Articles publicats en revistes (Física de la Matèria Condensada)

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    Role of connectivity anisotropies in the dynamics of cultured neuronal networks
    (Public Library of Science (PLoS), 2025-11-06) Houben, Akke Mats; García Ojalvo, Jordi; Soriano i Fradera, Jordi
    An inherent challenge in designing laboratory-grown, engineered living neuronal networks lies in predicting the dynamic repertoire of the resulting network and its sensitivity to experimental variables. To fill this gap, and inspired by recent experimental studies, we present a numerical model designed to replicate the anisotropies in connectivity introduced through engineering, characterize the emergent collective behavior of the neuronal network, and make predictions. The numerical model is developed to replicate experimental data, and subsequently used to quantify network dynamics in relation to tunable structural and dynamical parameters. These include the strength of imprinted anisotropies, synaptic noise, and average axon lengths. We show that the model successfully captures the behavior of engineered neuronal cultures, revealing a rich repertoire of activity patterns that are highly sensitive to connectivity architecture and noise levels. Specifically, the imprinted anisotropies promote modularity and high clustering coefficients, substantially reducing the pathological-like bursting of standard neuronal cultures, whereas noise and axonal length influence the variability in dynamical states and activity propagation velocities. Moreover, connectivity anisotropies significantly enhance the ability to reconstruct structural connectivity from activity data, an aspect that is important to understand the structure–function relationship in neuronal networks. Our work provides a robust in silico framework to assist experimentalists in the design of in vitro neuronal systems and in anticipating their outcomes. This predictive capability is particularly valuable in developing reliable brain-on-a-chip platforms and in exploring fundamental aspects of neural computation, including input–output relationships and information coding.
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    Citizen Science initiatives in climate-vulnerable neighbourhoods: a new transdisciplinary approach to tackle sustainability challenges?
    (Université Côte d'Azur, 2025-11-01) Bonhoure, Isabelle; Perelló, Josep, 1974-
    According to the Spanish State Meteorological Agency (AEMET), the summer of 2025 (June 1-August 31) was exceptionally warm across Spain, with an average temperature of 24.2°C on the mainland. This value is 2.1°C above the seasonal average for the reference period 1991-2020. It was the warmest summer since records began in 1961, surpassing the previous record set in 2022 by 0.1°C . Barcelona and its metropolitan area, located along the Catalan coast, have been particularly affected by rising summer temperatures, a situation exacerbated by the urban heat island effect (Ward et al., 2016; Zhao et al., 2018). On 16 August 2025, a temperature of 38.9°C was recorded at the Fabra Observatory , one of the city's four official meteorological stations. This value exceeded the previous August record of 38.8°C, registered in 2023. Furthermore, according to an international study (Barnes et al., 2025), Barcelona reported the third-highest number of heat-related deaths among European cities during the summer of 2025, surpassed only by Milan and Rome.
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    Preparation and Mechano-Functional Characterization of PEGylated Fibrin Hydrogels: Impact of Thrombin Concentration
    (2024-02-01) López-León, Clara F.; Planet Latorre, Ramon; Soriano i Fradera, Jordi
    Three-dimensional (3D) neuronal cultures grown in hydrogels are promising platforms to design brain-like neuronal networks in vitro. However, the optimal properties of such cultures must be tuned to ensure a hydrogel matrix sufficiently porous to promote healthy development but also sufficiently rigid for structural support. Such an optimization is difficult since it implies the exploration of different hydrogel compositions and, at the same time, a functional analysis to validate neuronal culture viability. To advance in this quest, here we present a combination of a rheological protocol and a network-based functional analysis to investigate PEGylated fibrin hydrogel networks with gradually higher stiffness, achieved by increasing the concentration of thrombin. We observed that moderate thrombin concentrations of 10% and 25% in volume shaped healthy networks, although the functional traits depended on the hydrogel stiffness, which was much higher for the latter concentration. Thrombin concentrations of 65% or higher led to networks that did not survive. Our results illustrate the difficulties and limitations in preparing 3D neuronal networks, and stress the importance of combining a mechano-structural characterization of a biomaterial with a functional one.
