A Kotlin multi-platform implementation of aggregate computing based on XC

The integration of technology in everyday activities is rising, with objects being increasingly equipped with computational capabilities and interconnected to form the Internet of Things, leading to the need for innovative cyber-physical services capable of creating a fast bridge between the real and virtual world. The central idea of this thesis focuses on leveraging Kotlin Multiplatform to enhance aggregate computing based on XC principles, addressing challenges in developing versatile solutions across different environments, with the need of efficient and scalable applications operating from cloud to edge to mesh networks. This thesis combines theoretical analysis, software development, and performance evaluation to assess the effectiveness of the objective, demonstrating versatility and efficiency. There is a notable improvement in performance, scalability, and adaptability across different network environments. With the proposed approach, the developed solution appears to be more efficient and effective in addressing complex challenges within systems rather than the current state of the art. The results demonstrate the transformative potential of this technology, suggesting that it can lead to more efficient and versatile service development. In summary, this thesis shows the feasibility of using Kotlin Multiplatform to implement aggregate computing based on XC, demonstrating that the proposed approach is more efficient and scalable than the state of the art. Improved performance and scalability are emphasised through this approach, which opens doors for more efficient and adaptable solutions. This study sets the stage for future developments that could improve service efficiency and effectiveness

Semi-supervised Learning in Graph Neural Networks for Structural and Property Prediction Applied to Advanced Functional Materials Design

Machine learning is becoming an integrating part of computational materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. But its efficacy is undermined by problems of data scarcity and portability challenges. This work explores the potential of graph neural networks in developing a unified predictor for material properties. The goal is to create a versatile molecular model using atomic number and relative distances as exclusive features. The model aims to handle diverse molecular classes, scales, and theory levels, enhancing precision in predicting material properties, even with limited data. To achieve this, inspired by recent advances in Natural Language Processing, we propose a Masked Molecular Modeling task, training the model in a semi-supervised manner without explicit labels. This task allows the model to predict the atomic type of masked atoms in a molecular structure, giving the opportunity to aggregate diverse data sources and mitigating data scarcity issues. We also assess the capacity of the model to perform property prediction, even with masked elements, and compare it with state-of-the-art approaches. By incorporating a graph attention mechanism, we not only enhance the model’s performance but also gain valuable insights into its internal representation and processing. This contributes to meaningful explanations and a deeper understanding of the model’s workings

Solution Processed Polymer-ABX4 Perovskite-Like Microcavities

Thanks to solution processability and broad emission in the visible spectral range, 2D hybrid perovskite-like materials are interesting for the realization of large area and flexible lighting devices. However, the deposition of these materials requires broad-spectrum solvents that can easily dissolve most of the commercial polymers and make perovskites incompatible with flexible photonics. Here, we demonstrated the integration of broadband-emitting (EDBE)PbCl4 (where EDBE = 2,2-(ethylenedioxy)bis(ethylammonium)) thin films with a solution-processed polymer planar microcavities, employing a sacrificial polymer multilayer. This approach allowed for spectral and angular redistribution of the perovskite-like material, photoluminescence, that can pave the way to all-solution-processed and flexible lightning devices that do not require complex and costly fabrication techniques

Sintesi e ricopertura organica di maghemiti per intrappolamento in polimeri biocompatibili. Applicazioni in nanomedicina.

Il seguente lavoro è stato condotto con lo scopo di sviluppare una nano-piattaforma per imaging multimodale MRI/SPECT e MRI/PET. Nanoparticelle magnetiche sono state intrappolate in un polimero biocompatibile, e la superficie delle nanostrutture risultanti è stata funzionalizzata con un agente chelante per radioisotopi

Polaron Self-localization in White-light Emitting Hybrid Perovskites

Two-dimensional (2D) perovskites with general formula $APbX_4$ are attracting increasing interest as solution processable, white-light emissive materials. Recent studies have shown that their broadband emission is related to the formation of intra-gap color centers; however, the nature and dynamics of the emissive species have remained elusive. Here we show that the broadband photoluminescence of the 2D perovskites $(EDBE)PbCl_4$ and $(EDBE)PbBr_4$ stems from the localization of small polarons within the lattice distortion field. Using a combination of spectroscopic techniques and first-principles calculations, we infer the formation of ${Pb_2}^{3+}$, $Pb^{3+}$, and ${X_2}^-$ (where X=Cl or Br) species confined within the inorganic perovskite framework. Due to strong Coulombic interactions, these species retain their original excitonic character and form self-trapped polaron-excitons acting as radiative color centers. These findings are expected to be applicable to a broad class of white-light emitting perovskites with large polaron relaxation energy.Comment: 34 pages, 15 figures, 3 table