Laboratoire de Mécanique Paris-Saclay

Catalyst systems populated by high-density single atoms are crucial for improving catalytic activity and selectivity, which can potentially maximize the industrial prospects of heterogeneous single-atom catalysts (SACs). However, achieving high-loading SACs with metal contents above 10 wt% remains challenging. Here we describe a general negative pressure annealing strategy to fabricate ultrahigh-loading SACs with metal contents up to 27.3-44.8 wt% for 13 different metals on a typical carbon nitride matrix. Furthermore, our approach enables the synthesis of high-entropy single-atom catalysts (HESACs) that exhibit the coexistence of multiple metal single atoms with high metal contents. In-situ aberration-corrected HAADF-STEM (AC-STEM) combined with ex-situ X-ray absorption fine structure (XAFS) demonstrate that the negative pressure annealing treatment accelerates the removal of anionic ligand in metal precursors and boosts the bonding of metal species with N defective sites, enabling the formation of dense N-coordinated metal sites. Increasing metal loading on a platinum (Pt) SAC to 41.8 wt% significantly enhances the activity of propane oxidation towards liquid products, including acetone, methanol, and acetic acid et al. This work presents a straightforward and universal approach for achieving many low-cost and high-density SACs for efficient catalytic transformations.

Abstract For a material exhibiting hysteresis such as quasi‐brittle materials, it is natural to consider that hysteresis and fatigue are related to each other. One shows in the present work that damage, from the continuum damage mechanics point of view, may be seen as the link between both phenomena. One attempts, hence, to set up a unified modelling of hysteresis and damage. Numerical examples are given for concrete and validate the proposed model of internal sliding and friction coupled with damage. The problem of a proper phenomenological modelling of the micro‐defects closure effect leading to a dissymmetric tension/compression response and to stiffness recovery in compression is also addressed. Cyclic and fatigue applications are in mind, and also random fatigue and seismic responses. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract As an emerging frontier, dual‐atom catalysts (DACs) have sparked broad interest in energy catalysis, however the undesired thermal atomic migration during synthesis process pose significant challenge in enabling further applications. Herein, an interfacial cladding strategy is reported to construct monodispersed dual‐atom metal sites (metal = Fe, Cu, or Ir), derived from metal dimer molecule functionalized metal‐organic frameworks. First, metal dimer molecule is immobilized at the surface of cubic ZIF‐8 by the interfacial cladding of polydopamine, thus preventing the potentially thermal migration of metal atoms during pyrolysis. Then, the paired metal atoms are anchored onto a hollow carbon nanocage and achieve nitrogen coordinated dual‐atom metal sites after annealing at 900 °C. Representatively, the resultant dual Fe catalysts exhibit remarkable activity for electrocatalytic oxygen reduction reaction with half‐wave potential of 0.951 and 0.816 V in alkaline and acidic media, respectively. The findings open up an avenue for the rational design of dual‐atom catalysts.

Abstract Contact electrification and triboelectric charging are areas of intense research. Despite their low ability to accept or donate electrons, polymer insulator based triboelectric nanogenerators have emerged as highly efficient mechanical‐to‐electrical conversion devices. Here, it is reviewed the structure–property–performance of polymer insulators in triboelectric nanogenerators and focus on tools that can be used to directly enhance charge generation, via altering a polymer's mechanical, thermal, chemical, and topographical properties. In addition to the discussion of these fundamental properties, the use of additives to locally manipulate the polymer surface structure is discussed. The link between each property and the underlying charging mechanism is discussed, in the context of both increasing surface charge and predicting the polarity of surface charge, and pathways to engineer triboelectric charging are highlighted. Key questions facing the field surrounding data reporting, the role of water, and synergy between mass, electron, and ion transfer mechanisms are highlighted with aspirational goals of a holistic model for triboelectric charging proposed.

Abstract Electrocatalytic acetylene semi-hydrogenation to ethylene powered by renewable electricity represents a sustainable pathway, but the inadequate current density and single-pass yield greatly impedes the production efficiency and industrial application. Herein, we develop a F-modified Cu catalyst that shows an industrial partial current density up to 0.76 A cm −2 with an ethylene Faradic efficiency surpass 90%, and the maximum single-pass yield reaches a notable 78.5%. Furthermore, the Cu-F showcase the capability to directly convert acetylene into polymer-grade ethylene in a tandem flow cell, almost no acetylene residual in the production. Combined characterizations and calculations reveal that the Cu δ+ (near fluorine) enhances the water dissociation, and the generated active hydrogen are immediately transferred to Cu 0 (away from fluorine) and react with the locally adsorbed acetylene. Therefore, the hydrogen evolution reaction is surpassed and the overall acetylene semi-hydrogenation performance is boosted. Our findings provide new opportunity towards rational design of catalysts for large-scale electrosynthesis of ethylene and other important industrial raw.

