Jingjie Yeo

Electrically conducting polymers with mechanical adaptability are essential for flexible electronics, yet most suffer structural degradation under rapid deformation. In this study, multiscale coarse-grained (MSCG) simulations are used to uncover the nanoscale origins of an unusual strain-rate-dependent stiffening in a poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PAMPSA)-polyaniline (PANI) blend. The self-assembled morphology consists of semicrystalline PANI-rich micellar cores dispersed in a soft, viscoelastic PAMPSA matrix. At low shear rates, micelles migrate and coalesce into larger aggregates, enhancing local crystallinity and transient entanglement density while dissipating stress through matrix deformation. At high shear rates, micelles cannot reorganize quickly enough, leading to core dissociation and the emergence of highly aligned PANI filaments that directly bear the load, with PAMPSA serving as a weak but extended support phase. These contrasting regimes (densification-driven local alignment versus dissociation-driven global alignment) enable rate-dependent mechanical stiffening across 3 orders of magnitude in shear rate. The results provide a molecular-level framework for designing solid-state polymers with tunable, rate-adaptive mechanical properties.

This study aims to develop methods and a toolkit to build realistic polymer melt and network models and understand contributions from individual chains and entanglements to mechanical properties of the models during uniaxial tensile at different conditions. In the study, classical molecular dynamics simulations of united-atom models was primarily used to generate data of mechanical qualities of various polymer systems. The study found that during small and fast uniaxial tensile, polymer melts and networks show similar behaviors and the contributions from different types of entanglements are indistinguishable. The study also identified that contributions from individual chains are more important for performance of polymers at fast deformations. The results of the study laid basis for further research in mechanical properties of complex networks and roles of chain interactions at multiple scales.

Abstract The development of high-performance solid-state electrolytes (SSEs) has entered a critical stage, where entropy-driven strategies offer transformative potential for enhancing electrochemical properties. By engineering local environments for conductive ions alongside introducing disorder, these approaches can significantly improve conductivity. However, embracing high-entropy designs does not always guarantee improved performance. Current entropy descriptions oversimplify disorder by accounting solely for host framework configurations, neglecting conductive ion-induced disorder, rendering such descriptions incomplete. Herein, we propose path entropy ( S p ) as a descriptor that quantifies diffusion pathway diversity, directly capturing diffusional disorder. Combining Markov state model with transition path theory, we reveal the interplay between diffusion pathway diversity of lithium and microscopic local environments in inorganic thiophosphates. Generalizing this path-informative S p for high-throughput screening, we demonstrate its broad applicability in identifying and designing high-performance SSEs. Our work establishes a critical link between entropy evolution underlying ion conduction and practical entropy-driven design principles.

ABSTRACT Biomineralization is a promising approach for producing composite materials that contain living organisms, biopolymers, and minerals. Brittle biomineralized materials can be potentially toughened by more deformable microorganisms and biopolymers. Microbially‐induced carbonate precipitation (MICP) is a common type of biomineralization that occurs when urease‐producing bacteria hydrolyze urea to form calcium carbonate (). MICP is often used in porous materials to improve their mechanical properties. The morphology of the precipitation is strongly influenced by the bacteria and the morphology and porosity of the host medium. To explore their effects on MICP, we constructed an agent‐based discrete element model to investigate clustering and morphology in tandem with the growth of bacterial cells and the secretion of extracellular polymers. The model parameters are validated and optimized via our experimental measurements. We further addressed parametric uncertainties and quantitatively evaluated the relative importance of enzyme secretion, catalytic kinetics, and diffusion dynamics parameters by employing Monte Carlo simulations combined with data‐driven variogram analysis. Based on this model, we also examine the influence of bacterial concentration, bacterial distribution, urea concentration, and the geometry of porous media on the morphology. Unlike prior field‐scale or particle‐based methods that primarily focus on structures, our model will pave the way toward the study of the kinetics of urease‐producing bacteria and the mechanically relevant microstructural features of MICP‐treated composite materials.

Electrolyte-gated transistors with ion-trapping layers offer a promising platform for artificial synapses in neuromorphic computing, yet molecular mechanisms governing ionic retention remain poorly understood. Here, we present a supramolecular approach to modulate ion retention by incorporating a crown ether derivative-based polymer network as an ion-trapping layer on top of a semiconducting monolayer. We show that the balance between ion–host binding and ion–solvent interactions dictates the kinetics of ion capture and release, which in turn controls the memory characteristics of the device. By varying the solvent dielectric constant, we tune the ionic retention time from nearly permanent trapping to rapid relaxation. Intermediate solvent polarity enables programmable short- and long-term synaptic behaviors, including excitatory postsynaptic current, paired-pulse facilitation, and long-term potentiation and depression. These findings establish a direct link between supramolecular ion recognition and synaptic plasticity and provide a generalizable design strategy for ionic–electronic neuromorphic devices.

