Archivio Istituzionale della Ricerca - Università degli Studi di Pavia
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Intercalation voltages and ion diffusion in Mn-based transition metal fluorophosphates as cathode materials for Na-ion batteries: a synergistic experimental and theoretical approach
No full textTransition metal fluorophosphates of general formula Na2MPO4F (M = transition metal) are interesting cathode materials for Na-ion batteries (NIBs), thanks to predicted high intercalation voltages and high theoretical capacities. However, the practical applications of several compositions in this family of compounds is limited by effective capacities lower than the theoretical ones and by high capacity fading, particularly at high charging rates. Thanks to the synergy of a broad spectrum of experimental and theoretical techniques, this work presents an extensive characterization of the electrochemical behavior of the prospect cathode material Na2-xMnPO4F (x = 0, 0.5, 1 and 2). Ab initio calculations, performed using Hubbard-corrected Kohn-Sham density functional theory (DFT) according to the DFT + U + V scheme, confirmed that the calculated intercalation voltages for the NaMnPO4F/MnPO4F couple are outside the stability window of conventional liquid electrolytes, thus limiting the material's performances to the extraction of 1 Na-ion per formula unit. To investigate the role of Na-ion diffusion on the electrochemical properties of this system, bond valence site energy (BVSE) analyses and molecular dynamics were used to identify the main diffusion pathways, and to correlate their energetics to the distortion of the Mn-O/F octahedra at various Na concentrations. The resulting 3D diffusion pathway is characterized by a relatively high diffusion coefficient for Na-ion in the perfect crystal, suggesting that the experimentally observed performances, lower than expected particularly at high charging rates, can only marginally be attributed to limited Na-ion diffusion in the system
Sfruttare la Sovraparametrizzazione dal Controllo Ottimale Quantistico al Machine Learning Quantistico
No full textLa meccanica quantistica, la principale teoria della fisica moderna, offre una comprensione senza precedenti dei sistemi ai livelli atomico e subatomico. A differenza della meccanica classica, che governa gli oggetti macroscopici, la meccanica quantistica è caratterizzata da fenomeni come la sovrapposizione e l'entanglement. Questi fenomeni diventano particolarmente rilevanti nella simulazione di sistemi quantistici complessi, dove i metodi di simulazione classica richiedono risorse computazionali in aumento esponenziale. Per evitare questo problema, l'idea di sviluppare computer quantistici ha preso piede nel corso del XX secolo. Successivamente, è emerso il campo del Controllo Ottimale Quantistico (QOC) per guidare ottimamente la dinamica dei sistemi quantistici attraverso controlli esterni, mentre la combinazione di calcolo con computer quantistici e il paradigma emergente dell'intelligenza artificiale ha dato vita al crescente campo del Machine Learning Quantistico (QML), mirato a imparare pattern dai dati e prendere decisioni autonome.
Questa tesi di dottorato tenta di unificare questi due campi apparentemente distinti sotto un comune quadro teorico. Presentando un'esplorazione dettagliata di ciascun campo, scopriamo i principi condivisi, in particolare nelle tecniche di ottimizzazione, controllabilità e nel concetto emergente di sovraparametrizzazione. Queste caratteristiche comuni fungono da fondamento per questo lavoro, offrendo nuove intuizioni e applicazioni in entrambi i domini. In modo specifico, questo lavoro si concentra sulla sovraparametrizzazione, un potente strumento che consente una convergenza esponenziale nei compiti di ottimizzazione ma che può anche portare a conseguenze indesiderate. Qui sfruttiamo questa tecnica da un lato, proponendo contemporaneamente nuove tecniche per prevenire tali effetti indesiderati.
Nel QOC, applichiamo la sovraparametrizzazione per affrontare la sfida di lunga data di ottenere operazioni entangled deterministicheQuantum mechanics, the leading theory of modern physics, provides an unparalleled understanding of systems at atomic and subatomic levels. Unlike classical mechanics, which governs macroscopic objects, quantum mechanics is characterized by phenomena such as superposition and entanglement. These phenomena become especially relevant when simulating complex quantum systems, where classical simulation methods require exponentially increasing computational resources. To avoid this problem, the idea of developing quantum computers has caught on during the 20th century. Later in time, the field of Quantum Optimal Control (QOC) emerged to optimally guide the dynamics of quantum systems through external controls, while the combination of computation employing quantum computers and the emerging paradigm of artificial intelligence gave birth to the growing field of Quantum Machine Learning (QML), aiming at learning patterns from data and make autonomous decisions.
This Ph.D. thesis is an attempt to unify these two seemingly distinct fields under a common theoretical framework. By presenting a detailed exploration of each field, we uncover the shared principles, particularly in optimization techniques and landscape, controllability, and the emerging concept of overparametrization. These shared features serve as the foundation for this work, offering new insights and applications across both domains. More specifically, the attention of this work is focused on overparametrization, a powerful tool enabling exponential convergence in optimization tasks that may also lead to undesired consequences. Here we leverage this technique on one side, and in parallel we propose novel techniques to prevent such undesired effects.
In QOC, we apply overparametrization to tackle the longstanding challenge of obtaining deterministic entangling operations using weak photonic nonlinearities with high fidelity. By going beyond the quantum speed limit, which in the common language of QOC simply corresponds to access the overparametrized regime, we design a numerical protocol that optimally tunes nonlinear quantum interferometers, enabling the realization of deterministic entangling gates, such as the CNOT and Mølmer-Sørensen gates, using single-photon qubit encoding. This discovery is a significant advancement, pushing the boundaries of what can be achieved with weak photon-photon interactions in integrated quantum photonic circuits.