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    Role of nanoparticle shape on the critical size for quasi-uniform ordering: From spheres to cubes through superballs
    (Elsevier B.V., 2025-12-10) López-Vázquez, Iago; Serantes, David; Iglesias, Òscar
    The equilibrium states of single-domain magnetite nanoparticles (NPs) result from a subtle interplay between size, geometry, and magnetocrystalline anisotropy. In this work, we present a micromagnetic study of shape-controlled magnetite NPs using the superball geometry, which provides a continuous interpolation between spheres and cubes. By isolating the influence of shape, we analyze the transition from quasi-uniform (single-domain) to vortex-like states as particle size increases, revealing critical sizes that depend on the superball exponent

    . Our simulations show that faceted geometries promote the stabilization of vortex states at larger sizes, with marked distortions in the vortex core structure. The inclusion of cubic magnetocrystalline anisotropy, representative of magnetite, further lowers the critical size and introduces preferential alignment along the [111] easy axes. For isotropic shapes, the critical size for this transition increases with p, ranging from 49 nm for spheres to 56 nm for cubes, in agreement with experimental trends. In contrast, the presence of slight particle elongation increases the critical size and induces another preferential alignment direction. These results demonstrate that even small deviations from sphericity or aspect ratio significantly alter the magnetic ordering and stability of equilibrium magnetic states.

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    Modular architecture confers robustness to damage and facilitates recovery in spikingneural networks modeling in vitro neurons
    (Frontiers Media, 2025-06-19) Sumi, Takuma; Houben, Akke Mats; Yamamoto, Hideaki; Kato, Hideyuki; Katori, Yuichi; Soriano i Fradera, Jordi; Hirano-Iwata, Ayumi
    Impaired brain function is restored following injury through dynamic processes that involve synaptic plasticity. This restoration is supported by the brain’s inherent modular organization, which promotes functional separation and redundancy. However, it remains unclear how modular structure interacts with synaptic plasticity to define damage response and recovery efficiency. In this work, we numerically modeled the response and recovery to damage of a neuronal network in vitro bearing a modular structure. The simulations aimed at capturing experimental observations in cultured neurons with modular traits which were physically disconnected through a focal lesion. The damage reduced the frequency of spontaneous collective activity events in the cultures, which recovered to pre-damage levels within 24 h. We rationalized this recovery in the numerical simulations by considering a plasticity mechanism based on spike-timing-dependent plasticity, a form of synaptic plasticity that modifies synaptic strength based on the relative timing of pre- and postsynaptic spikes. We observed that the in silico numerical model effectively captured the decline and subsequent recovery of spontaneous activity following the injury. The model supports that the combination of modularity and plasticity confers robustness to the damaged neuronal network by preventing the total loss of spontaneous network-wide activity and facilitating recovery. Additionally, by using our model within the reservoir computing framework, we show that information representation in the neuronal network improves with the recovery of network-wide activity.
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    Variability vs. phenotype: Multimodal analysis of Dravet syndrome brain organoids powered by deep learning
    (Elsevier, 2025-11-21) Lao, Oscar; Acosta, Sandra; Turpin, Isabel; Modrego, Adriana; Martí Sarrias, Andrea; Ortega Gascó, Alba; Haeb, Anna-Christina; García González, Laura; Soriano i Fradera, Jordi; Ruiz, Núria; Peñuelas Haro, Irene; Espinet, Elisa; Tornero, Daniel
    Dravet syndrome (DS) is a developmental epileptic encephalopathy (DEE) driven by pathogenic variants in the SCN1A gene. Brain organoids (BOs) have emerged as reliable models for neurodevelopmental genetic disorders, reproducing human brain developmental milestones and rising as a promising drug testing tool. Here, we determined the underlying molecular DS pathophysiology affecting neuronal connectivity, revealing an early onset excitatory-inhibitory imbalance in maturing DS organoid circuitry. However, neuronal circuitry modeling in BOs remains hampered by the notorious inter- and intra-organoid variability. Thus, leveraging deep learning (DL), we developed ImPheNet, a predictive tool grounded in BO live imaging datasets, to overcome the limitations of the intrinsic BOs variability. ImPheNet accurately classified healthy and DS phenotypes at early onset stages, revealing differences between genotypes and upon antiseizure drug exposure. Altogether, our DL-predictive live imaging strategy, ImPheNet, emerges as a powerful tool to accelerate DEEs research and advance toward treatment discovery in a time- and cost-efficient manner.
