Horacio D. Espinosa

Living systems do not operate in snapshots [...]

Soft tissues exhibit predominantly time-dependent mechanical behavior critical for their biological function in organs like the lungs and aorta, as they can deform and stretch at varying rates depending on their function. Collagen type I serves as the primary structural component in these tissues. The viscoelastic characteristics of such tissues, stemming from diverse energy dissipation mechanisms across various length scales, remains poorly characterized at the nanoscale. Prior experimental investigations have predominantly centered on analyzing tissue responses largely attributed to interactions between cells and fibers. Despite many studies on tissue viscoelasticity from scaffolds to single collagen fibrils, the time-dependent mechanics of collagen fibrils at the sub-fibrillar level remain poorly understood. This pioneering study employs atomic force microscopy (AFM) nano-rheometry and indentation testing to examine the viscoelastic characteristics of individual collagen type I fibrils at the ultrastructural level within distinct topographical zones, specifically focusing on gap and overlap regions. Our investigation has unveiled that collagen fibrils display a viscoelastic response that replicates the mechanical behavior of the tissue at the macroscale. Further, our findings suggest a distinct viscoelastic behavior between the gap and overlap regions, likely stemming from variances in molecular organization and cross-linking modalities within these specific sites. The results of our investigation provide unequivocal proof of the temporal dependence of mechanical properties and provides unique data to be compared to atomistic models, laying a foundation for refining the precision of macroscale models that strive to capture tissue viscoelasticity across varying length scales. STATEMENT OF SIGNIFICANCE: Soft tissues such as the lungs and aorta depend on collagen to stretch and perform their functions, which involve continuous and dynamic deformation. Although these tissues are known to exhibit viscoelastic behavior, the mechanisms behind this at the fine scale of individual collagen fibril ultrastructure are not well understood. In this study, we used AFM nano-rheometry and direct indentation to be the first to directly measure the viscoelastic properties of collagen at the ultrastructural level. We discovered that single fibrils show time-dependent behavior similar to that of whole tissues, with distinct mechanical differences between regions likely due to variations in molecular organization and bonding. These insights advance our understanding of tissue mechanics and contribute to more accurate multi-scale modeling.

fracture measurements and ab initio predictions of inversion domain formation — phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. This work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

We present a coupled nonlinear optimization framework that combines a physics-based dynamical model K with an optical propagation model H to perform dynamic low-dose characterization directly from time-resolved diffraction data. By embedding the equations of motion within the optical forward operator, we allow for mutual regularization between imaging and dynamics. The method converts the inverse imaging problem into a parameter-estimation task, thereby avoiding frame-by-frame phase retrieval and suppressing ill-conditionedness arising from noisy data. The approach is validated on synchrotron X-ray movies of a thermally actuated micro-electro-mechanical (MEMS) oscillator. At a photon dose of ≈ 4 photons per pixel per frame, it simultaneously reconstructs the shuttle edge profile, partially coherent probe modes, and their temporal occupancies. With an incident flux of ≈ 0.015 photons per pixel per frame, the framework still recovers the MEMS shuttle displacement trajectory. Compared with conventional two-step pipelines, the joint treatment yields improved noise robustness by exploiting temporal correlations and enforcing physically admissible motion.

Mechanical Metamaterials In article number 2420063, Horacio D. Espinosa and co-workers introduce a data-efficient inverse design framework using neural operators trained on sparse experimental data to predict and tailor the nonlinear mechanical behavior of spinodal metamaterials. The approach enables accurate, application-driven design of architected materials for extreme environments—paving the way for next-generation aerospace and multifunctional systems.

Abstract Thin membrane-delimited fluid cells supporting up to 1 at (0.1 MPa) of pressure are well known and commercially available for use in vacuum chambers of electron, photon, or various particle beam microscopies or spectroscopies. Hereby, we report on the development of fluid cells capable of working at 1–10 MPa, extending the analysis domain for investigating chemical, biochemical, or physical processes at pressures of interest in chemical synthesis, underwater biochemistry studies or underground geology. We explored ways to optimize cell membranes to better resist pressure beyond simply increasing the thickness or decreasing the size of the membranes, using finite element analysis and experimental validation via membrane bulging experiments and failure statistics. Fluid cell prototypes were fabricated using ∼75 nm-thick SiN x membranes, engineered to withstand 4.7 MPa (average value), compared to regular (un-engineered) membranes withstanding only 3.4 MPa (average value). The fluid cell prototypes include eight microchannels for feeding/evacuating the fluids and applying pressure into micro-reaction chambers, two electrodes for electrochemical or conduction measurements in the sample, and a possible pressure or temperature sensor, customizable for specific experiments.

