List of Abstracts

Colloidal systems are strongly affected by gravity over vertical length scales of a few millimeters. Hence, sedimentation-diffusion-equilibrium experiments, in which a colloidal suspension reaches equilibrium in a cuvette, cannot be understood without accounting for the gravitational field. The interplay between gravity and bulk phase behaviour is particularly strong in mixtures since each species couples differently to the gravitational field. As a direct consequence, the experimental results are difficult to interpret and are not well understood. In the first part of the talk I will show how to theoretically incorporate gravity into a microscopic bulk theory using a local equilibrium approximation, and illustrate how the coupling between gravity and bulk phase behaviour is responsible for a wealth of new phenomena in sedimentation experiments. These include the formation of stacking sequences with multiple layers of different bulk phases and a complex evolution of the stacking sequence by simply varying the height of the sample. Besides equilibrium phenomena, I will briefly discuss the effect of superadiabatic forces on the dynamics of colloidal systems. Superadiabatic forces are genuine non-equilibrium forces originated by the interparticle interac- tions. Superadiabatic forces can act on both the particle flow and the structure of the fluid. They are responsible of prominent physical phenomena such as viscosity, lane formation in oppositely driven mixtures, and colloidal migration in sheared flows 

We investigate by particle tracking the nucleation and growth process of a two-dimensional colloidal crystal, obtained by optically heating the solvent of a colloidal suspension, in which silica particles are strictly confined in a cell with thickness comparable to the particle size. Since any chance of particle convection is barred by the experimental geometry, particles move towards the laser spot solely due to thermophoresis, as confirmed by the analysis of the radial velocity profile compared to the applied temperature gradient, measured by fluorescence thermometry. The crystal formation can be accurately monitored introducing two order parameters gauging the local density and hexagonal orientational order of the growing structure. We show that crystallization occurs directly for sufficiently low initial area fractions. Conversely, when the particles surface coverage exceeds a minimal threshold, nucleation takes place with the formation of a dense liquid that eventually evolves in a crystallite. This threshold does not depend on the imposed thermal gradient, which controls however the lifetime of the dense liquid that precedes the formation of the ordered crystal. The late-growth kinetics of the optothermally-generated crystals displays a power-law behaviour, determined by the thermophoretic velocity radial profile.

The unprecedented combination of fluidity and polarity in recently discovered ferroelectric liquids crystals in their nematic phase (NF) [1,2] constitutes a unique research framework in which highly dipolar molecules have a complex and remarkable response to a very low external electric field. To have a privileged insight on such physical system in a precise confining environment, we produced, via laser ablation, microchannels buried in glass that connect, in a variety of shapes, gold electrodes. We filled the channels with RM734 [1,2] after having silanized their walls to provide planar anchoring [3] and studied the response to electric fields in the NF phase. We find that upon applying fields as low as E ≈ 0.5 V/mm, the NF phase orders with the nematic director following the path of the channels with no defect, indicating that polarization and E are always directed along the channels regardless of their shape, in analogy to the behavior of magnetic fields in ferromagnetic materials. As also confirmed by computer simulations, this is due to the accumulation of surface polarization charges on the channel walls, which cancel any component of E normal to the surfaces. As E is reversed, the polarity switches in two steps. (i) a highly distorted and defected director pattern develops in a time interval of the order of 10-100 ms, roughly proportional to 1/E and approximately independent from both the shape of the channels and the position within them. (ii) the completion of the reversal is obtained through the nucleation of a defect-free homogeneous NF volumes in the narrowest sections of the channel. The resulting defected/defect-free interfaces propagate along the channels as electrically charged solitons with a speed linearly depending on E [4]. 

References: 

[1] R. J. Mandle, S. J. Cowling, et al., Phys. Chem. Chem. Phys. 19, 11429–11435 (2017). 

[2] X. Chen, E. Korblova et al., Proc. Natl. Acad. Sci. U.S.A. 117, 14021–14031 (2020). 