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    Optimal navigability of weighted human brain connectomes in physical space
    (Elsevier B.V., 2024-08-15) Barjuan Ballabriga, Laia; Soriano i Fradera, Jordi; Serrano Moral, Ma. Ángeles (María Ángeles)
    Communication protocols in the brain connectome describe how to transfer information from one region to another. Typically, these protocols hinge on either the spatial distances between brain regions or the intensity of their connections. Yet, none of them combine both factors to achieve optimal efficiency. Here, we introduce a continuous spectrum of decentralized routing strategies that integrates link weights and the spatial embedding of connectomes to route signal transmission. We implemented the protocols on connectomes from individuals in two cohorts and on group-representative connectomes designed to capture weighted connectivity properties. We identified an intermediate domain of routing strategies, a sweet spot, where navigation achieves maximum communication efficiency at low transmission cost. This phenomenon is robust and independent of the particular configuration of weights. Our findings suggest an interplay between the intensity of neural connections and their topology and geometry that amplifies communicability, where weights play the role of noise in a stochastic resonance phenomenon. Such enhancement may support more effective responses to external and internal stimuli, underscoring the intricate diversity of brain functions.
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    Biblioteques i universitats públiques: un camí comú cap a una ciència més ciutadana
    (Universitat de Barcelona, 2025-11-01) Bonhoure, Isabelle; Perelló, Josep, 1974-
    Les biblioteques públiques estan vivint un procés de transformació per adaptar-se a l’esperit dels temps: desenvolupen nous rols de col·laboració amb les universitats, apropen la recerca científica a la comunitat i esdevenen espais segurs, de ben-estar social i ambiental per als ciutadans.
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    Analyzing user activity on Twitter during long-lasting crisis events: a case study of the Covid-19 crisis in Spain
    (Springer Nature, 2024-03-29) Esquirol Juanola, Bernat; Prignano, Luce; Díaz Guilera, Albert; Cozzo, Emanuele
    A pandemic crisis like the Covid-19 outbreak is a complex event, involving numerous aspects of the social life on multiple temporal scales. Focusing on the Spanish Twittersphere, we characterized users' activity behavior across the diferent phases of the Covid-19 frst wave. Firstly, we analyzed a sample of timelines of diferent classes of users from the Spanish Twittersphere in terms of their propensity to produce new information or to amplify information produced by others. Secondly, by performing stepwise segmented regression analysis and Bayesian switchpoint analysis, we looked for a possible behavioral footprint of the crisis in the statistics of users’ activity. We observed that generic Spanish Twitter users and journalists experienced an abrupt increment of their tweeting activity between March 9 and 14, in coincidence with control measures being announced by regional and state-level authorities. However, they displayed a stable proportion of retweets before and after the switching point. On the contrary, politicians represented an exception, being the only class of users not experimenting this abrupt change and following a completely endogenous dynamics determined by institutional agenda. On the one hand, they did not increment their overall activity, displaying instead a slight decrease. On the other hand, in times of crisis, politicians tended to strengthen their propensity to amplify information rather than produce it.
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    Explorative pedestrian mobility geolocated data from a citizen science experiment in a neighbourhood
    (Springer Nature, 2025-06-19) Larroya, Ferran; Perelló, Josep, 1974-; Paez i Blanch, Roger; Valtchanova, Manuela Mihailova
    Pedestrian geolocated data are key to a better understanding of micro-mobility within a neighbourhood. These data can bring new insights into walkability and livability in the context of urban sustainability. However, pedestrian open data are scarce and often lack a context for their transformation into actionable knowledge in a neighbourhood. Citizen science and public involvement practices are powerful instruments for obtaining these data and take a community-centred placemaking approach. The study shares some 3 000 geolocated records corresponding to 19 unique trajectories made and recorded by groups of participants from three distinct communities (72 participants and 19 groups) in a relatively small neighbourhood. The groups explored the neighbourhood through a number of actions and chose different places to stop and perform various social and festive activities. The study shares not only raw data but also processed records with specific filtering and processing to facilitate and accelerate data usage. Citizen science practices and the data-collection protocols involved are reported in order to offer a complete perspective of the research undertaken jointly with an assessment of how community-centred placemaking and operative mapping are incorporated into local urban transformation actions.
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    Emergence of dissipation and hysteresis form interactions among reversible, nondissipative units: The case of fluid-fluid interfaces.