Abstract Background Thermomechanical testing of nanomaterials is essential to assess their performance in applications where thermal and mechanical loads occur simultaneously. However, developing a multi-physics testing platform for nanomaterials that integrates temperature control, displacement control, and force sensing remains challenging due to the interference between heating and mechanical testing components. Objective This work aims to develop a novel microelectromechanical system-based platform for in situ thermomechanical testing of nanomaterials with displacement control and precise temperature regulation. Methods The platform integrates a high-stiffness thermal actuator, Joule heating elements, and a capacitive displacement sensor, along with sample stage heaters featuring thermal insulation and thermal expansion compensation structures. Finite element analysis was used to optimize the design and minimize thermomechanical interference. Heating performance was characterized using Raman spectroscopy and resistance measurements. Results Displacement control and precise localized temperature control are achieved, overcoming limitations of transient heat transfer and thermal drift observed in previous systems. Its performance is demonstrated through in situ thermomechanical tensile testing of silver nanowires, showcasing its capability for nanoscale material characterization. Conclusions The developed microelectromechanical system platform enables thermomechanical investigation of size-dependent phenomena in nanomaterials, such as phase transitions and temperature-dependent fracture. Its displacement control and localized temperature regulation, combined with in-situ observation, provide a powerful tool for understanding nanoscale deformation and fracture mechanisms.

Two-dimensional (2D) materials offer significant potential for applications in energy-harvesting devices, batteries, sensors, and transistors. However, their intrinsic brittleness makes them prone to mechanical failure, limiting their practical use. In this work, we perform in situ transmission electron microscopy (TEM) fracture tests on monolayer MoSe 2 and uncover an extrinsic toughening effect induced by an ultrathin adsorbed polystyrene adlayer. This adlayer substantially enhances the fracture resistance of the 2D flakes. Through a combination of molecular dynamics simulations and finite element analysis, we elucidate the molecular mechanism behind this toughening effect. It arises from the active crack-bridging behavior of entangled polymer chains and the formation of a fracture process zone that stabilizes crack propagation and increases the energy required for crack extension. The proposed toughening mechanism offers a pathway to improving the mechanical reliability of 2D material-based devices by mitigating the risk of sudden failure.

Machine learning (ML) is emerging as a transformative tool for the design of mechanical metamaterials, offering properties that far surpass those achievable through lab-based trial-and-error methods. However, a major challenge in current inverse design strategies is their reliance on extensive computational and/or experimental datasets, which becomes particularly problematic for designing micro-scale stochastic architected materials that exhibit nonlinear mechanical behaviors. Here, a comprehensive end-to-end scientific ML framework, leveraging deep neural operators (including DeepONet and its variants) is introduced, to directly learn the relationship between the complete microstructure and mechanical response of architected metamaterials from sparse but high-quality in situ experimental data. Various neural operators and standard neural networks are systematically compared to identify the model that offers better interpretability and accuracy. The approach facilitates the efficient inverse design of structures tailored to specific nonlinear mechanical behaviors. Results obtained from stochastic spinodal microstructures, printed using two-photon lithography, reveal that the prediction error for mechanical responses is within a range of 5 - 10%. This work underscores that by employing neural operators with advanced nano- and micro-mechanical experiments, the design of complex micro-architected materials with desired properties becomes feasible, even in scenarios constrained by data scarcity. This work marks a significant advancement in the field of materials-by-design, potentially heralding a new era in the discovery and development of next-generation metamaterials with unparalleled mechanical characteristics derived directly from experimental insights.

