Sinan Keten

Polymer-grafted nanoparticles (PGNs) have emerged as a versatile class of hybrid building blocks that can self-assemble into complex superlattices with highly tunable physical properties. While the mechanical behavior of single-component PGNs (SC-PGNs) has been extensively studied, the emergent structure and mechanics of multicomponent PGNs (MC-PGNs) remain poorly understood. In particular, whether improving packing by designer superlattices can enhance mechanical performance remains to be established. To achieve this goal, we investigate the mechanical response of binary mixtures of polystyrene-grafted Fe 3 O 4 ( A ) and Au ( B ) nanoparticles by combining coarse-grained molecular dynamics (CG-MD) simulations with a data-driven Gaussian Process (GP) metamodel and Bayesian optimization (BO). We discover NaCl ( AB ) lattices simultaneously achieve higher modulus and toughness under uniaxial tensile loading due to efficient nanoscale packing and systematically explore their design space by varying grafting parameters and stoichiometry to establish key structure–property relationships. BO provides ∼32% improvement in the toughness-modulus Pareto front, while Sobol sensitivity analysis highlights the dominant influence of larger A PGNs. Remarkably, molecular conformation analysis reveals that smaller B PGNs, although passive in terms of parameter sensitivity, play a critical role in enhancing nanoparticle packing and toughness through effective interparticle entanglements. The results provide a comprehensive framework for understanding and optimizing the mechanics of MC-PGNs, establishing a pathway for navigating the strength–toughness trade-off in polymer nanocomposites.

Polymer-grafted nanoparticles (PGNs) have emerged as a versatile class of hybrid building blocks that can self-assemble into complex superlattices with highly tunable physical properties. While the mechanical behavior of single-component PGNs (SC-PGNs) has been extensively studied, the emergent structure and mechanics of multicomponent PGNs (MC-PGNs) remain poorly understood. In particular, whether improving packing by designer superlattices can enhance mechanical performance remains to be established. To achieve this goal, we investigate the mechanical response of binary mixtures of polystyrene-grafted <i>Fe</i>₃<i>O</i>₄ (<i>A</i>) and <i>Au</i> (<i>B</i>) nanoparticles by combining coarse-grained molecular dynamics (CG-MD) simulations with a data-driven Gaussian Process (GP) metamodel and Bayesian optimization (BO). We discover <i>NaCl</i> (<i>AB</i>) lattices simultaneously achieve higher modulus and toughness under uniaxial tensile loading due to efficient nanoscale packing and systematically explore their design space by varying grafting parameters and stoichiometry to establish key structure–property relationships. BO provides ∼32% improvement in the toughness-modulus Pareto front, while Sobol sensitivity analysis highlights the dominant influence of larger <i>A</i> PGNs. Remarkably, molecular conformation analysis reveals that smaller <i>B</i> PGNs, although passive in terms of parameter sensitivity, play a critical role in enhancing nanoparticle packing and toughness through effective interparticle entanglements. The results provide a comprehensive framework for understanding and optimizing the mechanics of MC-PGNs, establishing a pathway for navigating the strength–toughness trade-off in polymer nanocomposites.

The effect of preparation conditions on the properties of glassy polymers has been a subject of intense research, and these glassy polymers also age with deleterious consequences on properties. Here, we surprisingly find a similar preparation dependence for polymer-grafted nanoparticle melts (PGNP), even when the chains are in the melt. Specifically, we show that processing PGNPs by spin-casting vs slowly casting them from solutions yields temporally stable states with vastly different properties, including surface morphologies, mechanical properties, and gas transport. We propose that these differences arise because the end-grafted polymer brushes, especially for high grafting density and short chain lengths, are in an extremely long-lived colloidal glassy state, even though the chains themselves are mobile. Simulations suggest that there are strong variations in the chain interpenetration states between adjacent nanoparticles driven by solvent evaporation rates. With slower evaporation, there is evidently increased chain interpenetration leading to mechanical property improvements, while collapsed brushes dramatically increase gas permeability properties under fast evaporated conditions. These results strongly argue that processing protocols are an unappreciated control variable in determining the temporally stable properties of this class of materials.

