arXiv subjects

Paolo Moretti

Publications and source records attributed to Paolo Moretti.

At least 19 recordsLinked to original sources

arXiv2026

Patterns of load, elastic energy and damage in network models of architected composite materials

We investigate the role of architected thin films in the interfacial failure properties of bi-layer composites. Our results show that, while graded structures can be used to prescribe failure at the interface, they do not offer significant advantages in terms of fracture toughness. Hierarchically patterned layers can localize failure at the interface and simultaneously enhance interface toughness, by enforcing a buffer region where elastic energy is dissipated in the form of diffuse damage, so that no stress concentration can drive crack growth. To analyze these mechanisms, the associated patterns of local load redistribution and the soft deformation modes, we develop a network formalism that brings together concepts of discrete differential geometry and spectral graph theory.

cond-mat.mtrl-sci↗

arXiv2025

Fiber bundle model of thermally activated creep failure

An equal load sharing fiber bundle model for thermally activated breakdown is developed using transition state theory to describe the rate of elementary failures. The lifetime distribution, average, variance and their asymptotic limits for uniform fiber failure thresholds are derived and found to be in excellent agreement with simulations. The asymptotic scaling with regards to the number of fibers matches analytical approximations in the low temperature limit derived by Roux and co-workers for a model of thermal breakdown by stationary Gaussian noise. For the case of randomly distributed fiber failure strengths, the lifetime distribution is derived as a multidimensional integral with no closed form solution. Simulations with different fiber strength distributions indicate that, in the limit of large fiber numbers, the statistics of bundle lifetimes shows a similar asymptotic scaling for distributed and for uniform thresholds. Fiber breakage by thermal activation occurs in avalanches triggered by individual thermally activated failure events, and the asymptotic avalanche size distribution obtained from the simulations matches earlier theoretical results derived for quasistatic loading.

cond-mat.stat-mech↗

arXiv2024

Tuning load redistribution and damage near heterogeneous interfaces

We investigate interface failure of model materials representing architected thin films in contact with heterogeneous substrates. We find that, while systems with statistically isotropic distributions of impurities derive their fracture strength from the ability to develop rough detachment fronts, materials with hierarchical microstructures confine failure near a prescribed surface, where crack growth is arrested and crack surface correlations are suppressed. We develop a theory of network Green's functions for the systems at hand, and we find that the ability of hierarchical microstructures to control failure mode and locations comes at no performance cost in terms of peak stress and specific work of failure and derives from the quenched local anistotropy of the elastic interaction kernel.

cond-mat.mtrl-sci↗

arXiv2023

Creep failure of hierarchical materials

Creep failure of hierarchical materials is investigated by simulation of beam network models. Such models are idealizations of hierarchical fibrous materials where bundles of load-carrying fibers are held together by multi-level (hierarchical) cross-links. Failure of individual beams is assumed to be governed by stress-assisted thermal activation over local barriers, and beam stresses are computed by solving the global balance equations of linear and angular momentum across the network. Disorder is mimicked by a statistical distribution of barrier heights. Both initially intact samples and samples containing side notches of various length are considered. Samples with hierarchical cross-link patterns are simulated alongside reference samples where cross-links are placed randomly without hierarchical organization. The results demonstrate that hierarchical patterning may strongly increase creep strain and creep lifetime while reducing the lifetime variation. This is due to the fact that hierarchical patterning induces a failure mode that differs significantly from the standard scenario of failure by nucleation and growth of a critical crack. Characterization of this failure mode demonstrates good agreement between the present simulations and experimental findings on hierarchically patterned paper sheets.

cond-mat.mtrl-sci↗

arXiv2023

Small-Angle X-ray Scattering Unveils the Internal Structure of Lipid Nanoparticles