Abstract Dielectric capacitors are fundamental energy storage components in electronics and electric power systems due to their unique ultrahigh power density. However, their relatively low energy storage density is a long‐standing challenge which greatly limits their practical application range. Chitosan (CS) and montmorillonite (MMT) are two kinds of materials that exist abundantly on the earth with natural surface charges. The positively charged CS and negatively charged MMT can be self‐assembled into the typical sandwich‐structured CS/MMT/CS 2D structure through an electrostatic attraction. Loading these surface‐charged sandwich‐structured nanosheets into poly(vinylidene fluoride)‐based composite with a weight fraction as tiny as 0.3 wt.%, an ultrahigh energy storage density of 32.5 J cm −3 accompanied with a high efficiency of 64% are concurrently achieved with a very low cost and scalable process. Guided by finite element simulation, it is revealed that a number of electric potential wells that exist in the charged sandwich nanosheets impede the acceleration of internal charges and hinder the growth of electrical trees. The results offer a novel paradigm for exploring ultrahigh energy storage density capacitors in an economical way.

Abstract Simultaneously optimizing elementary steps, such as water dissociation, hydroxyl transferring, and hydrogen combination, is crucial yet challenging for achieving efficient hydrogen evolution reaction (HER) in alkaline media. Herein, Ru single atom‐doped WO 2 nanoparticles with atomically dispersed Ru–W pair sites (Ru–W/WO 2 ‐800) are developed using a crystalline lattice‐confined strategy, aiming to gain efficient alkaline HER. It is found that Ru–W/WO 2 ‐800 exhibits remarkable HER activity, characterized by a low overpotential (11 mV at 10 mA cm −2 ), notable mass activity (5863 mA mg −1 Ru at 50 mV), and robust stability (500 h at 250 mA cm −2 ). The highly efficient activity of Ru–W/WO 2 ‐800 is attributed to the synergistic effect of Ru–W sites through ensemble catalysis. Specifically, the W sites expedite rapid hydroxyl transferring and water dissociation, while the Ru sites accelerate the hydrogen combination process, synergistically facilitating the HER activity. This study opens a promising pathway for tailoring the coordination environment of atomic‐scale catalysts to achieve efficient electro‐catalysis.

Because creep–fatigue is mainly studied in uniaxial tension, it is shown here how to proceed to perform both experiments and calculations under multiaxial loading and when the temperature varies both in time and space. The constitutive equations used are those of elasto‐visco‐plasticity coupled or not, to damage, with isotropic and kinematic hardening. It is shown that the unified damage law first proposed for ductile failure and then for fatigue may also be applied to multiaxial creep–fatigue interactions with a new expression for the damage threshold. The procedure for the identification of material parameters is described in detail. Finally, it is shown that the uncoupled calculation procedure, where damage is calculated as a post‐processing of an elasto‐visco‐plastic computation, gives satisfactory results in comparison to the fully coupled analysis; the latter being more accurate but very expensive in computer time.

Abstract This paper is a review on distributed optic fiber sensing for structural health monitoring applications, with a deeper focus on technologies relying on the Rayleigh backscattering phenomenon. It addresses the basic physical principles which are involved, the implementation and instrumentation of the measurement techniques, as well as recent practical applications, current performance, and remaining challenges. Being written at an elementary level and integrating relevant theoretical and technical details, we hope the document can be useful for researchers and engineers looking for an up-to-date overview on a field which currently undergoes significant development and increasing attractiveness, in particular for damage tracking in complex mechanical structures.

Multilayer pantographic metamaterials, in short, pantographic blocks, have shown peculiar mechanical behavior, especially when their constitutive hinges are revolving (i.e., perfect) joints. The pantographic block, which is the subject of the present paper, has been printed using a Powder Bed Fusion technology and its hinges may be modeled as perfect ones. In the reported in situ 3-point flexural test, the predictions obtained by second gradient models for its mechanical response are shown to be experimentally consistent thanks to measurements via Digital Volume Correlation. The deformation applied by the upper central support is almost entirely shielded by the pantographic block, namely, the specimen barely crosses through the reference bottom plane defined by the lower lateral supports, even when subjected to very large deformations. The mathematical model employed herein captures this observation in terms of a nonlinear ‘arching’ effect activated in the beams of the pantographic structure, provided elastic locking is introduced to prevent pantographic zero-energy modes.