Abstract Bayesian optimization (BO) is a widely used framework for optimizing expensive black-box functions and has found applications across engineering, materials science, and machine learning. However, in many practical tasks, feasibility of design is not readily quantifiable. Conventional BO methods often ignore or poorly model design feasibility, leading to impractical or invalid solutions. This work addresses the challenge of integrating feasibility information directly into the BO process. Here we show that modeling feasibility using Gaussian process (GP) classification and treating it as an objective together with other performance objectives in a multi-objective BO setup significantly improves solution quality across different benchmark design tasks. We develop a latent variable Gaussian process classifier for modeling feasibility over categorical design spaces, and use a Dirichlet-based GP classifier for continuous spaces. Our approach provides quantification of feasibility, offering clear optimization guidance. Comparative studies on analytical and real-world test problems demonstrate enhanced performance in terms of both feasibility and optimality. This approach could be extended to a wide range of applications where feasibility is implicit or difficult to define, such as materials discovery, drug design, and chemical process optimization. By re-framing feasibility as a learnable objective, our work opens new avenues for constrained optimization under uncertainty.

Polymer chains entangle when they are sufficiently long, dense, and mobile, comprising the microstructure of polymers. Entangled polymer chains cannot pass each other, but they slip and transmit tension to other polymer chains, showing unique effects on elastic and viscoelastic properties, as well as fracture properties. This review discusses recent advancements in understanding the relationship between entanglements and fracture. A summary of various fracture properties, including toughness, strength, stretchability, work of fracture, fatigue threshold, and endurance limit, across different polymeric systems, including gels, elastomers, and plastics, is provided with discussions on the role of entanglements. A thorough understanding of how entanglements affect fracture properties will enable a rational design of mechanically durable polymers and provide insights into inferring polymer structures from fracture properties.

Polyamide membranes, such as nanofiltration (NF) membranes, are widely used for water purification. However, the mechanisms of solute transport and solute rejection due to solute charge interactions with the membrane remain unclear at the molecular level. Here, we use molecular dynamics simulations to examine the transport of single-solute feeds through charged nanofiltration membranes with different membrane charge concentrations of COO- and NH+2 resulting from the deprotonation or protonation of polymeric end groups according to the pH level that the membrane experiences. The results show that Na+ and Cl- solute ions are better rejected when the membrane has a higher concentration of negatively charged groups, corresponding to a higher pH, whereas CaCl2 is well rejected at all pH levels studied. These results are consistent with those of experiments performed at the same pH conditions as the simulation setup. Moreover, solute transport behavior depends on the membrane functional group distribution. When COO- functional groups are concentrated at membrane feed surface, ion permeation into the membrane is reduced. Counter-ions tend to associate with charged functional groups while co-ions seem to pass by the charged groups more easily. In addition, steric effects play a role when ions of opposite charge cluster in pores of the membrane. This study reveals solute transport and rejection mechanisms related to membrane charge and provides insights into how membranes might be designed to achieve specific desired solute rejection.

Abstract We support a preliminary determination of the catch-bond character of a mechanical–chemical toy model using a tweezers construction with some modifications. We discuss a theoretical analysis of the problem using Newton trajectories. We propose a two-dimensional potential energy surfaces for this model. We discuss the slip, ideal and catch-bonds for this model using the previous potential parts of Dansuk and Keten (Matter 1:911, 2019). Chemical examples of the ansatz are allosteric reactions, especially FimH proteins. We note again that Newton trajectories provide the theoretical background of mechanochemistry. Construction of a potential energy surface and use of Newton trajectories by Wolfram Mathematica. Calculation of real catch bond behavior. We get for a tweezers model the catch bond behavior. Graphical abstract Two barriers under external force, F. The catch-bond barrier increases.

