Ab-initio determination of x-ray absorption near edge structure (xanes) spectra in an ultrasoft and norm conserving pseudopotentials scheme

X-ray absorption spectroscopy (XAS) is a powerful means of investigation of structural and electronic properties in condensed -matter physics. Analysis of the near edge part of the XAS spectrum, the so – called X-ray Absorption Near Edge Structure (XANES), can typically provide the following information on the photoexcited atom: - Oxidation state and coordination environment. - Speciation of transition metal compounds. - Conduction band DOS projected on the excited atomic species (PDOS). Analysis of XANES spectra is greatly aided by simulations; in the most common scheme the multiple scattering framework is used with the muffin tin approximation for the scattering potential and the spectral simulation is based on a hypothetical, reference structure. This approach has the advantage of requiring relatively little computing power but in many cases the assumed structure is quite different from the actual system measured and the muffin tin approximation is not adequate for low symmetry structures or highly directional bonds. It is therefore very interesting and justified to develop alternative methods. In one approach, the spectral simulation is based on atomic coordinates obtained from a DFT (Density Functional Theory) optimized structure. In another approach, which is the object of this thesis, the XANES spectrum is calculated directly based on an ab – initio DFT calculation of the atomic and electronic structure. This method takes full advantage of the real many-electron final wavefunction that can be computed with DFT algorithms that include a core-hole in the absorbing atom to compute the final cross section. To calculate the many-electron final wavefunction the Projector Augmented Wave method (PAW) is used. In this scheme, the absorption cross section is written in function of several contributions as the many-electrons function of the finale state; it is calculated starting from pseudo-wavefunction and performing a reconstruction of the real-wavefunction by using a transform operator which contains some parameters, called partial waves and projector waves. The aim of my thesis is to apply and test the PAW methodology to the calculation of the XANES cross section. I have focused on iron and silicon structures and on some biological molecules target (myoglobin and cytochrome c). Finally other inorganic and biological systems could be taken into account for future applications of this methodology, which could become an important improvement with respect to the multiscattering approach.

Charge accumulation and transport in degenerately doped semiconducting polymers with mixed ionic and electronic conductivity

This thesis is part of the fields of Material Physics and Organic Electronics and aims to determine the charge carrier density and mobility in the hydrated conducting polymer–polyelectrolyte blend PEDOT:PSS. This kind of material combines electronic semiconductor functionality with selective ionic transport, biocompatibility and electrochemical stability in water. This advantageous material properties combination makes PEDOT:PSS a unique material to build organic electrochemical transistors (OECTs), which have relevant application as amplifying transducers for bioelectronic signals. In order to measure charge carrier density and mobility, an innovative 4-wire, contact independent characterization technique was introduced, the electrolyte-gated van der Pauw (EgVDP) method, which was combined with electrochemical impedance spectroscopy. The technique was applied to macroscopic thin film samples and micro-structured PEDOT:PSS thin film devices fabricated using photolithography. The EgVDP method revealed to be effective for the measurements of holes’ mobility in hydrated PEDOT:PSS thin films, which resulted to be <μ>=(0.67±0.02) cm^2/(V*s). By comparing this result with 2-point-probe measurements, we found that contact resistance effects led to a mobility overestimation in the latter. Ion accumulation at the drain contact creates a gate-dependent potential barrier and is discussed as a probable reason for the overestimation in 2-point-probe measurements. The measured charge transport properties of PEDOT:PSS were analyzed in the framework of an extended drift-diffusion model. The extended model fits well also to the non-linear response in the transport characterization and results suggest a Gaussian DOS for PEDOT:PSS. The PEDOT:PSS-electrolyte interface capacitance resulted to be voltage-independent, confirming the hypothesis of its morphological origin, related to the separation between the electronic (PEDOT) and ionic (PSS) phases in the blend.

A deep learning model via thermodynamics and condensed matter physics

Deep Learning architectures give brilliant results in a large variety of fields, but a comprehensive theoretical description of their inner functioning is still lacking. In this work, we try to understand the behavior of neural networks by modelling in the frameworks of Thermodynamics and Condensed Matter Physics. We approach neural networks as in a real laboratory and we measure the frequency spectrum and the entropy of the weights of the trained model. The stochasticity of the training occupies a central role in the dynamics of the weights and makes it difficult to assimilate neural networks to simple physical systems. However, the analogy with Thermodynamics and the introduction of a well defined temperature leads us to an interesting result: if we eliminate from a CNN the "hottest" filters, the performance of the model remains the same, whereas, if we eliminate the "coldest" ones, the performance gets drastically worst. This result could be exploited in the realization of a training loop which eliminates the filters that do not contribute to loss reduction. In this way, the computational cost of the training will be lightened and more importantly this would be done by following a physical model. In any case, beside important practical applications, our analysis proves that a new and improved modeling of Deep Learning systems can pave the way to new and more efficient algorithms.

Characterization of in-gap electronic states in two-dimensional single crystal PEA2PbBr4 perovskite for X-ray detection

Hybrid Organic-Inorganic Halide Perovskites (HOIPs) include a large class of materials described with the general formula ABX3, where A is an organic cation, B an inorganic cation and X an halide anion. HOIPs show excellent optoelectronic characteristics such as tunable band gap, high adsorption coefficient and great mobility life-time. A subclass of these materials, the so-called two- dimensional (2D) layered HOIPs, have emerged as potential alternatives to traditional 3D analogs to enhance the stability and increase performance of perovskite devices, with particular regard in the area of ionizing radiation detectors, where these materials have reached truly remarkable milestones. One of the key challenges for future development of efficient and stable 2D perovskite X-ray detector is a complete understanding of the nature of defects that lead to the formation of deep states. Deep states act as non-radiative recombination centers for charge carriers and are one of the factors that most hinder the development of efficient 2D HOIPs-based X-ray detectors. In this work, deep states in PEA2PbBr4 were studied through Photo-Induced Current Transient Spectroscopy (PICTS), a highly sensitive spectroscopic technique capable of detecting the presence of deep states in highly resistive ohmic materials, and characterizing their activation energy, capture cross section and, under stringent conditions, the concentration of these states. The evolution of deep states in PEA 2 PbBr 4 was evaluated after exposure of the material to high doses of ionizing radiation and during aging (one year). The data obtained allowed us to evaluate the contribution of ion migration in PEA2PbBr4. This work represents an important starting point for a better understanding of transport and recombination phenomena in 2D perovskites. To date, the PICTS technique applied to 2D perovskites has not yet been reported in the scientific literature.

