develop an innovative control law for the primary frequency regulation, able to maximize the performances of the regulating service and effectively manage the ESS state of charge
SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks
No full textInvestigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies
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