Lipid nanoparticles own a remarkable potential in nanomedicine, only partially disclosed. While the clinical use of liposomes and cationic lipid-nucleic acid complexes is well-established, liquid lipid nanoparticles (nanoemulsions), solid lipid nanoparticles, and nanostructured lipid carriers have even greater potential. However, they face obstacles in being used in clinics due to a lack of understanding about the molecular mechanisms controlling their drug loading and release, interactions with the biological environment (such as the protein corona), and shelf-life stability. To create effective drug delivery carriers and successfully translate bench research to clinical settings, it is crucial to have a thorough understanding of the internal structure of lipid nanoparticles. Through synchrotron small-angle X-ray scattering experiments, we determined the spatial distribution and internal structure of the nanoparticles' lipid, surfactant, and the water in them. The nanoparticles themselves have a barrel-like shape that consists of coplanar lipid platelets (specifically cetyl palmitate) that are partially covered by polysorbate 80 surfactant and retain a small amount of hydration water. Although the platelet structure was expected, the presence of surfactant molecules forming sticky patches between adjacent platelets challenges the classical core-shell model used to describe solid lipid nanoparticles. Additionally, the surfactant partially covers the water-nanoparticle interface, allowing certain lipid regions to come into direct contact with surrounding water. These structural features play a significant role in drug loading and release, biological fluid interaction, and nanoparticle stability, making these findings valuable for the rational design of lipid-based nanoparticles.

cond-mat.soft↗

arXiv2023

Failure precursors and failure mechanisms in hierarchically patterned paper sheets in tensile and creep loading

Quasi-brittle materials endowed with (statistically) self-similar hierarcical microstructures show distinct failure patterns that deviate from the standard scenario of damage accumulation followed by crack nucleation-and-growth. Here we study the failure of paper sheets with hierarchical slice patterns as well as non-hierarchical and unpatterned reference samples, considering both uncracked samples and samples containing a macroscopic crack. Failure is studied under displacement-controlled tensile loading as well as under creep conditions. Acoustic emission records and surface strain patterns are recorded alongside stress-strain and creep curves. The measurements demonstrate that hierarchical patterning efficiently mitigates against strain localization and crack propagation. In tensile loading, this results in a significantly increased residual strength of cracked samples. Under creep conditions, for a given range of lifetimes hierarchically patterned samples are found to sustain larger creep strains at higher stress levels; their creep curves show unusual behavior characterized by multiple creep rate minima due to the repeated arrest of emergent localization bands.

cond-mat.mtrl-sci↗

arXiv2022

Predicting Creep Failure by Machine Learning -- Which Features Matter?

Spatial and temporal features are studied with respect to their predictive value for failure time prediction in subcritical failure with machine learning (ML). Data are generated from simulations of a novel, brittle random fuse model (RFM), as well as elasto-plastic finite element simulations (FEM) of a stochastic plasticity model with damage, both models considering stochastic thermally activated damage/failure processes in disordered materials. Fuse networks are generated with hierarchical and nonhierarchical architectures. Random forests - a specific ML algorithm - allow us to measure the feature importance through a feature's average error reduction. RFM simulation data are found to become more predictable with increasing system size and temperature. Increasing the load or the scatter in local materials properties has the opposite effect. Damage accumulation in these models proceeds in stochastic avalanches, and statistical signatures such as avalanche rate or magnitude have been discussed in the literature as predictors of incipient failure. However, in the present study such features proved of no measurable use to the ML models, which mostly rely on global or local strain for prediction. This suggests the strain as viable quantity to monitor in future experimental studies as it is accessible via digital image correlation.

cond-mat.mtrl-sci↗

arXiv2022

Enhanced fault-tolerance in biomimetic hierarchical materials -- a simulation study

Hierarchical microstructures are often invoked to explain the high resilience and fracture toughness of biological materials such as bone and nacre. Biomimetic material models inspired by those structural arrangements face the obvious challenge of capturing their inherent multi-scale complexity, both in experiments and in simulations. To study the influence of hierarchical microstructural patterns in fracture behavior, we propose a large scale three-dimensional hierarchical beam-element simulation framework, where we generalize the constitutive behavior of Timoshenko beam elasticity and Maximum Distortion Energy Theory failure criteria to the complex case of hierarchical networks of approximately 5 million elements. We perform a statistical study of stress-strain relationships and fracture surface mophologies, and conclude that hierarchical systems are capable of arresting crack propagation, an ability that reduces their sensitivity to pre-existing damage and enhances their fault tolerance compared to reference generic fibrous materials.

cond-mat.mtrl-sci↗

arXiv2022

Automated Classification of General Movements in Infants Using a Two-stream Spatiotemporal Fusion Network