    (American Physical Society, 2024-06-03) Holtzman, Ran; Dentz, Marco; Moura, Marcel; Chubynsky, Mykyta; Planet Latorre, Ramon; Ortín, Jordi, 1959-
    We examine the nonequilibrium nature of two-phase fluid displacements in a quasi-twodimensional medium (a model open fracture) in the presence of localized constrictions (“defects”) from a theoretical and numerical standpoint. Our analysis predicts the capillary energy dissipated in abrupt interfacial displacements (jumps) across defects, and relates it to the corresponding hysteresis cycle, e.g., in pressure-saturation. We distinguish between “weak” (reversible interface displacement, exhibiting no hysteresis and dissipation) and “strong” (irreversible) defects. We expose the emergence of dissipation and irreversibility caused by spatial interactions, mediated by interfacial tension, among otherwise weak defects. We exemplify this cooperative behavior for a pair of weak defects and establish a critical separation distance, analytically and numerically, verified by a proof-of-concept experiment.
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    Mechanisms of interface jumps, pinning and hysteresis during cyclic fluid displacements in an isolated pore
    (Elsevier, 2025-10-15) Nepal, Animesh; Hidalgo, Juan J.; Ortín, Jordi, 1959-; Lunati, Ivan; Dentz, Marco
    Hypothesis Quasi-static displacements of one immiscible fluid by another in a single pore can lead to interface jumps, pinning and capillary hysteresis, depending on the pore dimensions. It is expected that there is a critical pore configuration for which the interface becomes unstable and an interface jump is triggered. These processes are at the origin of hysteresis in porous media and control macroscopic two-phase fluid displacements. Experiments and theory We conduct quasi-static imbibition and drainage experiments and detailed numerical simulations in three and two-dimensional pores, represented by capillaries of different radii that are joined by a conical section (ink-bottle). A theoretical model for the interface is derived based on pressure balance that captures the full spectrum of possible interface behaviors. Findings Depending on the slope of the conical section, we observe a range of interfacial behaviors, including capillary jumps and interface pinning during both imbibition and drainage, which give rise to capillary hysteresis, that is, history dependence of the interface position. We identify a critical pore configuration for the occurrence of interface jumps and hysteresis, which depends on surface tension and contact angle.
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    Multicaloric effects and magnetostructural coupling in the Cr2Ge2Te6 van der Waals crystal
    (Elsevier, 2025-04-22) Abadia-Huguet, Aleix; Mendive Tapia, Eduardo; Stern Taulats, Enric; Planes Vila, Antoni; Eggert, Benedikt; Wende, Heiko; Acet, Mehmet; Sturza, Mihai-Ionut; Kohlmann, Holger; Costache, Marius V.; Mañosa, Lluís
    Materials with significant coupling between magnetism and their crystal structure are prone to exhibit multicaloric effects, which offer a novel approach to addressing the bottlenecks of ecologic solid-state refrigeration by optimizing the interplay of multiple driving fields. Here we uncover the multicaloric properties of CrGeTe, establishing ferromagnetic van der Waals (vdW) crystals, famous for their spintronics applications, as a previously unrecognized class of multicaloric materials. By combining magnetization measurements with an ab initio disordered local moment theory, we report, for the first time, pronounced barocaloric and multicaloric effects induced by the application of magnetic fields and hydrostatic pressure around CrGeTe’s ferromagnetic phase transition. Our experimental and ab initio analysis quantifies the underlying magnetostructural coupling in this material, which accounts for approximately 25% of the total multicaloric entropy change. Significant multicaloric effects are expected to be found in other vdW ferromagnets with strong magnetostructural coupling.
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    First Order Alignment Transition in an Interfaced Active Nematic
    (American Physical Society, 2024-06-15) Bantysh, Olga; Martinez-Prat, Berta; Nambisan, Jyothishraj; Fernandez de las Nieves, Alberto; Sagués i Mestre, Francesc; Ignés i Mullol, Jordi
    We investigate experimentally the dynamic phase transition of a two-dimensional active nematic layer interfaced with a passive liquid crystal. Under a temperature ramp that leads to the transition of the passive liquid into a highly anisotropic lamellar smectic-A phase, and in the presence of a magnetic field, the coupled active nematic reorganizes its flow and orientational patterns from the turbulent into a quasilaminar regime aligned perpendicularly to the field. Remarkably, while the phase transition of the passive fluid is known to be continuous, or second order, our observations reveal intermittent dynamics of the order parameter and the coexistence of aligned and turbulent regions in the active nematic, a signature of discontinuous, or first order, phase transitions, similar to what is known to occur in relation to flocking in dry active matter. Our results suggest that alignment transitions in active systems are intrinsically discontinuous, regardless of the symmetry and momentum-damping mechanisms.