Polycarbonate (PC) possesses uniquely high toughness among polymers, making it well-suited for use as an impact-resistant barrier material. This propensity toward energy dissipation has been associated with characteristics such as backbone flexibility, high entanglement density, and homogeneity. While recent works have enhanced our understanding of how these nanoscale mechanisms contribute to toughness in PC, it remains unclear how they are affected by the deformation mode, rate, and molecular weight of the chains. To study these effects over spatiotemporal scales that extend beyond the reach of atomistic models, we utilized a coarse-grained molecular dynamics (CGMD) model of PC developed with the energy renormalization method. We establish that yield stress rate dependence follows the Cowper–Symonds model for flow stress, the fit for which asymptotically converges to values consistent with low-rate experimental data. As a demonstration of the model’s utility, we additionally explore the effects of PC chain length on fracture behavior and show that toughness is improved through the augmentation of extensive entanglement networks that enable increased stress levels in the material. For chains 50 monomers and longer, chain length has a minimal effect on yield stress and elastic modulus, suggesting that small-strain mechanical response is dominated by nonbonded interactions. This work enables an enhanced understanding of molecular contributions to the macroscopic mechanical behavior of PC and reflects the importance of the polycarbonate chain network in modulating energy dissipation. It additionally highlights the importance of bond breaking in MD models subjected to large strain. More broadly, it represents a critical step toward the CGMD modeling of PC-based nanocomposites.

The properties of biological materials like proteins and nucleic acids are largely determined by their primary sequence. Certain segments in the sequence strongly influence specific functions, but identifying these segments, or so-called motifs, is challenging due to the complexity of sequential data. While deep learning (DL) models can accurately capture sequence–property relationships, the degree of nonlinearity in these models limits the assessment of monomer contributions to a property─a critical step in identifying key motifs. Recent advances in explainable AI (XAI) offer attention and gradient-based methods for estimating monomeric contributions. However, these methods are primarily applied to classification tasks, such as binding site identification, where they achieve limited accuracy (40–45%) and rely on qualitative evaluations. To address these limitations, we introduce a DL model with interpretable steps, enabling direct tracing of monomeric contributions. Inspired by the masking technique commonly used in vision and natural language processing domains, we propose a new metric ( I ) for quantitative analysis on datasets mainly containing distinct properties of anticancer peptides (ACP), antimicrobial peptides (AMP), and collagen. Our model exhibits 22% higher explainability than the gradient and attention-based state-of-the-art models, recognizes critical motifs (RRR, RRI, and RSS) that significantly destabilize ACPs, and identifies motifs in AMPs that are 50% more effective in converting non-AMPs to AMPs. These findings highlight the potential of our model in guiding mutation strategies for designing protein-based biomaterials.

Although polymers are most often used within bulk materials, investigating their conformations and dynamics has long been a challenging endeavor in this configuration, particularly under external forces. Addressing this, we utilize single-molecule localization microscopy as a powerful imaging tool to visualize bottlebrush poly(n-butyl acrylate) chains in the bulk state under spherical indentation, quantitatively describing changes in behavior of single polymer chains. We compare these experiments to displacement fields determined analytically and confirmed through finite element analysis. This study pioneers visualizing polymer conformational changes in their native environment in situ, offering transformative insights into polymer behavior and dynamics.

Polymer-grafted nanoparticles (PGNs) in matrix-free nanocomposites offer unique opportunities for highly loaded nanocomposites and superior mechanical performance compared to neat polymers. However, increasing Young's modulus with high nanoparticle volume fractions generally reduces toughness. This study uses coarse-grained molecular dynamics simulations to examine how grafted chain length, grafting density, and nanoparticle size affect the high strain rate mechanical performance of glassy PGN systems. Young's modulus generally increases with the inorganic volume fraction but deviates across grafting densities due to steric hindrance near the PGN core, causing stiffening. Sparsely grafted PGNs demonstrate superior toughness due to the release of nanoconfinement in the polymer brush. This reduction in confinement enables high interdigitation, facilitating effective inter-PGN entanglements that drive strain hardening and enhance toughness. Finally, two primary fracture mechanisms, disentanglement and chain scission, are attributed to enabling sustained energy dissipation during large deformations, promoting PGN toughness.