The assessment of general movements (GMs) in infants is a useful tool in the early diagnosis of neurodevelopmental disorders. However, its evaluation in clinical practice relies on visual inspection by experts, and an automated solution is eagerly awaited. Recently, video-based GMs classification has attracted attention, but this approach would be strongly affected by irrelevant information, such as background clutter in the video. Furthermore, for reliability, it is necessary to properly extract the spatiotemporal features of infants during GMs. In this study, we propose an automated GMs classification method, which consists of preprocessing networks that remove unnecessary background information from GMs videos and adjust the infant's body position, and a subsequent motion classification network based on a two-stream structure. The proposed method can efficiently extract the essential spatiotemporal features for GMs classification while preventing overfitting to irrelevant information for different recording environments. We validated the proposed method using videos obtained from 100 infants. The experimental results demonstrate that the proposed method outperforms several baseline models and the existing methods.

cs.CV↗

arXiv2021

Persistence of hierarchical network organization and emergent topologies in models of functional connectivity

Functional networks provide a topological description of activity patterns in the brain, as they stem from the propagation of neural activity on the underlying anatomical or structural network of synaptic connections. This latter is well known to be organized in hierarchical and modular way. While it is assumed that structural networks shape their functional counterparts, it is also hypothesized that alterations of brain dynamics come with transformations of functional connectivity. In this computational study, we introduce a novel methodology to monitor the persistence and breakdown of hierarchical order in functional networks, generated from computational models of activity spreading on both synthetic and real structural connectomes. We show that hierarchical connectivity appears in functional networks in a persistent way if the dynamics is set to be in the quasi-critical regime associated with optimal processing capabilities and normal brain function, while it breaks down in other (supercritical) dynamical regimes, often associated with pathological conditions. Our results offer important clues for the study of optimal neurocomputing architectures and processes, which are capable of controlling patterns of activity and information flow. We conclude that functional connectivity patterns achieve optimal balance between local specialized processing (i.e. segregation) and global integration by inheriting the hierarchical organization of the underlying structural architecture.

cond-mat.dis-nn↗

arXiv2020

Anomalous Lifshitz dimension in hierarchical networks of brain connectivity

The spectral dimension is a generalization of the Euclidean dimension and quantifies the propensity of a network to transmit and diffuse information. We show that, in hierarchical-modular network models of the brain, dynamics are anomalously slow and the spectral dimension is not defined. Inspired by Anderson localization in quantum systems, we relate the localization of neural activity - essential to embed brain functionality - to the network spectrum and to the existence of an anomalous "Lifshitz dimension". In a broader context, our results help shedding light on the relationship between structure and function in biological information-processing complex networks.

cond-mat.dis-nn↗

arXiv2020

Multilayer Structures of Graphene and Pt Nanoparticles -- a Multiscale Computational Study

We report results of a multiscale simulation study of multilayer structures consisting of graphene sheets with embedded Pt nanoparticles. Density functional theory is used to understand the energetics of Pt-graphene interfaces and provide reference data for the parameterization of a Pt-graphene interaction potential. Molecular dynamics simulations then provide the conformation and energetics of graphene sheets with embedded Pt nanoparticles of varying density, form and size. These results are interpreted using a continuum mechanical model of sheet deformation, and serve to parameterize a meso-scale Monte Carlo model to investigate the question under which conditions the free volume around the Pt nanoparticles forms a percolating cluster, such that the structures can be used in catalytic applications. We conclude with a discussion of potential applications of such multilayer structures.

physics.app-ph↗

arXiv2018

Graph theoretical approaches for the characterization of damage in hierarchical materials

We discuss the relevance of methods of graph theory for the study of damage in simple model materials described by the random fuse model. While such methods are not commonly used when dealing with regular random lattices, which mimic disordered but statistically homogeneous materials, they become relevant in materials with microstructures that exhibit complex multi-scale patterns. We specifically address the case of hierarchical materials, whose failure, due to an uncommon fracture mode, is not well described in terms of either damage percolation or crack nucleation-and-growth. We show that in these systems, incipient failure is accompanied by an increase in eigenvector localization and a drop in topological dimension. We propose these two novel indicators as possible candidates to monitor a system in the approach to failure. As such, they provide alternatives to monitoring changes in the precursory avalanche activity, which is often invoked as a candidate for failure prediction in materials which exhibit critical-like behavior at failure, but may not work in the context of hierarchical materials which exhibit scale-free avalanche statistics even very far from the critical load.