Accurately predicting the non-equilibrium mechanical properties of two-dimensional (2D) materials is essential for understanding their deformation, thermo-mechanical properties, and failure mechanisms. In this study, we parameterize and evaluate two machine learning (ML) interatomic potentials, SNAP and Allegro, for modeling the non-equilibrium behavior of monolayer MoSe2. Using a density functional theory (DFT) derived dataset, we systematically compare their accuracy and transferability against the physics-based Tersoff force field. Our results show that SNAP and Allegro significantly outperform Tersoff, achieving near-DFT accuracy while maintaining computational efficiency. Allegro surpasses SNAP in both accuracy and efficiency due to its advanced neural network architecture. Both ML potentials demonstrate strong transferability, accurately predicting out-of-sample properties such as surface stability, inversion domain formation, and fracture toughness. Unlike Tersoff, SNAP and Allegro reliably model temperature-dependent edge stabilities and phase transformation pathways, aligning closely with DFT benchmarks. Notably, their fracture toughness predictions closely match experimental measurements, reinforcing their suitability for large-scale simulations of mechanical failure in 2D materials. This study establishes ML-based force fields as a powerful alternative to traditional potentials for modeling non-equilibrium mechanical properties in 2D materials.

The esophagus, the tube-like organ responsible for transporting food from the pharynx to the stomach, operates as a highly mechanical structure, exhibiting complex contraction and distension patterns triggered by neurological impulses. Despite the critical role of mechanics in its function and the need for high-fidelity models of esophageal transport, mechanical characterization studies of human esophagus remain relatively scarce. In addition to the paucity of studies in human specimens, the available results are often scattered in terms of methodology and scope, making it difficult to compare findings across studies and thereby limiting their use in computational models. In this work, we present a detailed passive-mechanical and structural characterization of the esophageal muscular layers, excised from short esophageal segments obtained from live patients with varied clinical presentations. Specifically, we conducted uniaxial and planar biaxial extension tests on the smooth muscle layers, complemented by pre- and post-testing structural characterization via histological imaging. Unlike existing studies, our experimental results on passive behavior are discussed in the context of physiological relevance (e.g., physiological stretches, and activity-inhibiting pathologies), providing valuable insights that guide the subsequent modeling of the esophagus' mechanical response. As such, this work provides new insights into the passive properties of the fresh human esophagus, expands the existing database of mechanical parameters for computational modeling, and lays the foundation for future studies on active mechanical properties. STATEMENT OF SIGNIFICANCE: Understanding the mechanical properties of the esophagus is crucial for developing accurate models of its function and suitable replacements. This study provides insights into the passive mechanical behavior of fresh human esophageal tissue, enhancing our understanding of how it responds to stretching under physiological conditions. By characterizing the properties of different esophageal layers, obtained from esophagectomy specimens with various presentations, and considering their relevance to both normal and abnormal functioning, this work addresses the gap in ex-vivo human esophagus studies. The findings emphasize the importance of contextually analyzing experimental results within physiological parameters and suggest avenues for future research to further refine our understanding of esophageal mechanics, paving the way for improved diagnostic and therapeutic approaches in managing esophageal disorders.

The powerful strikes generated by the smasher mantis shrimp require it to possess a robust protection mechanism to withstand the resultant forces. Although recent studies have suggested that phononic bandgaps complement the mantis shrimp's defensive suite, direct experimental evidence for this mechanism has remained elusive. In this work, we explored the phononic properties of the mantis shrimp's dactyl club using laser ultrasonic techniques and numerical simulations. Our results demonstrate that the dactyl club's periodic region functions as a dispersive, high-quality graded system, exhibiting Bloch harmonics, flat dispersion branches, ultraslow wave modes, and wide Bragg bandgaps in the lower megahertz range. These features effectively shield the shrimp from harmful high-frequency stress waves generated by cavitation bubble collapse events during impact.

We develop a method that reconstructs nano-oscillator displacement from low-dose diffraction movies by jointly optimizing over dynamics and optical model; the formulation readily extends to simultaneously retrieve unknown sample shape and probe.

Summary Developing a reservoir requires key data on reservoir quality, well productivity, and hydrocarbon volume. Traditionally, formation testers and Drill Stem Testing (DST) have been used, but both have limitations: formation testers have restricted pressure transient testing range, and DST involves flaring, which increases CO₂ emissions. In Southeast Asia, reservoirs are often laminated, thinly bedded, and compartmentalized, making DST of every zone costly and time-consuming. To address this, Deep Transient Testing (DTT) was introduced as a zero-flaring alternative that extends formation tester capabilities and reduces the need for DST, especially in laminated reservoirs. Numbers of DTT case studies from South-East Asia will be presented in this paper. With the total of 5 DTTs, a 71–99% reduction in CO2e (CO2 equivalent) quantities compared to DST. This paper will also discuss the integration of DTT and DST to help obtain layer information by using this digital workflow.