Polyamide membranes, such as nanofiltration (NF) and reverse osmosis (RO) membranes, are widely used for water desalination and purification. However, the mechanisms of solute transport and solute rejection due to charge interactions remain unclear at the molecular level. Here we use molecular dynamics (MD) simulations to examine the transport of single-solute feeds through charged nanofiltration membranes with different membrane charge concentrations of COO$^{\text{-}}$ and NH$_2\!^+$ corresponding to different pH levels. Results show that Na$^+$ and Cl$^{\text{-}}$ solute ions are better rejected when the membrane has a higher concentration of negatively charged groups, corresponding to a higher pH, whereas CaCl$_2$ is well-rejected at all pH levels studied. These results are consistent with experimental findings which are performed at the same pH conditions as simulation setup. Moreover, solute transport behavior depends on the membrane functional group distribution. When COO$^{\text{-}}$ functional groups are concentrated at membrane feed surface, ion permeation into the membrane is reduced. Counter-ions tend to associate with charged functional groups while co-ions seem to pass by the charged groups more easily. In addition, steric effects play a role when ions of opposite charge cluster in pores of the membrane. This study reveals solute transport and rejection mechanisms related to membrane charge and provides insights into how membranes might be designed to achieve specific desired solute rejection.

A major challenge in synthesizing strong and tough protein fibers based on spider silk motifs is understanding the coupling between protein sequence and the postspin drawing process. We clarify how drawing-induced elongational force affects ordering, chain extension, interchain contacts, and molecular mobility through mesoscale simulations of silk-based fibers. We show that these emergent features can be used to predict mechanical property enhancements arising from postspin drawing. Simulations recapitulate a purely process-dependent mechanical property envelope in which order enhances fiber strength while preserving toughness. The relationship between chain extension and crystalline domain alignment observed in simulations is validated by Raman spectroscopy of wet-spun fibers. Property enhancements attributed to the progression of anisotropic extension are verified by mechanical tests of drawn silk fibers and justified by theory. These findings elucidate how drawing enhances properties of protein-based fibers and shed light on how to incorporate this effect into predictive models.

Recent work has demonstrated that polymer-grafted nanoparticle (PGN) melts are spatially heterogeneous media with tunable gas transport properties. In particular, it is thought that the region near the nanoparticle (NP) surface, where the grafted chains are stretched due to crowding effects, as well as the interstitial regions within the NP packing, exhibit distinct transport behaviors. Based on these notions, this work proposes an analytical two channel model with a high-barrier channel akin to the pure polymer melt, and a low-barrier channel with zero activation energy. The model, developed with these simplifying assumptions, has one parameter, the fractional occupancy of the high-barrier channel, which is fit to gas permeability data as a function of chain molecular weight and gas type. Gases as big as CO 2 are present in both channels, while all larger gases primarily occupy the ”high-barrier” channel. Since the model does not distinguish between solubility and diffusivity, it is concluded that the results found for the larger gases are consistent with the experimental findings showing that they have increased solubility within the interstitial spaces of the PGN structure. Similarly, the low channel corresponds to the stretched polymer brush with fast transport for all gases. Despite their higher fractional occupancy in the high-barrier channel, large gases also preferably transport through the low-barrier channel. The distinctions in energy barriers between the two channels manifest through a critical gas size beyond which the model’s effective energy barrier becomes gas size-independent. This highlights the bilinear nature of gas transport in PGNs which results from their heterogeneous spatial structure.