The metastable A15 \ensuremath{\beta} phase of tungsten exhibits the largest spin Hall angle among all transition metals and is of interest for applications in spintronic devices. However, \ensuremath{\beta}-W is known to support very high stresses in such applications, which may affect properties and reliability. To understand these stresses, the thermomechanical properties must be known. The coefficient of thermal expansion (CTE) and elastic constants of \ensuremath{\beta}-W thin films containing 0--11.1 at. % nitrogen were determined within the ${24}^{\ensuremath{\circ}}\ensuremath{-}100{\phantom{\rule{0.16em}{0ex}}}^{\ensuremath{\circ}}\mathrm{C}$ temperature range using three methods: substrate curvature measurements of thermoelastic curves for films deposited on $\mathrm{Si}\phantom{\rule{0.16em}{0ex}}\ensuremath{\langle}100\ensuremath{\rangle}$ and fused silica substrates, ab initio molecular dynamics (AIMD) simulations, and classical molecular dynamics (MD) simulations using a Spectral Neighbor Analysis Potential fitted from AIMD results using machine learning. Single crystal elastic constants obtained from the simulations were used along with the measured textures of the films to calculate biaxial elastic moduli. Results from all three approaches were consistent. The results of the same methods applied to the well-known BCC \ensuremath{\alpha} phase of W were within 3% of reference values, and those for \ensuremath{\beta}-W are expected to have a similar accuracy. It was found that the CTE of \ensuremath{\beta}-W increases while the biaxial modulus decreases with increasing nitrogen content. The average CTE and biaxial modulus of pure \ensuremath{\beta}-W within ${24}^{\ensuremath{\circ}}\ensuremath{-}100{\phantom{\rule{0.16em}{0ex}}}^{\ensuremath{\circ}}\mathrm{C}$ were determined to be 4.931 $\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}6}{/}^{\ensuremath{\circ}}\mathrm{C}$ and 503 GPa, respectively, which are 10% higher and 11% lower than those of \ensuremath{\alpha}-W.

Publisher of over 50 scientific journals across the life, physical, earth, and health sciences, both independently and in partnership with scientific societies including Cell, Neuron, Immunity, Current Biology, AJHG, and the Trends Journals.

Developing refractory high-entropy superalloys (RSAs) with performance advantages over nickel-based alloys is a critical frontier in materials science. Body-centered cubic (bcc)-based RSAs have attracted significant attention, with ruthenium (Ru) playing a key role in forming two-phase regions of A2 (disordered bcc) + B2 (ordered bcc), which could lead to superalloy-like microstructures. This study introduces the application of the Kolmogorov-Arnold Network (KAN) model to predict the mechanical and thermodynamic properties of Ru while comparing its performance against other commonly used machine-learned models. Utilizing density functional theory calculations as training data, the KAN model demonstrates superior accuracy and computational efficiency compared to conventional methods, while reducing descriptor complexity. The model accurately predicts a range of properties, including elastic constants, thermal expansion coefficients, and various moduli, with discrepancies within 6% of experimental reference data. Molecular dynamics simulations further validate the model’s efficacy, accurately capturing Ru’s phase transitions from hexagonal close-packed (hcp) to face-centered cubic structure and the melting point. This work presents the first application of KAN in materials science, demonstrating how its balanced performance and efficiency provide a new pathway for designing advanced materials, with unique advantages over conventional machine learning approaches in predicting material properties.

Inspired by the flimmer hairs found on the flagella of certain species of choanoflagellates, we show in this paper that nanoscale flagella hair on slender flagellum surfaces can drive flow with nanoscale motion. Using molecular dynamics, we provide numerical proof that the nanoscale hairs, moving in a biased periodic motion, can attain high water flow rates in excess of 1200 [Formula: see text]m 3 ⋅ s[Formula: see text]. This flow rate is on par with the experimentally measured flow rates of natural sponges, which are known to be capable of exceptionally high pumping efficiency. This paper highlights the potential of using collective motion of nanohairs to pump fluid and suggests a range of parameters of the force function that can achieve significant flow.