The archetypical antiferroelectric, PbZrO3, is currently attracting a lot of interest, but no consensus can be clearly established on the nature of its ground state as well as on the influence of external stimuli over its physical properties. Here, the antiferroelectric state of 45-nm-thick epitaxial thin films of PbZrO3 is established by observing the characteristic structural periodicity of antiparallel dipoles at the atomic scale, combined with clear double hysteresis of the polarization-electric field response related to antiferroelectric–to–ferroelectric phase transitions. Surprisingly, while the antiferroelectric state is identified as the ground state, temperature-dependent measurements show that a transition to a ferroelectric-like state appears in a large temperature window (100 K). Atomistic simulations further confirm the existence, and provides the origin, of such ferroelectric state in the films. Electric-field-induced ferroelectric transitions are also detected by the divergence of the piezoresponse force microscopy response. Using this technique, we further reveal the signature of a ferroelectric ground state for 4-nm-thick PbZrO3 films. Compared with bulk crystals, these results suggest a more complex competition between ferroelectric and antiferroelectric phases in epitaxial thin films of PbZrO3.

Abstract This article proposes a new approach to train physics‐augmented neural networks with observable data to represent mechanical constitutive laws. To train the neural network and learn thermodynamics potentials, the proposed method does not rely on strain‐stress or strain‐free energy pairs but needs only partial strain or displacement measurements inside the structure. The neural network is trained thanks to an unsupervised procedure in which the modified constitutive relation error (mCRE) is minimized. The mCRE functional provides a bias‐aware data assimilation framework with a rich physical sense as the constitutive relation error (CRE) part can be interpreted as a modeling error continuously defined over the structure, and can be used as a prediction quality in the inference phase. This article also extends previous works on the mCRE by introducing a new minimization procedure in the case of nonlinear state laws. As typical structural health monitoring applications may require that the neural networks should be trained online, an important focus is thus made on automatic and adaptive tuning of sensitive hyperparameters (learning rate, weighting between losses, number of epochs and initialization). It is shown that when the training database is rich enough with respect to the loading cases, the proposed method achieves remarkable performance regarding the quality of the learned model, noise robustness, and low sensitivity to user‐defined hyperparameters. The method is evaluated on two test cases: a non‐quadratic potential in the small strain regime with synthetic optic fiber measurements, and a Mooney–Rivlin model in the hyperelastic case with synthetic digital image correlation observations.

This article is a review of basic concepts and tools devoted to a posteriori error estimation for problems solved with the finite element method. For the sake of simplicity and clarity, we mostly focus on linear elliptic diffusion problems, approximated by a conforming numerical discretization. The main goal of this review is to present in a unified manner a large set of powerful verification methods, centered around the concept of equilibrium. Methods based on that concept provide error bounds that are fully computable and mathematically certified. We discuss recovery methods, residual methods, and duality-based methods for the estimation of the whole solution error (i.e., the error in energy norm), as well as goal-oriented error estimation (to assess the error on specific quantities of interest). We briefly survey the possible extensions to nonconforming numerical methods, as well as more complex (e.g., nonlinear or time-dependent) problems. We also provide some illustrating numerical examples on a linear elasticity problem in three dimensions.

Polymer-based capacitors are essential energy storage components in the electronic and electrical industries, which is benefit for their high power density and fast charge–discharge capabilities. However, the low energy density of polymer-based capacitors limits their miniaturization and intelligent applications. In this study, we present the novel poly(vinylidene fluoride) (PVDF)-based composites with exceptional energy storage performance at the submicron metal filler loadings. Guided by synergistically improving the dielectric constant and breakdown strength of polymer-based composites, metal–organic framework (MOF)-derived Fe fillers and Press & Heat (P&H) cycles are mainly implemented. The polymer-based composites exhibit a superior dielectric constant of 15.3, while simultaneously maintain a high breakdown strength of 617.1 MV/m. The excellent energy density of 28.9 J/cm3 is obtained at the ultralow filler loading of 0.2 wt%. Synergistic tuning the loading content of MOF-derived Fe and optimizing the P&H cycles not only leads to a novel composite dielectrics with outstanding energy storage properties, but also presents a new strategy for exploring high-performance capacitive polymer composites.