Nanoconfinements are utilized to program how polymers entangle and disentangle as chain clusters to engineer pseudo bonds with tunable strength, multivalency, and directionality. When amorphous polymers are grafted to nanoparticles that are one magnitude larger in size than individual polymers, programming grafted chain conformations can "synthesize" high-performance nanocomposites with moduli of ≈25GPa and a circular lifecycle without forming and/or breaking chemical bonds. These nanocomposites dissipate external stresses by disentangling and stretching grafted polymers up to ≈98% of their contour length, analogous to that of folded proteins; use both polymers and nanoparticles for load bearing; and exhibit a non-linear dependence on composition throughout the microscopic, nanoscopic, and single-particle levels.

Incorporating dynamic covalent linkages into thermosets can endow previously unrecyclable materials with new functionality and reprocessing options. Recent work has shown that the properties of the resulting covalent adaptable networks (CANs) are highly dependent on network topology, specifically the phenomenon of percolation, when permanent linkages form a connected skeleton that spans the material. Here, we use a model glassy disulfide based CAN to assess the merits of mean-field percolation theory as a tool to describe the network topology of CANs. After challenging the theory with both experimental data and a coarse-grained molecular dynamics simulation, we find that the mean-field approach is surprisingly accurate, despite its simplifying assumptions. The theory is particularly well suited to the unique context of mixed-composition CANs and provides practical guidance on how to design for reprocessability.

We utilize nanoconfinements to program how polymers entangle and disentangle as chain clusters to engineer pseudo bonds with tunable strength, multivalency, and directionality. When amorphous polymers are grafted to nanoparticles that are one magnitude larger in size than individual polymer, programming grafted chain conformations can “synthesize” high performance nanocomposites with moduli of ~25GPa and a circular lifecycle without forming and/or breaking chemical bonds. These nanocomposites dissipate external stresses by disentangling and stretching grafted polymers up to ~98% of their contour length, analogous to that of folded proteins; use both polymers and nanoparticles for load bearing; and exhibit a non-linear dependence on composition throughout the microscopic, nanoscopic and single particle levels.

Abstract Fabrication of architected materials through self‐assembly of units offers many advantages over monolithic solids including recyclability, reconfigurability, self‐healing, and diversity of emergent properties – all prescribed chiefly by the choice of the building blocks. While self‐assembly is prevalent in biosynthesis, it remains challenging to recapitulate it macroscopically. Recent success in the self‐assembly of 2D ordered open magneto‐elastic lattices from centimeter‐long bar units with sticky magnetic ends, showcasing graceful failure at “magnetic bonds” and re‐assembly under extreme loading. However, it is still unclear how this approach can be generalized to design units that preferably form ordered low‐energy structures with desirable mechanical properties such as ductility, auxetics, and impact resistance. Here, diverse ordered 2D lattice structures are predicted as the self‐assembly outcomes from units with 2 (bar), 3 (Y‐shape), and 4 (cross) branches with magnetic ends. The defect formation is significantly reduced by a computational design approach. Tunable mechanical behavior is shown to be achieved by varying unit shapes and magnet orientations. Cross‐shaped units are identified for their promise in auxetic response and penetration resistance with these findings validated through experiments. The work highlights the potential of self‐assembling magnetic architected materials for adaptive structures, impact mitigation, and energy adsorption.

Abstract Membranes facilitate scalable and continuous lithium concentration from hypersaline salt lakes and battery leachates. Conventional nanofiltration (NF) membranes, however, exhibit poor monovalent selectivity in high‐salinity environments due to weakened exclusion mechanisms. This study examines polyamide NF membranes coated with polyelectrolytes enriched with ammonium groups to maintain high monovalent cation selectivity in hypersaline conditions. Over 8000 ion rejection measurements are recorded using salt lake brines and battery leachates. The experiments exemplify the coated membrane's ability to reduce magnesium concentrations to 0.14% from salt lakes and elevate lithium purity to 98% from battery leachates, in a single filtration stage. The membrane's selectivity is retained after 12 weeks in acidic conditions. Molecular dynamics analyses reveal that the ammonium groups create an electrostatic barrier at low pH, selectively hindering multivalent cation transport. This is corroborated by the Coulombic attraction between cations and carboxylate groups, along with a repulsive barrier from ammonium groups. Despite a 14.7% increase in specific energy, a two‐stage NF system using the coated membranes for lithium recovery significantly reduces permeate magnesium composition to 0.031% from Chilean salt lake brines. For NMC leachates, the coated membranes achieve permeate lithium purity exceeding 99.5%, yielding enhanced permeate quality with minor increases in energy demands.