cond-mat.dis-nn↗

arXiv2017

Avalanche precursors of failure in hierarchical fuse networks

We study precursors of failure in hierarchical random fuse network models which can be considered as idealizations of hierarchical (bio)materials where fibrous assemblies are held together by multi-level (hierarchical) cross-links. When such structures are loaded towards failure, the patterns of precursory avalanche activity exhibit generic scale invariance: Irrespective of load, precursor activity is characterized by power-law avalanche size distributions without apparent cut-off, with power-law exponents that decrease continuously with increasing load. This failure behavior and the ensuing super-rough crack morphology differ significantly from the findings in non-hierarchical structures.

cond-mat.dis-nn↗

arXiv2017

Topological dimension tunes activity patterns in hierarchical modular network models

Connectivity patterns of relevance in neuroscience and systems biology can be encoded in hierarchical modular networks (HMNs). Moreover, recent studies highlight the role of hierarchical modular organization in shaping brain activity patterns, providing an excellent substrate to promote both the segregation and integration of neural information. Here we propose an extensive numerical analysis of the critical spreading rate (or "epidemic" threshold) --separating a phase with endemic persistent activity from one in which activity ceases-- on diverse HMNs. By employing analytical and computational techniques we determine the nature of such a threshold and scrutinize how it depends on general structural features of the underlying HMN. We critically discuss the extent to which current graph-spectral methods can be applied to predict the onset of spreading in HMNs, and we propose the network topological dimension as a relevant and unifying structural parameter, controlling the epidemic threshold.

cond-mat.dis-nn↗

arXiv2016

Complex synchronization patterns in the human connectome network

A major challenge in neuroscience is posed by the need for relating the emerging dynamical features of brain activity with the underlying modular structure of neural connections, hierarchically organized throughout several scales. The spontaneous emergence of coherence and synchronization across such scales is crucial to neural function, while its anomalies often relate to pathological conditions. Here we provide a numerical study of synchronization dynamics in the human connectome network. Our purpose is to provide a detailed characterization of the recently uncovered broad dynamic regime, interposed between order and disorder, which stems from the hierarchical modular organization of the human connectome. In this regime -similar in essence to a Griffiths phase- synchronization dynamics are trapped within metastable attractors of local coherence. Here we explore the role of noise, as an effective description of external perturbations, and discuss how its presence accounts for the ability of the system to escape intermittently from such attractors and explore complex dynamic repertoires of locally coherent states, in analogy with experimentally recorded patterns of cerebral activity.

q-bio.NC↗

arXiv2014

Griffiths phases and the stretching of criticality in brain networks

Hallmarks of criticality, such as power-laws and scale invariance, have been empirically found in cortical networks and it has been conjectured that operating at criticality entails functional advantages, such as optimal computational capabilities, memory, and large dynamical ranges. As critical behavior requires a high degree of fine tuning to emerge, some type of self-tuning mechanism needs to be invoked. Here we show that, taking into account the complex hierarchical-modular architecture of cortical networks, the singular critical point is replaced by an extended critical-like region which corresponds --in the jargon of statistical mechanics-- to a Griffiths phase. Using computational and analytical approaches, we find Griffiths phases in synthetic hierarchical networks and also in empirical brain networks such as the human connectome and the caenorhabditis elegans one. Stretched critical regions, stemming from structural disorder, yield enhanced functionality in a generic way, facilitating the task of self-organizing, adaptive, and evolutionary mechanisms selecting for criticality.

cond-mat.dis-nn↗

arXiv2014

Rounding of abrupt phase transitions in brain networks

The observation of critical-like behavior in cortical networks represents a major step forward in elucidating how the brain manages information. Understanding the origin and functionality of critical-like dynamics, as well as their robustness, is a major challenge in contemporary neuroscience. Here, we present an extensive numerical study of a family of simple dynamic models, which describe activity propagation in brain networks through the integration of different neighboring spiking potentials, mimicking basic neural interactions. The requirement of signal integration may lead to discontinuous phase transitions in networks that are well described by the mean field approximation, thus preventing the emergence of critical points in such systems. Here we show that criticality in the brain is instead robust, as a consequence of the hierarchical organization of the higher layers of cortical networks, which signals a departure from the mean-field paradigm. We show that, in finite-dimensional hierarchical networks, discontinuous phase transitions exhibit a rounding phenomenon and turn continuous for values of the topological dimension $D\le 2$, due to the presence of structural or topological disorder. Our results may prove significant in explaining the observation of traits of critical behavior in large-scale measurements of brain activity.

q-bio.NC↗