Charge and spin transport in memristive La0.7Sr0.3Mno3/SrTiO3/Co devices

Il lavoro svolto si concentra sul trasporto di carica e spin in dispositivi trilayer La0.7Sr0.3MnO3/SrTiO3/Co multifunzionali. Questi dispositivi mostrano sia magnetoresistenza che resistive switching, con un'interessante interazione fra i due effetti. Le giunzioni SrTiO3 sono state scelte per questo lavoro sia per via dei precedenti studi su SrTiO3 come barriera in dispositivi spintronici (cioè dispositivi con magnetoresistenza), sia perché sono promettenti come materiale base per costruire memristor (cioè dispositivi con resistive switching). Il lavoro di tesi è stato svolto all'Istituto per lo studio dei materiali nanostrutturati (ISMN-CNR) a Bologna. Nella prima parte di questa tesi illustrerò la fisica dietro al resistive switching e alla magnetoresistenza di dispositivi trilayer, mostrando anche risultati di studi su dispositivi simili a quelli da me studiati. Nella seconda parte mostrerò la complessa fisica degli ossidi utilizzati nei nostri dispositivi e i possibili meccanismi di trasporto attraverso essi. Nell'ultima parte descriverò i risultati ottenuti. I dispositivi La0.7Sr0.3MnO3/SrTiO3/Co sono stati studiati tramite caratterizzazione elettrica, di magnetotrasporto e con spettroscopia di impedenza. Le misure ottenute hanno mostrato una fisica molto ricca dietro al trasporto di spin e carica in questi dispositivi, e la mutua interazione fra fenomeni spintronici e di resistive switching rappresenta una chiave per comprendere la fisica di questi fenomeni. Analisi dati della dipendenza della resistenza della temperature e caratteristiche corrente-tensioni saranno usati per quantificare e descrivere il trasporto in questi dispositivi.

Functionals for TDDFT description of electron transport in nanostructures

La Teoria di Densità Funzionale (DFT) e la sua versione dipendente dal tempo (TDDFT) sono strumenti largamente usati per simulare e calcolare le proprietà statiche e dinamiche di sistemi con elettroni interagenti. La precisione del metodo si basa su una serie di approssimazioni degli effetti di exchange correlation fra gli elettroni, descritti da un funzionale della sola densità di carica. Nella presente tesi viene testata l'affidabilità del funzionale Mixed Localization Potential (MLP), una media pesata fra Single Orbital Approximation (SOA) e un potenziale di riferimento, ad esempio Local Density Approximation (LDA). I risultati mostrano capacità simulative superiori a LDA per i sistemi statici (curando anche un limite di LDA noto in letteratura come fractional dissociation) e dei progressi per sistemi dinamici quando si sviluppano correnti di carica. Il livello di localizzazione del sistema, inteso come la capacità di un elettrone di tenere lontani da sé altri elettroni, è descritto dalla funzione Electron Localization Function (ELF). Viene studiato il suo ruolo come guida nella costruzione e ottimizzazione del funzionale MLP.

Smart freight transport in urban areas: a crowd-shipping model using autonomous vehicles

L'avanzamento dell'e-commerce e l'aumento della densità abitativa nel centro città sono elementi che incentivano l'incremento della richiesta merci all'interno dei centri urbani. L'attenzione all'impatto ambientale derivante da queste attività operative è un punto focale oggetto di sempre maggiore interesse. Attraverso il seguente studio, l'obiettivo è definire attuali e potenziali soluzioni nell'ambito della logistica urbana, con particolare interesse alle consegne dell'ultimo miglio. Una soluzione proposta riguarda la possibilità di sfruttare la capacità disponibile nei flussi generati dalla folla per movimentare merce, pratica nota sotto il nome di Crowd-shipping. L'idea consiste nella saturazione di mezzi già presenti nella rete urbana al fine di ridurre il numero di veicoli commerciali e minimizzare le esternalità negative annesse. A supporto di questa iniziativa, nell'analisi verranno considerati veicoli autonomi elettrici a guida autonoma. La tesi è incentrata sulla definizione di un modello di ottimizzazione matematica, che mira a designare un network logistico-distributivo efficiente per le consegne dell'ultimo miglio e a minimizzare le distanze degli attori coinvolti. Il problema proposto rappresenta una variante del Vehicle Routing Problem con time windows e multi depots. Il problema è NP-hard, quindi computazionalmente complesso per cui sarà necessario, in fase di analisi, definire un approccio euristico che permetterà di ottenere una soluzione sub-ottima in un tempo di calcolo ragionevole per istanze maggiori. L'analisi è stata sviluppata nell'ambiente di sviluppo Eclipse, attraverso il risolutore Cplex, in linguaggio Java. Per poterne comprendere la validità, è prevista un'ultima fase in cui gli output del modello ottimo e dell'euristica vengono confrontati tra loro su parametri caratteristici. Bisogna tuttavia considerare che l' utilizzo di sistemi cyber-fisici a supporto della logistica non può prescindere da un costante sguardo verso il progresso.