[3] F. Caimi, G. Nava et al. Soft Matter 17, 8130–8139 (2021). 

[4] F. Caimi, G. Nava, et al. arXiv : 2210.00886 

Failure of materials under mechanical loading is a widespread phenomenon occurring at very different length scales ranging from the geological ones down to molecular scale. While the mechanisms leading to failure in crystalline solids are quite well understood and have wellidentified structural features [1], a unified picture of the microscopic events leading to yielding in amorphous solids is still missing. In this context, soft material such as colloidal suspensions, concentrated emulsions, clays, or foams, which promptly deform and eventually flow when subject even to relatively small loads, represent a very convenient model system to investigate the mechanical failure and the onset of plasticity in amorphous solids [2]. In this work, we exploit a custom shear-cell coupled to a standard bright-field microscope to follow the dynamics of embedded tracers in simple yield stress fluids when subjected to large oscillatory shear deformation [3]. Through a novel acquisition protocol and image analysis scheme [4], we can accurately monitor shear-induced structural rearrangements within the material and characterize the mesoscopic deformation field, while simultaneously measuring its rheological response. We find that the structural rearrangement activity exhibits an abrupt speed-up across the yielding transition, being heterogeneous in space and temporally intermittent below yielding, while it is more spatially homogenous and uniform in time above the transition. Our results, by establishing a firm connection between macroscopic mechanical response and microscopic shear-induced dynamics, represent a promising step forward in the understanding of the yielding transition of amorphous solids, and support its interpretation in terms of underlying non equilibrium phase transition [5]. 

[1] L. B. Chen, C. F. Zukoski, B. J. Ackerson, H. J. M. Hanley, G. C. Straty, J. Barker, and C. J. Glinka, Physical Review Letters, 1992, 69, 688 

[2] A.Nicolas, E. E. Ferrero, K. Martens and J.Barrat, Reviews of Modern Physics, 2018, 90, 045006 

[3] S. Villa, P. Edera, M. Brizioli, V. Trappe, F. Giavazzi and R. Cerbino, Frontiers in Physics, 2022, 905.

[4] P. Edera, M. Brizioli, G. Zanchetta, G. Petekidis, F. Giavazzi, and R. Cerbino, Soft Matter, 2021, 17(37), 8553-8566. 

[5] A. Liu and S. Nagel, Nature, 1998, 396(6706), 21-22. 

Complex plasma is a state of soft matter where microparticles are immersed in a weakly ionized gas. The particles acquire charges in the plasma that scales with the surface potential and the particle size and ranges to 103–104 elementary charges for micrometer sized particles. This provides a strong Coulomb interaction and therefore strong coupling between the microparticles and allows studying gaseous, liquid and crystalline states of the particle arrangements as well as transitions between them on the individual particle—the kinetic – level. The microparticles’ high mass – compared to electrons, ions and neutrals of the surrounding medium – on the one hand has the positive effect that the microparticle motion is slowed down and can be easily tracked with standard optical diagnostics. On the other hand, gravity starts to dominate over all other forces, and the microparticles sediment in the gravitational field. The particles can be levitated by a strong electric field in the sheath of an electrode system or through thermophoretic force in a strong temperature gradient. The first allows the formation of two-dimensional (2D) and compressed three-dimensional (3D) complex plasmas in the liquid and crystalline state - the plasma crystal - and the investigation of many interesting phenomena on the kinetic level like melting and crystallization, defect motion, or laser induced shear- melting. The latter allows the formation of large 3D structures in the bulk of the plasma under stress produced mainly through the gas convection within the strong temperature gradient necessary to counterbalance gravity. Only under microgravity conditions large 3D, homogeneous and isotropic complex plasma systems can be formed and investigated. Therefore, for a full understanding of complex plasmas, the complementary research under microgravity conditions, e.g. on parabolic flights, sounding rockets or the International Space Station is necessary. A longstanding program exists for this microgravity research. Especially the long-term projects PKE-Nefedov (2001–2005), PK-3 Plus (2006–2013) and PK-4 (operational since 2014) on the International Space Station (ISS) allow a deep understanding of the physics and opened up new research topics in the field, like electrorheological effects in complex plasmas, and phase separation in binary (regarding the microparticle component) mixtures. In this lecture I will present an overview of research in complex plasmas using highlight topics investigated in 2D and 3D systems. 