Summary 3D Far field sonic imaging, a novel sonic reflection imaging analysis that identifies dip, azimuth, and distance to reflector for acoustic impedance contrast events tens of meters deep into the formation was applied in a naturally fractured heterogenous brecciated reservoir. Integration of these events with borehole image enabled interpretation of structural bed boundaries, faults and open fractures, their extension and connectivity within the reservoir. The consistency in the integration identified the fracture network extending deep into formation. The results were used to update the static discrete fracture network resulting in a production history match that had not been possible before.

Natural design and fabrication strategies have long served as a source of inspiration for novel materials with enhanced properties. Less investigated is the prospect of leveraging the complexity of readily available, naturally occurring micro-/nanostructures as platforms for investigating functional materials. In the field of phononics, exploiting structural biocomposites is gaining traction; but finding natural phononic structures that interact with ultra- and hypersonic acoustic waves remains an open quest. In this context, the phononic behavior of natural Nacre, a biocomposite often looked at for inspiration due to its superlattice-like architecture of alternating organic and inorganic phases, is here characterized. To such end, a combination of non-contact pump-probe laser ultrasonics techniques and Brillouin spectroscopy are employed to interrogate Nacre's hierarchical structure at the micro- and nanoscale and measure its phononic dispersion behavior in the MHz and GHz range. It is found that for wavelengths longer than the brick-and-mortar characteristic length, Nacre behaves as a dispersionless medium with effective transversely isotropic properties; but as the wavelengths become comparable to its structural periodicity an involved phononic spectrum arises which challenges the notion of a perfectly periodic, high mechanical-contrast biocomposite.

Abstract Soft tissues exhibit predominantly time-dependent mechanical behavior critical for their biological function in organs like the lungs and aorta, as they can deform and stretch at varying rates depending on their function. Collagen type I serves as the primary structural component in these tissues. The viscoelastic characteristics of such tissues, stemming from diverse energy dissipation mechanisms across various length scales, remains poorly characterized at the nanoscale. Prior experimental investigations have predominantly centered on analyzing tissue responses largely attributed to interactions between cells and fibers. Despite many studies on tissue viscoelasticity from scaffolds to single collagen fibrils, the time-dependent mechanics of collagen fibrils at the sub-fibrillar level remain poorly understood. This pioneering study employs atomic force microscopy (AFM) nano-rheometry and indentation testing to examine the viscoelastic characteristics of individual collagen type I fibrils at the ultrastructural level within distinct topographical zones, specifically focusing on gap and overlap regions. Our investigation has unveiled that collagen fibrils display a viscoelastic response that replicates the mechanical behavior of the tissue at the macroscale. Further, our findings suggest a distinct viscoelastic behavior between the gap and overlap regions, likely stemming from variances in molecular organization and cross-linking modalities within these specific sites. The results of our investigation provide unequivocal proof of the temporal dependence of mechanical properties and provides unique data to be compared to atomistic models, laying a foundation for refining the precision of macroscale models that strive to capture tissue viscoelasticity across varying length scales.

The mechanical and thermal properties of transition metal dichalcogenides (TMDs) are directly relevant to their applications in electronics, thermoelectric devices, and heat management systems. In this study, we use a machine learning (ML) approach to parametrize molecular dynamics (MD) force fields to predict the mechanical and thermal transport properties of a library of monolayered TMDs (MoS 2, MoTe 2, WSe 2, WS 2, and ReS 2 ). The ML-trained force fields were then employed in equilibrium MD simulations to calculate the lattice thermal conductivities of the foregoing TMDs and to investigate how they are affected by small and large mechanical strains. Furthermore, using nonequilibrium MD, we studied thermal transport across grain boundaries. The presented approach provides a fast albeit accurate methodology to compute both mechanical and thermal properties of TMDs, especially for relatively large systems and spatially complex structures, where density functional theory computational cost is prohibitive.