Abstract Spider dragline silk is known for its exceptional strength and toughness; hence understanding the link between its primary sequence and mechanics is crucial. Here, we establish a deep-learning framework to clarify this link in dragline silk. The method utilizes sequence and mechanical property data of dragline spider silk as well as enriching descriptors such as residue-level mobility (B-factor) predictions. Our sequence representation captures the relative position, repetitiveness, as well as descriptors of amino acids that serve to physically enrich the model. We obtain high Pearson correlation coefficients (0.76–0.88) for strength, toughness, and other properties, which show that our B-factor based representation outperforms pure sequence-based models or models that use other descriptors. We prove the utility of our framework by identifying influential motifs and demonstrating how the B-factor serves to pinpoint potential mutations that improve strength and toughness, thereby establishing a validated, predictive, and interpretable sequence model for designing tailored biomaterials.

Grafting polymer chains to the surface of nanoparticles overcomes the challenge of nanoparticle dispersion within nanocomposites and establishes high-volume fractions that are found to enable enhanced material mechanical properties. This study utilizes coarse-grained molecular dynamics simulations to quantify how the shear modulus of polymer-grafted nanoparticle (PGN) systems in their glassy state depends on parameters such as strain rate, nanoparticle size, grafting density, and chain length. The results are interpreted through further analysis of the dynamics of chain conformations and volume fraction arguments. The volume fraction of nanoparticles is found to be the most influential variable in deciding the shear modulus of PGN systems. A simple rule of mixture is utilized to express the monotonic dependence of shear modulus on the volume fraction of nanoparticles. Due to the reinforcing effect of nanoparticles, shortening the grafted chains results in a higher shear modulus in PGNs, which is not seen in linear systems. These results offer timely insight into calibrating molecular design parameters for achieving the desired mechanical properties in PGNs.

Branched polymer architectures are used to tune the mechanical properties of impact-resistant thin films through parameters, such as chain length and grafting density. While chain dispersity affects molecular properties, such as interpenetration and entanglements, structure-property relationships accounting for dispersity are challenging to obtain experimentally and are often neglected in computational models. We employ molecular dynamics simulations to model the high-rate tensile elongation and nanoballistic impact of thin films composed of bidisperse star polymers with varying arm lengths. We find that, at fixed molecular weight, high dispersity can significantly enhance the toughness and impact resistance of the films without decreasing their elastic modulus. Bidisperse stars with fewer longer arms are less entangled, but stretch and interpenetrate for longer times during crazing, leading to increased toughness. These findings highlight controlled dispersity as a design strategy to improve the mechanical properties of polymer composites across Pareto fronts.

μ-Ballistic simulations performed on the PGN thin films reveal a positive influence of cohesive energy density on the performance. PGN with heavier nanoparticles arrest bullets more rapidly, however, lighter particles exhibit a higher .

Abstract The lifetime of chemical bonds shortens exponentially with force. Oddly, some protein-ligand complexes called catch bonds exhibit a sharp increase in lifetime when pulled with greater force. Inventing catch bond interfaces in synthetic materials would enable force-enhanced kinetics or self-strengthening under mechanical stress. Here, we present a molecular design that recapitulates catch bond behavior between nanoparticles tethered with macromolecules, consisting of one looped and one straight tether linking particles with weak adhesion. We calibrate the loop stiffness such that it opens around a target force to enable load-sharing among tethers, which facilitates a sequential to coordinated failure transition that reproduces experimental catch bond force-lifetime curve characteristics. We derive an analytical relation validated by molecular simulations to prove that loop and adhesion interactions can be tailored to achieve a spectrum of catch bond lifetime curves with this simple design. Our predictions break new ground towards designing tunable, catch-bond inspired self-strengthening materials.