The development of high-performance solid-state electrolytes (SSEs) has entered a critical stage, where entropy-driven strategies offer transformative potential for enhancing electrochemical properties. By engineering local environments for conductive ions alongside introducing disorder, these approaches can significantly improve conductivity. However, embracing high-entropy designs does not always guarantee improved performance. Current entropy descriptions oversimplify disorder by accounting solely for host framework configurations, neglecting conductive ion-induced disorder, rendering such descriptions incomplete. Herein, we propose path entropy (Sp) as a descriptor that quantifies diffusion pathway diversity, directly capturing diffusional disorder. Combining Markov state model with transition path theory, we reveal the interplay between diffusion pathway diversity of lithium and microscopic local environments in inorganic thiophosphates. Generalizing this path-informative Sp for high-throughput screening, we demonstrate its broad applicability in identifying and designing high-performance SSEs. Our work establishes a critical link between entropy evolution underlying ion conduction and practical entropy-driven design principles.

Programmable Protein Actuators In article 2401562, Wenwen Huang and co-workers develop a bioinspired strategy that synergistically combines genetic and chemical engineering approaches to program biological hydrogel actuators. Current work offers a more versatile and bio-friendly platform for the de novo design of protein actuators, toward biomimetic living materials.

We report a new supramolecular porous crystal assembled from fused macrocycle-cage molecules. The molecule comprises a prismatic cage with three macrocycles radially attached. The molecules form a nanoporous crystal with one-dimensional (1D) nanochannels. The supramolecular porous crystal can take up lithium-ion electrolytes and achieve an ionic conductivity of up to 8.3 × 10 –4 S/cm. Structural analysis and density functional theory calculations reveal that efficient Li-ion electrolyte uptake, the presence of 1D nanochannels, and weak interactions between lithium ions and the crystal enable fast lithium-ion transport. Our findings demonstrate the potential of fused macrocycle-cage molecules as a new design motif for ion-conducting molecular crystals.

Protein hydrogels with tailored stimuli-responsive features and tunable stiffness have garnered considerable attention due to the growing demand for biomedical soft robotics. However, integrating multiple responsive features toward intelligent yet biocompatible actuators remains challenging. Here, a facile approach that synergistically combines genetic and chemical engineering for the design of protein hydrogel actuators with programmable complex spatial deformation is reported. Genetically engineered silk-elastin-like proteins (SELPs) are encoded with stimuli-responsive motifs and enzymatic crosslinking sites via simulation-guided genetic engineering strategies. Chemical modifications of the recombinant proteins are also used as secondary control points to tailor material properties, responsive features, and anisotropy in SELP hydrogels. As a proof-of-concept example, diazonium coupling chemistry is exploited to incorporate sulfanilic acid groups onto the tyrosine residues in the elastin domains of SELPs to achieve patterned SELP hydrogels. These hydrogels can be programmed to perform various actuations, including controllable bending, buckling, and complex deformation under external stimuli, such as temperature, ionic strength, or pH. With the inspiration of genetic and chemical engineering in natural organisms, this work offers a predictable, tunable, and environmentally sustainable approach for the fabrication of programmed intelligent soft actuators, with implications for a variety of biomedical materials and biorobotics needs.

The gastrointestinal tract is inhabited by a vast community of microorganisms, termed the gut microbiota. Large colonies can pose a health threat but the gastrointestinal mucus system protects epithelial cells from microbiota invasion. The human colon features a bilayer of mucus lining. Due to imbalances in intestinal homeostasis, bacteria may successfully penetrate the inner mucus layer which can lead to severe gut diseases. However, it is hard to tease apart the competing mechanisms that lead to this penetration. To probe the conditions that permit bacteria penetration into the inner mucus layer, we develop an agent-based model consisting of bacteria and an inner mucus layer subject to a constant flux of nutrient fields feeding the bacteria. We find that there are three important variables that determine bacterial invasion: the bacterial reproduction rate, the contact energy between bacteria and mucus, and the rate of bacteria degrading the mucus. Under healthy conditions, all bacteria are naturally eliminated by the constant removal of mucus. In diseased states, imbalances between the rates of bacterial degradation and mucus secretion lead to bacterial invasion at certain junctures. We conduct uncertainty quantification and sensitivity analysis to compare the relative impact between these parameters. The contact energy has the strongest influence on bacterial penetration which, in combination with bacterial degradation rate and growth rate, greatly accelerates bacterial invasion of the human gut mucus lining. Our findings will serve as predictive indicators for the etiology of intestinal diseases and highlight important considerations when developing gut therapeutics.