Magnetized nonneutral plasmas are an excellent environment to test nonlinear fluid phenomena experimentally and with highly-controllable conditions. Specifically, an electron plasma confined in an electro-magnetostatic (also called Penning-Malmberg) trap can be shown to be perfectly isomorphic in its transverse (cross-B field) dynamics to a non-viscous twodimensional fluid. In particular, we have developed experimental protocols to generate electron plasmas with tailored transverse density (fluid vorticity) distributions where we observe azimuthal velocity shear phenomena leading to the insurgence of Kelvin-Helmholtz (KH) wave modes, which can be the outcome of either self-organization processes or selected resonant excitation. We review here a series of experimental findings on these electron vortices, from the generation and growth of such coherent structures to instability and decay phenomena under free and forced evolution conditions, for which we have implemented fine-tuned control techniques based on multipolar rotating electric fields (equivalent to fluid strains) 

Being able to predict the failure of materials based on structural information is a fundamental issue with enormous practical and industrial relevance for the monitoring of devices and components. Thanks to recent advances in deep learning, accurate fracture predictions are becoming possible even for strongly disordered solids, but the sheer number of parameters used in the process renders a physical interpretation of the results impossible. Here we address this issue and use machine learning methods to predict the failure of simulated two-dimensional silica glasses from their initial undeformed structure. We show that our predictions can be transferred to samples with different shapes or sizes than those used in training, as well as to experimental images. 

Ref.: 

Font-Clos, F., Zanchi, M., Hiemer, S., Bonfanti, S., Guerra, R., Zaiser, M., & Zapperi, S. (2022). Predicting the failure of two-dimensional silica glasses. Nature communications, 13(1), 2820. 

Bonfanti, S., Guerra, R., Mondal, C., Procaccia, I., & Zapperi, S. (2019). Elementary plastic events in amorphous silica. Physical Review E, 100(6), 060602 

The landscape properties of high-dimensional constraint satisfaction problems (CSPs) can completely determine the type of configurations that can be efficiently sampled from their space of solutions. In recent years, empirical studies on the landscape of neural networks have shown that low-lying configurations are often found in complex connected structures, where zero-energy paths between pairs of distant solutions can be constructed. In this brief presentation, I will talk about investigating the connectivity of solutions in the negative perceptron, a linear neural network (NN) model and a basic prototype of a non-convex continuous CSP. After defining the context and giving a few hints on how learning algorithms are naturally biased towards configurations with local connectivity properties, I will introduce a novel analytical method for characterizing the typical energy barriers between groups of configurations sampled from the zero-temperature measure of the problem. We will see that, despite the overall non-convexity of the problem, solutions that are significant for the performance of NNs live in a convex core where the geodesic path among pairs remains strictly at zero-energy if the density of constraints α is below a certain critical value α∗ where the simple connectivity property breaks down. Finally, I will underline the analysis by providing some evidence of the correlation with generalization performance and robustness to noise. 

Everything flows: that’s where rheology comes from. Yet, the flow of solid matter can be more complex than that of a simple liquid. Or can it? Soft glassy materials such as those that populate our fridge may take forever to spontaneously spread on our toast, and luckily this process can be helped by mechanical stress, and/or heat. Is spreading butter any similar to melting it? Surprisingly, or maybe unsurprisingly, it turns out that what is so common in our daily experience is based on profound science that is not yet fully understood. In this talk, I will discuss experiments in which we investigate the microscopic signature of mechanical yielding in various soft materials, by simultaneously measuring their mechanical response and microscopic dynamics. I will show that these dynamics change qualitatively upon yielding, and that this change can be described by an Ising-like model formally identical to that describing thermodynamic phase changes in real gases. I will build upon this analogy to propose a unified state diagram for the yielding transition of soft colloids, and will discuss potential implications of this unified description.