B factors provide critical insight into protein dynamics. Predicting B factors of an atom in new proteins remains challenging as it is impacted by their neighbors in Euclidean space. Previous learning methods developed have resulted in low Pearson correlation coefficients beyond the training set due to their limited ability to capture the effect of neighboring atoms. With the advances in deep learning methods, we develop a sequence-based model that is tested on 2,442 proteins and outperforms the state-of-the-art models by 30%. We find that the model learns that the B factor of a site is prominently affected by atoms within a 12-15 Å radius, which is in excellent agreement with cutoffs from protein network models. The ablation study revealed that the B factor can largely be predicted from the primary sequence alone. Based on the abovementioned points, our model lays a foundation for predicting other properties that are correlated with the B factor.

exhibits exceptional underwater adhesion to diverse surfaces to the extent that adhesion strength typically exceeds the cohesive strength of the plaque. While sequence effects such as presence of charged residues, metal ion coordination, and high catechol content have been identified to govern fp5's interaction with surfaces, molecular contributors to its cohesive strength remain to be fully understood. Addressing this issue is critical for designing mussel-inspired sequences for new adhesives and biomaterials enabled by synthetic biology. Here we carry out all-atom molecular dynamics simulations on hydrated model fp5 biopolymer melts to understand how sequence features such as tyrosine and charge content affect packing density and inter-residue and ionic interaction strengths and consequently influence the cohesive strength and toughness. Systematic serine (S) substitutions for lysine (K), arginine (R) and tyrosine (Y) residues reveal that Y to S substitution surprisingly results in improvement of cohesive strength due to densification of the material by removal of steric hindrances, whereas the removal of charge in K and R to S substitutions has a detrimental impact on strength and toughness as it reduces cohesive interactions facilitated by electrostatic interactions. Additionally, melts formed from split fp5 sequences with only C or N terminal halves show distinct mechanical responses that further illustrate the role of charge. Our findings provide new insights for designing materials that could potentially surpass the performance of existing biomolecular and bioinspired adhesives, specifically by tailoring sequences for balancing charge and excluded volume effects.

Magneto-Elastic Networks In article number 2202189, Sinan Keten and co-workers propose a magneto-elastic network that can be self-assembled from elastic elements decorated with permanent magnets under random vibrations. This design could be used in applications featuring impact mitigation and energy adsorption, such as the helmet. Under mechanical loading, the fractured or collapsed system could be reassembled into the original functional structure on the fly with random excitation.

Often nanostructures formed by self-assembly of small molecules based on hydrophobic interactions are rather unstable, causing morphological changes or even dissolution when exposed to changes in aqueous media. In contrast, peptides offer precise control of the nanostructure through a range of molecular interactions where physical stability can be engineered in and, to a certain extent, decoupled from size via rational design. Here, we investigate a family of peptides that form beta-sheet nanofibers and demonstrate a remarkable physical stability even after attachment of poly(ethylene glycol). We employed small-angle neutron/X-ray scattering, circular dichroism spectroscopy, and molecular dynamics simulation techniques to investigate the detailed nanostructure, stability, and molecular exchange. The results for the most stable sequence did not reveal any structural alterations or unimer exchange for temperatures up to 85 °C in the biologically relevant pH range. Only under severe mechanical perturbation (i.e., tip sonication) would the fibers break up, which is reflected in a very high activation barrier for unimer exchange of ∼320 kJ/mol extracted from simulations. The results give important insight into the relation between molecular structure and stability of peptide nanostructure that is important for, e.g., biomedical applications.