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    A new era in brain drug delivery: Integrating multivalency and computational optimisation for blood–brain barrier permeation
    (Elsevier, 2025-06-14) Porro, Giulia Maria; Basile, Marco; Xie, Zhendong; Tuveri, Gianmarco; Battaglia, Giuseppe; Carvalho Ferreira Lopes, Cátia Daniela
    Efficient drug delivery across the blood–brain barrier (BBB) remains a significant obstacle in treating central nervous system (CNS) disorders. This review provides an in-depth analysis of the structural and molecular mechanisms underlying BBB integrity and its functional properties. We detail the role of key cellular and molecular components that regulate selective molecular transport across the barrier, alongside a description of the current therapeutic approaches for brain drug delivery, including those leveraging receptor-mediated transcytosis. Emphasis is placed on multivalency-based strategies that enhance the specificity of nanoparticle targeting and improve transport efficacy across the BBB. Additionally, we discuss the added value of integrating mathematical and computational models with experimental validation for accelerating BBB-targeted delivery systems optimisation.
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    Discounting the Distant Future: What Do Historical Bond Prices Imply about the Long-Term Discount Rate?
    (MDPI, 2024-03-01) Farmer, J. Doyne; Geanakoplos, John; Richiardi, Matteo G.; Montero Matellanes, M. Mikel; Perelló, Josep, 1974-; Masoliver, Jaume, 1951-
    We present a thorough empirical study on real interest rates by also including risk aversion through the introduction of the market price of risk. From the viewpoint of complex systems science and its multidisciplinary approach, we use the theory of bond pricing to study the long-term discount rate to estimate the rate when taking historical US and UK data, and to further contribute to the discussion about the urgency of climate action in the context of environmental economics and stochastic methods. Century-long historical records of 3-month bonds, 10-year bonds, and inflation allow us to estimate real interest rates for the UK and the US. Real interest rates are negative about a third of the time and the real yield curves are inverted more than a third of the time, sometimes by substantial amounts. This rules out most of the standard bond-pricing models, which are designed for nominal rates that are assumed to be positive. We, therefore, use the Ornstein–Uhlenbeck model, which allows negative rates and gives a good match to inversions of the yield curve. We derive the discount function using the method of Fourier transforms and fit it to the historical data. The estimated long-term discount rate is 1.7% for the UK and 2.2% for the US. The value of 1.4% used by Stern is less than a standard deviation from our estimated long-run return rate for the UK, and less than two standard deviations of the estimated value for the US. All of this once more reinforces the need for immediate and substantial spending to combat climate change.
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    Characterizing the Hard and Soft Nanoparticle-Protein Corona with Multilayer Adsorption
    (Frontiers Media, 2025-01-17) Vilanova, O.; Martinez-Serra, Alberto; Monopoli, Marco P.; Franzese, Giancarlo
    Nanoparticles (NPs) in contact with biological fluid adsorb biomolecules into a corona. This corona comprises proteins that strongly bind to the NP (hard corona) and loosely bound proteins (soft corona) that dynamically exchange with the surrounding solution. While the kinetics of hard corona formation is relatively well understood, thanks to experiments and robust simulation models, the experimental characterization and simulation of the soft corona present a more significant challenge. Here, we review the current state of the art in soft corona characterization and introduce a novel open-source computational model to simulate its dynamic behavior, for which we provide the documentation. We focus on the case of transferrin (Tf) interacting with polystyrene NPs as an illustrative example, demonstrating how this model captures the complexities of the soft corona and offers deeper insights into its structure and behavior. We show that the soft corona is dominated by a glassy evolution that we relate to crowding effects. This work advances our understanding of the soft corona, bridging experimental limitations with improved simulation techniques.