The potential of Kirigami-based metamaterials as versatile platforms in applications where shape-morphability is desired is presently well established. Interestingly, in-plane loading under simple tension has been the predominant actuation-of-choice, with other fundamental loadings (e.g., shear, torsion) being largely overlooked. In this work, we explore the instability landscape of a simple Kirigami motif under in-plane shear loading and demonstrate how the incorporation of shear actuation effectively augments both the design and output space of Kirigami-based metamaterials. The nonlinear response of Kirigami under shear is first elucidated parametrically via nonlinear Finite Element Analysis on a subset of geometrically asymmetric motifs. The interplay between cut layout and the bidirectional nature of in-plane shear enables a variegated bifurcation landscape that includes unusual inversions and sharp kinks in the bifurcation curves. The numerical analysis is complemented with experimental validation of the out-of-plane bifurcation curves of several cases of interest by means of shadow moir ´ e and laser displacement sensing. Together with the experimental validation, a proof-of-concept demonstration of the applicability of Kirigami-shear metamaterials as multistate electro-mechanical switches is presented. Overall, our findings add a new dimension to an already rich design space, thus opening a vista of opportunities for multimodal programmable structures, particularly appealing in sensing and actuation applications.

Stress-strain curves, or more generally, stress functions, are an extremely important characterization of a material's mechanical properties. However, stress functions are often difficult to derive and are narrowly tailored to a specific material. Further, large deformations, high strain-rates, temperature sensitivity, and effect of material parameters compound modeling challenges. We propose a generalized deep neural network approach to model stress as a state function with quantile regression to capture uncertainty. We extend these models to uniaxial impact mechanics using stochastic differential equations to demonstrate a use case and provide a framework for implementing this uncertainty-aware stress function. We provide experiments benchmarking our approach against leading constitutive, machine learning, and transfer learning approaches to stress and impact mechanics modeling on publicly available and newly presented data sets. We also provide a framework to optimize material parameters given multiple competing impact scenarios.

The mechanical properties and deformation mechanisms of single crystal magnesium under c-axis quasi-static and high-strain rate compressions are investigated through in situ scanning electron microscope (SEM) experiments and post-mortem transmission electron microscope (TEM) characterization. The findings revealed that ductility and high rates of hardening are preserved for pillars as large as 15 μ m. Furthermore, rate effects result in a mild increase in flow stress with plastic deformations controlled primarily by the slip of < a + c > type dislocations. Importantly and in contrast to other literature reports, plastic deformation occurs in the absence of twining . As the strain increases and plastic deformation exceeds about 4%, crystal rotation activates basal slip, < a > type dislocations, resulting in a more rate independent flow stress. TEM observation on micropillars compressed at a strain rate of 250/s, revealed the activation of { 1122 }〈 1123 > slip systems and high mobility of screw dislocations as major contributors to plastic strains in excess of 10% without fracture . These findings are relevant to the design of lightweight materials used in transportation systems, e.g., selection of material grain size. Moreover, the experimental data here reported provides the materials science community with a unique opportunity to validate discrete dislocation dynamics (DDD) formulations employed in multiscale design of materials.

Efficient and nontoxic delivery of foreign cargo into cells is a critical step in many biological studies and cell engineering workflows with applications in areas such as biomanufacturing and cell-based therapeutics. However, effective molecular delivery into cells involves optimizing several experimental parameters. In the case of electroporation-based intracellular delivery, there is a need to optimize parameters like pulse voltage, duration, buffer type, and cargo concentration for each unique application. Here, we present the protocol for fabricating and utilizing a high-throughput multi-well localized electroporation device (LEPD) assisted by deep learning-based image analysis to enable rapid optimization of experimental parameters for efficient and nontoxic molecular delivery into cells. The LEPD and the optimization workflow presented herein are relevant to both adherent and suspended cell types and different molecular cargo (DNA, RNA, and proteins). The workflow enables multiplexed combinatorial experiments and can be adapted to cell engineering applications requiring in vitro delivery. Key features • A high-throughput multi-well localized electroporation device (LEPD) that can be optimized for both adherent and suspended cell types. • Allows for multiplexed experiments combined with tailored pulse voltage, duration, buffer type, and cargo concentration. • Compatible with various molecular cargoes, including DNA, RNA, and proteins, enhancing its versatility for cell engineering applications. • Integration with deep learning-based image analysis enables rapid optimization of experimental parameters.

Abstract Mechanical metamaterials, whose unique mechanical properties stem from their structural design rather than material constituents, are gaining popularity in engineering applications. In particular, recent advances in self-assembly techniques offer the potential to fabricate load-bearing mechanical metamaterials with unparalleled feature size control and scalability compared to those produced by additive manufacturing (AM). Yet, the field is still in its early stages. In this perspective, we first provide an overview of the state-of-the-art self-assembly techniques, with a focus on the copolymer and colloid crystal self-assembly processes. We then discuss current challenges and future opportunities in this research area, focusing on novel fabrication approaches, the need for high-throughput characterization methods, and the integration of Machine Learning (ML) and lab automation for inverse design. Given recent progress in all these areas, we foresee mechanical metamaterials fabricated from self-assembly techniques impacting a variety of applications relying on lightweight, strong, and tough materials.