Improving the toughness of brittle epoxy while keeping its high strength-to-weight ratio is challenging, as these two properties work against each other. Fracture processes are difficult to ascertain with experiments, as they occur at nanoscopic lengths and time scales and require higher efficiency than what can be attained with atomistic simulations. To overcome this challenge, we utilize a recently developed chemistry specific coarse-grained model to examine two different hardeners, diamine 4,4′-methylenebis(cyclohexylamine) (PACM) and propylene oxide diamine (Jeffamine), to cure bisphenol A diglycidyl ether (DGEBA) at varying stoichiometries and understand how hardener composition influences the elasticity, yield strength, and fracture toughness of epoxy resins. The results indicate that PACM mainly contributes to the modulus and that long Jeffamine chains increase fracture toughness by making the epoxy ductile, whereas short Jeffamine chains significantly improve the yield strength. Longer Jeffamines also lead to larger voids and the formation of fibrils that carry a significant amount of stress and contribute to toughness. Interestingly, the Ashby plots reveal that epoxies with intermediate-length Jeffamine chains (D800 and D2000) outperform other systems, as the toughness enhancement from flexible Jeffamine chains and the stiffness due to PACM help to overcome the strength–toughness trade-off. Our modeling framework and findings establish a path toward resin design from predictive multiscale models with no empirical input and reveal new insights into the molecular failure mechanisms of epoxy resins.

Abstract Magneto‐elastic materials facilitate features such as shape programmability, adaptive stiffness, and tunable strength, which are critical for advances in structural and robotic materials. Magneto‐elastic networks are commonly fabricated by employing hard magnets embedded in soft matrices to constitute a monolithic body. These architected network materials have excellent mechanical properties but damage incurred in extreme loading scenarios are permanent. To overcome this limitation, we present a novel design for elastic bars with permanent fixed dipole magnets at their ends and demonstrate their ability to self‐assemble into magneto‐elastic networks under random vibrations. The magneto‐elastic unit configuration, most notably the orientation of end dipoles, is shown to dictate the self‐assembled network topology, which can range from quasi‐ordered triangular lattices to stacks or strings of particles. Network mechanics are probed with uniaxial tensile tests and design criteria for forming stable lightweight 2D networks are established. It is shown that these magneto‐elastic networks rearrange and break gracefully at their magnetic nodes under large excitations and yet recover their original structure at moderate random excitations. This work paves the way for structural materials that can be self‐assembled and repaired on‐the‐fly with random vibrations, and broadens the applications of magneto‐elastic soft materials.

Abstract While most calibration methods focus on inferring a set of model parameters that are unknown but assumed to be constant, many models have parameters that have a functional relation with the controllable input variables. Formulating a low-dimensional approximation of these calibration functions allows modelers to use low-fidelity models to explore phenomena at lengths and time scales unattainable with their high-fidelity sources. While functional calibration methods are available for low-dimensional problems (e.g., one to three unknown calibration functions), exploring high-dimensional spaces of unknown calibration functions (e.g., more than ten) is still a challenging task due to its computational cost and the risk for identifiability issues. To address this challenge, we introduce a semiparametric calibration method that uses an approximate Bayesian computation scheme to quantify the uncertainty in the unknown calibration functions and uses this insight to identify what functions can be replaced with low-dimensional approximations. Through a test problem and a coarse-grained model of an epoxy resin, we demonstrate that the introduced method enables the identification of a low-dimensional set of calibration functions with a limited compromise in calibration accuracy. The novelty of the presented method is the ability to synthesize domain knowledge from various sources (i.e., physical experiments, simulation models, and expert insight) to enable high-dimensional functional calibration without the need for prior knowledge on the class of unknown calibration functions.

We present a missingness-aware fusion network (MAFN) to identify a person’s digital phenotype from continuously measured longitudinal multi-modal wearable data. This work is done as a part of Track 1 of e-Prevention: Person Identification and Relapse Detection from Continuous Recordings of Biosignals Signal Processing Grand Challenge at International Conference on Acoustics, Speech, & Signal Processing (ICASSP) 2023. MAFN achieves an accuracy of 91.36% on test data. Additionally, our experiments confirm findings from previous works that kinetic features derived from the accelerometer in-deed contain more discriminative features for person identification task.