By generalizing the notion of maximally random jammed (MRJ) state [1,2] to that of MRJline, we show [3] that it is reasonable to assume the most random branch of jammed states to undergo crowding in a way qualitatively similar to an equilibrium liquid. We then prove that, for hard-sphere systems, liquid-state theories can be successfully used to estimate the RCP density, when the latter is identified with the densest isostatic point, i. e. the densest among the MRJ states with z=6. Our finding is further enforced by the analysis of polydisperse systems. Either in the case of bidisperse and polydisperse hard spheres our prediction of the RCP density is in very good agreement with simulations, for a large values of size ratios and polydispersity. 

[1] S. Toquato, T. M. Truskett, and P. G. Debenedetti, Phys. Rev. Lett. 84, 2064 (2000) 

[2] S. Torquato and F. H. Stillinger, Reviews of Modern Physics 82, 2633 (2010) 

[3] C. Anzivino, M. Casiulis, T. Zhang, A. S. Moussa, S. Martiniani and A. Zaccone, J. Chem. Phys. 158, 044901 (2023) 

Surface properties influence bacterial adhesion, which is the first step towards colonization and biofilm formation. For implantable devices, biofilm-associated infections are the most common clinical complications, given their resistance against mechanical stress and antibiotics; therefore, it becomes of paramount importance the design and fabrication of surfaces able to prevent or reduce bacterial colonization. We investigated the effect of micrometer-scale surface wrinkled topographies subjected to fluid shear on the attachment and proliferation of motile and non-motile Pseudomonas aeruginosa bacterial strain. Specifically, sinusoidal (1D) patterns were fabricated by mechanical wrinkling of plasma-oxidized polydimethylsiloxane (PDMS) bilayers and contrasted with flat (F) surfaces. To describe the effect of topographies under fluid shear over bacterial initial attachment (up to 4h), F and 1D wrinkled topographies were incorporated into microfluidic devices, oriented according and in opposition to the direction of the fluid flow. Significantly, the combination of topography and flow is found to disrupt the spatial arrangement of bacteria, delaying proliferation for several hours and reducing it (by up to ~50\%) thereafter compared to flat (F) surfaces. Our findings suggest an effective framework to rationalize the impact of micrometer-scale topography, in fluidic conditions, and demonstrating that the judicious combination of surface patterning and fluid shear provides an effective strategy to delay and frustrate the early stages of bacterial proliferation. 

Collective motion—or flocking—is an emergent phenomenon that underlies many biological processes of relevance, from cellular migrations to animal group movements. Here we derive scaling relations for the fluctuations of the mean direction of motion and for the static density structure factor (which encodes static density fluctuations) in the presence of a homogeneous, small external field. This allows us to formulate two different and complementary criteria capable of detecting instances of directed motion exclusively from easily measurable dynamical and static signatures of the collective dynamics, without the need to detect correlations with environmental cues. The static one is informative in large enough systems, while the dynamical one requires large observation times to be effective. We believe these criteria may prove useful to detect or confirm the directed nature of collective motion in in vivo experimental observations, which are typically conducted in complex and not fully controlled environments. 

Sea robins are fishes with sensory appendages (“legs”) that they use to walk and to dig live prey from within the substrate. Their preadation strategy is so effective that they are often followed by other fish trying to steal their prey. I will discuss a set of behavioral experiments suggesting that these animals take advantage of sensory information from the water column as well as on sand. This “alternating” behavior, sampling sensory cues on substrates vs in the bulk of fluids, is well documented in rodents and dogs, suggesting that animals integrate both fluidborne and substrate cues into a multi-modal navigation strategy. What dictates alternation between these two sensorimotor modalities in the context of olfactory navigation ? I will switch from experiments to theory and show approximately optimal search strategies obtained through reinforcement learning techniques. I will show that efficient searchers do indeed alternate between sampling the substrate and the bulk. Far from the target, the searcher is most likely to rely on bulk odors as the search is information limited. The exact moment when the searcher switches between substrate and bulk is dictated by a marginality condition that stems from an emergent cast-and-surge behavior that optimal searchers undertake. 