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    Stacking correlation length in single-stranded DNA
    (Oxford University Press, 2024-10-29) Viader-Godoy, Xavier; Mañosas Castejón, María; Ritort Farran, Fèlix
    Base stacking is crucial in nucleic acid stabilization, from DNA duplex hybridization to single-stranded DNA (ssDNA) protein binding. While stacking energies are tiny in ssDNA, they are inextricably mixed with hydrogen bonding in DNA base pairing, making their measurement challenging. We conduct unzipping experiments with optical tweezers of short poly-purine (dA and alternating dG and dA) sequences of 20–40 bases. We introduce a helix-coil model of the stacking–unstacking transition that includes finite length effects and reproduces the force-extension curves. Fitting the model to the experimental data, we derive the stacking energy per base, finding the salt-independent value kcal/mol for poly-dA and kcal/mol for poly-dGdA. Stacking in these polymeric sequences is predominantly cooperative with a correlation length of ∼4 bases at zero force . The correlation length reaches a maximum of ∼10 and 5 bases at the stacking–unstacking transition force of ∼10 and 20 pN for poly-dA and poly-dGdA, respectively. The salt dependencies of the cooperativity parameter in ssDNA and the energy of DNA hybridization are in agreement, suggesting that double-helix stability is primarily due to stacking. Analysis of poly-rA and poly-rC RNA sequences shows a larger stacking stability but a lower stacking correlation length of ∼2 bases.
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    Integrated information decomposition unveils major structural traits of in silico and in vitro neuronal networks
    (American Institute of Physics (AIP), 2024-05-01) Menesse, Gustavo; Houben, Akke Mats; Soriano i Fradera, Jordi; Torres, Joaquín J.
    The properties of complex networked systems arise from the interplay between the dynamics of their elements and the underlying topology. Thus, to understand their behavior, it is crucial to convene as much information as possible about their topological organization. However, in large systems, such as neuronal networks, the reconstruction of such topology is usually carried out from the information encoded in the dynamics on the network, such as spike train time series, and by measuring the transfer entropy between system elements. The topological  information recovered by these methods does not necessarily capture the connectivity layout, but rather the causal flow of information between elements. New theoretical frameworks, such as Integrated Information Decomposition (Phi-ID), allow one to explore the modes in which information can flow between parts of a system, opening a rich landscape of interactions between network topology, dynamics, and information. Here, we apply Phi-ID on in silico and in vitro data to decompose the usual transfer entropy measure into different modes of information transfer, namely, synergistic, redundant, or unique. We demonstrate that the unique information transfer is the most relevant measure to uncover structural topological details from network activity data, while redundant information only introduces residual information for this application. Although the retrieved network connectivity is still functional, it captures more details of the underlying structural topology by avoiding to take into account emergent high-order interactions and information redundancy between elements, which are important for the functional behavior, but mask the detection of direct simple interactions between elements constituted by the structural network topology.
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    Unveiling the crystal and magnetic texture of iron oxide nanoflowers†
    (Royal Society of Chemistry, 2024-01-03) Moya Álvarez, Carlos; Escoda I Torroella, Mariona; Rodríguez Álvarez, Javier; Figueroa Garcia, Adriana Isabel; García, Íker; Batalla Ferrer-Vidal, Inés; Gallo Cordova, Álvaro; Morales, Maria del Puerto; Aballe, Lucía; Fraile Rodríguez, Arantxa; Labarta, Amílcar; Batlle Gelabert, Xavier
    Iron oxide nanoflowers (IONF) are densely packed multi-core aggregates known for their high saturation magnetization and initial susceptibility, as well as low remanence and coercive field. This study reports on how the local magnetic texture originating at the crystalline correlations among the cores determines the special magnetic properties of individual IONF over a wide size range from 40 to 400 nm. Regardless of this significant size variation in the aggregates, all samples exhibit a consistent crystalline correlation that extends well beyond the IONF cores. Furthermore, a nearly zero remnant magnetization, together with the presence of a persistently blocked state, and almost temperature-independent field-cooled magnetization, support the existence of a 3D magnetic texture throughout the IONF. This is confirmed by magnetic transmission X-ray microscopy images of tens of individual IONF, showing, in all cases, a nearly demagnetized state caused by the vorticity of the magnetic texture. Micromagnetic simulations agree well with these experimental findings, showing that the interplay between the inter-core direct exchange coupling and the demagnetizing field is responsible for the highly vortex-like spin configuration that stabilizes at low magnetic fields and appears to have partial topological protection. Overall, this comprehensive study provides valuable insights into the impact of crystalline texture on the magnetic properties of IONF over a wide size range, offering a deeper understanding of their potential applications in fields such as biomedicine and water remediation