2D-Delocalized vs Confined Diradicals

Resumen de la comunicación oral seleccionadaDiradicals are beautiful chemical objects where the more basic and intricate aspects of the chemical bonding are revealed.1 Not this being important enough, nowadays, diradical-based substrates are becoming very appealing for new organic electronic applications. We focus here in conjugated organic diradicals formed by competition between non-aromatic quinoidal structures and their canonical aromatic forms. How this quinoidal(closed-shell)-vs-aromatic(open-shell) energetic balance producing the diradical is affected by several situations has been our objective in the last few years.2 Now, we focusses on how the properties of diradicals are influenced when several diradical canonical forms are available in such a way that create a 2D (i.e., bidimensional) electron delocalization surface in which the diradical substructures are in cross-conjugation mode producing the curious effect of diradical confinement.3 Herein, the diradical molecular properties of compound 1 in Figure 1 will be discussed in connection with 2D delocalization, cross-conjugation and surface confinement. 1. Rajca, A., Chem. Rev., 1994, 94, 871; Abe, M., Chem. Rev. 2013, 113, 7011. 2. Zeng, Z.; X. Shi, L.; Chi, C.; Casado, J.; Wu, J. Chem. Soc. Rev. 2015, 44, 6578. 3. Yuan, D.; Huang, D.; Medina Rivero, S.; Carreras, A.; Zhang, C.; Zou, Y.; Jiao, X.; McNeill, C.R.; Zhu, X.; Di, C.; Zhu, D.; Casanova, D.; Casado, J. CHEM, 2019, accepted.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Diradicals and their driving forces

Several series of aromatic and quinoidal compounds, such as oligothiophenes (Scheme 1), oligophenylene-vinylenes, oligoperylenes (oligophenyls) and graphene nanoribbon derivatives, are studied in the common context of the capability to stabilize diradical structures. [1,2,3,4]. In this work, we try to clarify how several driving forces (i.e., thermodynamic and entropic) are responsible for the generation of diradical and diradicaloid structures. A combination of different types of molecular spectroscopies (i.e., electronic absorption, electronic emission, excited state absorption, vibrational Raman, vibrational infrared, etc.) as well as hybridized with thermal and pressure-dependent techniques are shown to provide important information about the origin of the formation and stabilization of diradicals. From a conceptual point of view, we analyze these properties in the context of the oligomer approach which is the study of the evolution of these spectroscopic quantities as a function of the oligomer size. References [1] P. Mayorga Burrezo, J.L. Zafra, J. Casado. Angew. Chem. Int. Ed., 2017, 56, 2250. [2] J. Casado, R. Ponce Ortiz, J. T. Lopez Navarrete, Chem. Soc. Rev. 2012, 41, 5672. [3] P. Mayorga Burrezo, X. Zhu, S. F. Zhu, Q. Yan, J. T. Lopez Navarrete, H. Tsuji, E. Nakamura, J. Casado, J. Am. Chem. Soc. 2015, 137, 3834-3843. [4] J. Casado, Para-quinodimethanes: A unified review of the quinoidal-versus-aromatic competition and its implications. Top. Curr. Chem. 2017, 375, 73.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Fourier transform and rigidity of certain distributions

Let $E$ be a finite dimensional vector space over a local field, and $F$ be its dual. For a closed subset $X$ of $E$, and $Y$ of $F$, consider the space $D^{-\xi}(E;X,Y)$ of tempered distributions on $E$ whose support are contained in $X$ and support of whose Fourier transform are contained in $Y$. We show that $D^{-\xi}(E;X,Y)$ possesses a certain rigidity property, for $X$, $Y$ which are some finite unions of affine subspaces.Comment: 10 page

The effect of Hebbian plasticity on the attractors of a dynamical system

Poster presentation A central problem in neuroscience is to bridge local synaptic plasticity and the global behavior of a system. It has been shown that Hebbian learning of connections in a feedforward network performs PCA on its inputs [1]. In recurrent Hopfield network with binary units, the Hebbian-learnt patterns form the attractors of the network [2]. Starting from a random recurrent network, Hebbian learning reduces system complexity from chaotic to fixed point [3]. In this paper, we investigate the effect of Hebbian plasticity on the attractors of a continuous dynamical system. In a Hopfield network with binary units, it can be shown that Hebbian learning of an attractor stabilizes it with deepened energy landscape and larger basin of attraction. We are interested in how these properties carry over to continuous dynamical systems. Consider system of the form Math(1) where xi is a real variable, and fi a nondecreasing nonlinear function with range [-1,1]. T is the synaptic matrix, which is assumed to have been learned from orthogonal binary ({1,-1}) patterns ξμ, by the Hebbian rule: Math. Similar to the continuous Hopfield network [4], ξμ are no longer attractors, unless the gains gi are big. Assume that the system settles down to an attractor X*, and undergoes Hebbian plasticity: T´ = T + εX*X*T, where ε > 0 is the learning rate. We study how the attractor dynamics change following this plasticity. We show that, in system (1) under certain general conditions, Hebbian plasticity makes the attractor move towards its corner of the hypercube. Linear stability analysis around the attractor shows that the maximum eigenvalue becomes more negative with learning, indicating a deeper landscape. This in a way improves the system´s ability to retrieve the corresponding stored binary pattern, although the attractor itself is no longer stabilized the way it does in binary Hopfield networks

A 0.18μm CMOS 300MHz Current-Mode LF Seventh-order Linear Phase Filter for Hard Disk Read Channels

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”A 300MHz CMOS seventh-order linear phase gm-C filter based on a current-mode multiple loop feedback (MLF) leap-frog (LF) structure is realized. The filter is implemented using a fully-differential linear operational transconductance amplifier (OTA) based on a source degeneration topology. PSpice simulations using a standard TSMC 0.18μm CMOS process with 2.5V power supply have shown that the cut-off frequency of the filter can be tuned from 260MHz to 320MHz and dynamic range is about 66dB. Group delay ripple is approximately 4.5% over the whole tuning range and maximum power consumption is 210mW