Decades-long literature testifies to the success of statistical mechanics at clarifying fundamental aspects of deep learning. Yet the ultimate goal remains elusive: we lack a complete theoretical framework to predict practically relevant scores, such as the train and test accuracy, from knowledge of the training data. Huge simplifications arise in the infinite-width limit, where the number of units Nℓ in each hidden layer far exceeds the number P of training examples. This idealisation, however, blatantly departs from the reality of deep learning practice, where training sets are larger than the widths of the networks. Here, we show one way to overcome these limitations. The partition function for fully-connected architectures, which encodes information about the trained models, can be computed analytically with the toolset of statistical mechanics. The computation holds in the "thermodynamic limit'' where both Nℓ and P are large and their ratio αℓ = P/Nℓ, which vanishes in the infinite-width limit, is now finite and generic. This advance allows us to obtain (i) a closed formula for the generalisation error associated to a regression task in a one-hidden layer network with finite αℓ; (ii) an expression of the partition function (technically, via an "effective action'') for fully-connected architectures with arbitrary number of hidden layers, in terms of a finite number of degrees of freedom (technically, "order parameters''); (iii) a demonstration that the Gaussian processes arising in the infinite-width limit should be replaced by Student-t processes; (iv) a simple analytical criterion to predict, for a given training set, whether finite-width networks (with ReLU activations) achieve better test accuracy than infinite-width ones. 

The early development of aneuploidy from an accidental chromosome missegregation shows contrasting effects. On the one hand, it is associated with significant cellular stress and decreased fitness. On the other hand, it often carries a beneficial effect and provides a quick (but typically transient) solution to external stress. These apparently controversial trends emerge in several experimental contexts, particularly in the presence of duplicated chromosomes. However, we lack a mathematical evolutionary modeling framework that comprehensively captures these trends from the mutational dynamics and the trade-offs involved in the early stages of aneuploidy. Here, focusing on chromosome gains, we address this point by introducing a fitness model where a fitness cost of chromosome duplications is contrasted by a fitness advantage from the dosage of specific genes. The model successfully captures the experimentally measured probability of emergence of extra chromosomes in a laboratory evolution setup. Additionally, using phenotypic data collected in rich media, we explored the fitness landscape, finding evidence supporting the existence of a per-gene cost of extra chromosomes. Finally, we show that the substitution dynamics of our model, evaluated in the empirical fitness landscape, explains the relative abundance of duplicated chromosomes observed in yeast population genomics data. These findings lay a firm framework for the understanding of the establishment of newly duplicated chromosomes, providing testable quantitative predictions for future observations.

Chromatin is a biopolymer made of DNA and proteins that is found in eukaryotic cells. During the interphase, chromatin forms a complex tangle with characteristic organization and dynamics. The three-dimensional structure of chromatin is thought to play an important role in fundamental biological processes such as DNA replication, maintenance of genome integrity, and gene transcription. The dynamics, moreover, is far from trivial and features ATPconsuming processes like loop extrusion, a mechanic initiated by a protein complex called cohesin which strongly impacts the final structure of chromatin. These complex phenomena are modeled using a combination of computational techniques (Molecular Dynamics simulations, Monte Carlo) and theoretical methods from statistical mechanics; by studying the mean squared displacement of elements of the polymer during the time evolution, we describe the dynamics of this system by the scaling laws that characterize the motion. These theoretical studies, side by side with live-cell experiments, attempt to reveal the mechanistic details of the physical interactions within chromosomes and quantitatively characterize the chromatin organization, its temporal variability and its relationship with transcriptional activity, all questions which are currently widely unexplored.