Machine learning (ML) is emerging as a transformative tool for the design of architected materials, offering properties that far surpass those achievable through lab-based trial-and-error methods. However, a major challenge in current inverse design strategies is their reliance on extensive computational and/or experimental datasets, which becomes particularly problematic for designing micro-scale stochastic architected materials that exhibit nonlinear mechanical behaviors. Here, we introduce a new end-to-end scientific ML framework, leveraging deep neural operators (DeepONet), to directly learn the relationship between the complete microstructure and mechanical response of architected metamaterials from sparse but high-quality in situ experimental data. The approach facilitates the inverse design of structures tailored to specific nonlinear mechanical behaviors. Results obtained from spinodal microstructures, printed using two-photon lithography, reveal that the prediction error for mechanical responses is within a range of 5 - 10%. Our work underscores that by employing neural operators with advanced micro-mechanics experimental techniques, the design of complex micro-architected materials with desired properties becomes feasible, even in scenarios constrained by data scarcity. Our work marks a significant advancement in the field of materials-by-design, potentially heralding a new era in the discovery and development of next-generation metamaterials with unparalleled mechanical characteristics derived directly from experimental insights.

Mechanical metamaterials, whose unique mechanical properties stem from their structural design rather than material constituents, are gaining popularity in engineering applications. In particular, recent advances in self-assembly techniques offer the potential to fabricate load-bearing mechanical metamaterials with unparalleled feature size control and scalability compared to those produced by additive manufacturing (AM). Yet, the field is still in its early stages. In this perspective, we first provide an overview of the state-of-the-art self-assembly techniques, with a focus on the copolymer and colloid crystal self-assembly processes. We then discuss current challenges and future opportunities in this research area, focusing on novel fabrication approaches, the need for high-throughput characterization methods, and the integration of Machine Learning (ML) and lab automation for inverse design. Given recent progress in all these areas, we foresee mechanical metamaterials fabricated from self-assembly techniques impacting a variety of applications relying on lightweight, strong, and tough materials.

Stress-strain curves, or more generally, stress functions, are an extremely important characterization of a material's mechanical properties. However, stress functions are often difficult to derive and are narrowly tailored to a specific material. Further, large deformations, high strain-rates, temperature sensitivity, and effect of material parameters compound modeling challenges. We propose a generalized deep neural network approach to model stress as a state function with quantile regression to capture uncertainty. We extend these models to uniaxial impact mechanics using stochastic differential equations to demonstrate a use case and provide a framework for implementing this uncertainty-aware stress function. We provide experiments benchmarking our approach against leading constitutive, machine learning, and transfer learning approaches to stress and impact mechanics modeling on publicly available and newly presented data sets. We also provide a framework to optimize material parameters given multiple competing impact scenarios.

Vacancy engineering of 2H-transition metal dichalcogenides (2H-TMDs) has recently attracted great attention due to its potential to fi ne-tune the phonon and opto-electric properties of these materials. From a mechanical perspective, this symmetry-breaking process typically reduces the overall crack resistance of the material and adversely affects its reliability. However, vacancies can trigger the formation of heterogeneous phases that synergistically improve fracture properties. In this study, using MoSe 2 as an example, we characterize the types and density of vacancies that can emerge under electron irradiation and quantify their effect on fracture. Molecular dynamic (MD) simulations, employing a re-parameterized Tersoff potential capable of accurately capturing bond dissociation and structural phase changes, reveal that isolated transition metal monovacancies or chalcogenide divacancies tend to arrest the crack tip and hence enhance the monolayer toughness. In contrast, isolated chalcogenide monovacancies do not signi fi cantly affect toughness. The investigation further reveals that selenium vacancy lines, formed by high electron dose rates, alter the crack propagating direction and lead to multiple crack kinking. Using atomic displacements and virial stresses together with a continuum mapping, displacement, strain, and stress fi elds are computed to extract mechanistic information, e.g., conditions for crack kinking and size effects in fracture events. The study also reveals the potential of speci fi c defect patterns, “ vacancy engineering, ” to improve the toughness of 2H-TMDs materials.