Oxygen reduction reaction (ORR), operating via four-electron (H2O) or two-electron (H2O2) pathways, underpins critical processes in energy conversion and biological metabolism. Solid–liquid contact electrification enables 2e– ORR for both pollutant oxidation degradation and metal reduction without external metal catalysts. However, the criteria dictating oxidation versus reduction in such Janus contact-electro-chemistry (CE-Chemistry) systems remain unclear. This study systematically demonstrates that the redox selectivity in CE-Chemistry is controlled by the standard electrode potential (SEP) of the reactants, with a clear threshold distinguishing the oxidation and reduction pathways. Reduction of metal ions (e.g., [AuCl4]−, Pd2+, [PtCl4]2– Ag+, Rh3+, and Ir3+) was achieved when their SEPs lie between the 2e– ORR (E0 = 0.695 V vs NHE) and the 4e– ORR (E0 = 1.229 V vs NHE). Conversely, SEPs below the 2e– ORR threshold favored oxidation (e.g., ferrocyanide). For the first time, methanol-to-formaldehyde oxidation was achieved in both aqueous and nonaqueous CE-Chemistry. Remarkably, the formaldehyde production rate in dimethyl sulfoxide was 25 times higher than in aqueous systems, which has already surpassed some photocatalytic processes. This study provides a comprehensive mechanistic framework for CE-Chemistry, highlighting the pivotal role of SEPs in regulating its Janus redox properties and the tunable radical reactivity in nonaqueous environments.

Pantographic blocks are metamaterials made of a finite number of parallel pantographic sheets interconnected by cylindrical pivots. In this paper, a pantographic block subjected to 3-point flexure, where the prescribed displacements are parallel to the pantographic plane, shows essentially monoclastic deformation (i.e., one of the principal curvatures of the top surface is found to be negligible wrt. the other one). Pantographic blocks are modeled herein with a second gradient 3-dimensional continuum model that is valid at the length scale of pantographic cells. This reduced order model allows for predictive numerical simulations whose computational burden is relatively small. Second gradient effects (i.e., higher-order terms contributing to the strain energy) are limited to the second derivatives along the fibers of their transverse displacements. Digital Volume Correlation (DVC) techniques are employed to measure deformed shapes of pantographic blocks. A model-driven initialization procedure of DVC is followed to quantify the shape of such pantographic blocks in large displacements and strains.

International audience

Spiral strands subjected to tensile force and bending loading display a nonlinear dissipative behavior due to frictional interactions between their elementary wires. This study aims to provide an efficient method, based on a computational homogenization procedure, to accurately characterize the nonlinear response of such strands. By using 1D beam elements in both micro- and macro-scale, homogenization is performed along the axial direction of a representative volume element (RVE), leading to expressing a boundary value problem on RVE, driven in a mixed manner by either strains or resulting forces or moments. The boundary value problem on the RVE is solved using an in–house implicit finite element solver for finite strain, considering all frictional contact interactions. A method is proposed to predict the bending moment’s evolution for any curvature variation from the simulation results of only one bending loading test on the RVE. The nonlinear behavior of the strand in the micro-scale identified through this offline technique can then be used in the macro-model to simulate various bending loading tests under constant tensile load. Results obtained with the multiscale model are compared to those provided by direct numerical simulation to demonstrate the validity of the proposed approach.

This work introduces a new approach for accelerating the numerical analysis of time-domain partial differential equations (PDEs) governing complex physical systems. The methodology is based on a combination of a classical reduced-order modeling (ROM) framework and recently-introduced Graph Neural Networks (GNNs), where the latter is trained on highly heterogeneous databases of varying numerical discretization sizes. The proposed techniques are shown to be particularly suitable for non-parametric geometries, ultimately enabling the treatment of a diverse range of geometries and topologies. Performance studies are presented in an application context related to the design of aircraft seats and their corresponding mechanical responses to shocks, where the main motivation is to reduce the computational burden and enable the rapid design iteration for such problems that entail non-parametric geometries. The methods proposed here are straightforwardly applicable to other scientific or engineering problems requiring a large-number of finite element-based numerical simulations, with the potential to significantly enhance efficiency while maintaining reasonable accuracy.

The formulation of history-dependent material laws has been a significant research and industrial activity in solid mechanics for over a century. A large variety of models has been developed, tailored for the description of different families of materials. However, model selection for a specific problem is a delicate issue and there still remain open problems. In fact, the catalog of yield models is continuously being enriched by experts to meet new needs and tailor models to new experimental evidence. We propose here an alternative approach, that is a flexible model-data-driven plasticity formulation, and analyze its challenges and opportunities. A phenomenological yield model is locally improved by a data-driven correction term. The data-based correction component is described by a surrogate model built from machine-learning techniques. In this regard, the framework is versatile, as shown by the fact that similar performances are obtained with Support Vector Regression, Gaussian Process Regression, and Neural Networks. The convexity of model-data yield functions is guaranteed by employing convex extensions of the adopted machine-learning techniques. The proposed approach is substantiated by reproducing a highly anisotropic yield response with tension/compression asymmetries from a simple phenomenological model enhanced with a limited number of synthetic data points of yield onset. It is shown that a crucial role is played by both model and data, the former allowing to use a limited number of data and the latter significantly enhancing model performance.