In the context of QML, we address the potential pitfalls of overparametrization, particularly the risk of overfitting with Quantum Neural Networks (QNNs). To mitigate overfitting issues, we investigated the use of quantum dropout as a regularization technique, demonstrating its effectiveness in preventing overfitting while preserving the expressibility and entanglement associated to the QNN. By carefully controlling the dropout probability, we ensured that the model remained overparametrized without losing its ability to generalize. This study offers valuable guidelines for practitioners aiming to use overparametrized QNNs in machine learning tasks, presenting quantum dropout as a promising tool for enhancing model performance.
Finally, we explore the diagnostic capabilities of the Quantum Neural Tangent Kernel (QNTK) as a tool for assessing the training and generalization abilities of overparametrized QNNs, without the need for extensive training. The QNTK provides an efficient means of predicting the behaviour of both shallows and overparametrized QNNs, particularly in scenarios in which overfitting hinders generalization or barren plateaus — a common issue in quantum optimization — make training unfeasible. We conclude that by leveraging the QNTK, researchers can bypass the computational difficulties associated with QNNs, significantly reducing the time and resources required for efficient training
Volumetric atlas of the rat inner ear from microCT and iDISCO+ cleared temporal bone
No full textWe have reconstructed 34 structures from the rat temporal bone, which are available as both image stacks and printable 3D objects in a shared repository for download. These can be used for teaching, localizing cells or other features within the ear, modeling auditory and vestibular sensory physiology and training of automated segmentation machine learning tools
The Philosopher in the Cage: Philosophy and Animality in Leon Battista Alberti
No full textAbstract · This article offers an interpretation of Momus by Leon Battista Alberti
as an ironic reflection on the nature and pretensions of philosophy. It does so by
analysing the significance of the parallels between philosophers and animals in the
story, and especially of a number of role exchanges in which animals take the place
of philosophers, or vice versa. Alberti employs these exchanges programmatically in
order to diagnose the shortcomings of philosophy, especially its autoreferentiality,
and its tendency to favour authority over sound arguments. Yet, this paper suggests
that Alberti does not dismiss philosophy as such, but that his ironic treatment of the
philosophers’ faults prompts a necessary transition from philosophical theory to an
improved philosophical practice.
Keywords · Leon Battista Alberti, Momus, Philosophers, Animals, Persona
Design and Mechanistic Analysis of a Potent Bivalent Inhibitor of Transthyretin Amyloid Fibrillogenesis
No full textTransthyretin amyloidosis (ATTR) is a systemic disease that primarily affects the heart and the peripheral nervous system. Despite available therapeutic options, advanced ATTR amyloidosis still presents unmet medical needs. We have therefore focused on the design of bivalent small molecules starting from our prototype palindromic ligand mds84, whose binding by transthyretin (TTR) greatly improves stability of the native structure by overcoming the negative cooperativity which is typical of monovalent stabilizers. Among the newly designed compounds here, we present B26, which is pseudoirreversibly bound by native TTR with faster entry kinetics into the protein molecule compared to mds84. It retains the ability to inhibit fibril formation in vitro, together with improved solubility. Using solution NMR, we show that B26 occupies both TTR binding sites simultaneously, leading to conformational effects distant from the binding site, including the proteolytic cleavage site involved in fibril formation by the mechano-enzymatic mechanism
A Tele-Coaching Pilot Study: An Innovative Approach to Enhance Motor Skills in Adolescents With Down Syndrome
No full textBackground: Limited knowledge exists regarding the effectiveness of training programmes for individuals with Down syndrome, particularly innovative approaches like tele-coaching. Our pilot study aimed to improve strength and balance using tele-coaching sessions in children with Down syndrome. Materials and methods: We enrolled 18 children and adolescents (aged 9-17 years) with Down syndrome. The intervention consisted of a training programme based on games and was conducted remotely through an online platform (e-gym) 3 days per week (15 weeks). Participants engaged in playful activities targeting limb strength and balance. Results: We found an improvement in systolic blood pressure (p = 0.04) and balance (p = 0.002). Our analysis showed a non-significant decrease in adiposity parameters, including weight, BMI, BMI z-score, WC and WC/H. Conclusions: Our findings contribute to evidence supporting online exercise interventions for individuals with Down syndrome. Integrating these interventions into community support programmes could enhance access to tailored services
Sustainable adaptation of heritage buildings in tropical rainforest climates: The innovative practice of Tanjong Pagar Railway Station in Singapore
No full textHeritage buildings in tropical climates face unique challenges in balancing preservation with modern sustainability goals, particularly in energy efficiency and climate resilience. This study introduces a methodological framework for adapting historic buildings in tropical climates while improving their energy performance. Singapore’s Tanjong Pagar Railway Station serves as the primary case study for this investigation. The methodology combines climate analysis, heritage-compatible redesign, and photovoltaic integration. These elements are validated through sophisticated digital modeling and performance simulations. Results demonstrate significant energy efficiency improvements through custom-designed photovoltaic systems, potentially reducing annual energy consumption by up to 83% while preserving historical integrity. This research provides a scalable model for sustainable heritage conservation in tropical regions. It offers valuable insights for urban planners, policymakers, and conservationists working at the intersection of cultural preservation and sustainable development. The findings can inform policy development and conservation practices across Southeast Asia and similar tropical regions facing heritage adaptation challenges