Lattice-based constructs, often made by additive manufacturing, are attractive for many applications. Typically, such constructs are made from microscale or larger elements; however, smaller nanoscale components can lead to more unusual properties, including greater strength, lighter weight, and unprecedented resiliencies. Here, solid and hollow nanoparticles (nanoframes and nanocages; frame size: ~15 nanometers) were assembled into colloidal crystals using DNA, and their mechanical strengths were studied. Nanosolid, nanocage, and nanoframe lattices with identical crystal symmetries exhibit markedly different specific stiffnesses and strengths. Unexpectedly, the nanoframe lattice is approximately six times stronger than the nanosolid lattice. Nanomechanical experiments, electron microscopy, and finite element analysis show that this property results from the buckling, densification, and size-dependent strain hardening of nanoframe lattices. Last, these unusual open architectures show that lattices with structural elements as small as 15 nanometers can retain a high degree of strength, and as such, they represent target components for making and exploring a variety of miniaturized devices.

Abstract For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel artificial materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is growing exponentially, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano- and micromechanics, architected materials, and two-dimensional materials. Finally, we highlight some current challenges of applying ML to multimodality and multifidelity experimental datasets, quantifying the uncertainty of ML predictions, and proposing several future research directions. This review aims to provide valuable insights into the use of ML methods and a variety of examples for researchers in solid mechanics to integrate into their experiments.

A multiport high-pressure fluid cell was developed based on an engineered thin film membrane solution with a reinforced frame of boron-doped silicon, optimized to withstand better pressure with an increase of 17.6% in average burst pressure over similar membranes without frame.The novel cells (patent pending) have 8 microfluidic ports, two intra-cavity electrodes and two pressure sensors on a 8 mm x 8 mm Si chip.The prototypes resisted up to 60 bar with 50-nm-thick silicon nitride membranes of size 70 nm x 70 nm.The cells can be used for testing reactions in fluids in medium-high pressure (up to 300 bar) using X-rays (primary application), electron beams, IR or visible light.Improvements in the pressure sensing setup and customization for applications are supposed to follow.

For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano- and micro-mechanics, architected materials, and 2D material. Finally, we highlight some current challenges of applying ML to multi-modality and multi-fidelity experimental datasets and propose several future research directions. This review aims to provide valuable insights into the use of ML methods as well as a variety of examples for researchers in solid mechanics to integrate into their experiments.

Membrane disruption using Bulk Electroporation (BEP) is a widely used non-viral method for delivering biomolecules into cells. Recently, its microfluidic counterpart, Localized Electroporation (LEP), has been successfully used for several applications ranging from reprogramming and engineering cells for therapeutic purposes to non-destructive sampling from live cells for temporal analysis. However, the side effects of these processes on gene expression, that can affect the physiology of sensitive stem cells are not well understood. Here, we use single cell RNA sequencing (scRNA-seq) to investigate the effects of BEP and LEP on murine neural stem cell (NSC) gene expression. Our results indicate that unlike BEP, LEP does not lead to extensive cell death or activation of cell stress response pathways that may affect their long-term physiology. Additionally, our demonstrations show that LEP is suitable for multi-day delivery protocols as it enables better preservation of cell viability and integrity as compared to BEP.

Delivery of proteins and protein-nucleic acid constructs into live cells enables a wide range of applications from gene editing to cell-based therapies and intracellular sensing. However, electroporation-based protein delivery remains challenging due to the large sizes of proteins, their low surface charge, and susceptibility to conformational changes that result in loss of function. Here, we use a nanochannel-based localized electroporation platform with multiplexing capabilities to optimize the intracellular delivery of large proteins (β-galactosidase, 472 kDa, 75.38% efficiency), protein-nucleic acid conjugates (protein spherical nucleic acids (ProSNA), 668 kDa, 80.25% efficiency), and Cas9-ribonucleoprotein complex (160 kDa, ∼60% knock-out and ∼24% knock-in) while retaining functionality post-delivery. Importantly, we delivered the largest protein to date using a localized electroporation platform and showed a nearly 2-fold improvement in gene editing efficiencies compared to previous reports. Furthermore, using confocal microscopy, we observed enhanced cytosolic delivery of ProSNAs, which may expand opportunities for detection and therapy.