Chemical permeation enhancers (CPEs) represent a prevalent and safe strategy to enable noninvasive drug delivery across skin-like biological barriers such as the tympanic membrane (TM). While most existing CPEs interact strongly with the lipid bilayers in the stratum corneum to create defects as diffusion paths, their interactions with the delivery system, such as polymers forming a hydrogel, can compromise gelation, formulation stability, and drug diffusion. To overcome this challenge, differing interactions between CPEs and the hydrogel system are explored, especially those with sodium dodecyl sulfate (SDS), an ionic surfactant and a common CPE, and those with methyl laurate (ML), a nonionic counterpart with a similar length alkyl chain. Notably, the use of ML effectively decouples permeation enhancement from gelation, enabling sustained delivery across TMs to treat acute otitis media (AOM), which is not possible with the use of SDS. Ciprofloxacin and ML are shown to form a pseudo-surfactant that significantly boosts transtympanic permeation. The middle ear ciprofloxacin concentration is increased by 70-fold in vivo in a chinchilla AOM model, yielding superior efficacy and biocompatibility than the previous highest-performing formulation. Beyond improved efficacy and biocompatibility, this single-CPE formulation significantly accelerates its progression toward clinical deployment.

The gastrointestinal (GI) tract's mucus layer serves as a critical barrier and a mediator in drug nanoparticle delivery. The mucus layer's diverse molecular structures and spatial complexity complicates the mechanistic study of the diffusion dynamics of particulate materials. In response, we developed a bi-component coarse-grained mucus model, specifically tailored for the colorectal cancer environment, that contained the two most abundant glycoproteins in GI mucus: Muc2 and Muc5AC. This model demonstrated the effects of molecular composition and concentration on mucus pore size, a key determinant in the permeability of nanoparticles. Using this computational model, we investigated the diffusion rate of polyethylene glycol (PEG) coated nanoparticles, a widely used muco-penetrating nanoparticle. We validated our model with experimentally characterized mucus pore sizes and the diffusional coefficients of PEG-coated nanoparticles in the mucus collected from cultured human colorectal goblet cells. Machine learning fingerprints were then employed to provide a mechanistic understanding of nanoparticle diffusional behavior. We found that larger nanoparticles tended to be trapped in mucus over longer durations but exhibited more ballistic diffusion over shorter time spans. Through these discoveries, our model provides a promising platform to study pharmacokinetics in the GI mucus layer.

The Lab performs Computations in Materials and the Sciences to engineer dynamically-responsive, living materials through interdisciplinary studies of material and biological phenomena with multiscale computational methods for engineering and medical applications.He is a co-instructor in Station1, a social nonprofit organization dedicated to building the foundations of the university of the future through educational opportunity and socially-directed frontier STEM education, research, and internships.Before joining Cornell University in 2020, Prof. Yeo was a research scientist in the Institute of High Performance Computing,

Machine learning-based inverse materials discovery has attracted enormous attention recently due to its flexibility in dealing with black box models. Yet, many metaheuristic algorithms are not as widely applied to materials discovery applications as machine learning methods. There are ongoing challenges in applying different optimization algorithms to discover materials with single- or multi-elemental compositions and how these algorithms differ in mining the ideal materials. We comprehensively compare 11 different optimization algorithms for the design of single- and multi-elemental crystals with targeted properties. By maximizing the bulk modulus and minimizing the Fermi energy through perturbing the parameterized elemental composition representations, we estimated the unique counts of elemental compositions, mean density scan of the objectives space, mean objectives, and frequency distributed over the materials’ representations and objectives. We found that nature-inspired algorithms contain more uncertainties in the defined elemental composition design tasks, which correspond to their dependency on multiple hyperparameters. Runge–Kutta optimization (RUN) exhibits higher mean objectives, whereas Bayesian optimization (BO) displayed low mean objectives compared with other methods. Combined with materials count and density scan, we propose that BO strives to approximate a more accurate surrogate of the design space by sampling more elemental compositions and hence have lower mean objectives, yet RUN will repeatedly sample the targeted elemental compositions with higher objective values. Our work sheds light on the automated digital design of materials with single- and multi-elemental compositions and is expected to elicit future studies on materials optimization, such as composite and alloy design based on specific desired properties.

Positioned within the eye, the lens supports vision by transmitting and focusing light onto the retina. As an adaptive glassy material, the lens is constituted primarily by densely-packed, polydisperse crystallin proteins that organize to resist aggregation and crystallization at high volume fractions, yet the details of how crystallins coordinate with one another to template and maintain this transparent microstructure remain unclear. The role of individual crystallin subtypes (α, β, and γ) and paired subtype compositions, including how they experience and resist crowding-induced turbidity in solution, is explored using combinations of spectrophotometry, hard-sphere simulations, and surface pressure measurements. After assaying crystallin combinations, β-crystallins emerged as a principal component in all mixtures that enabled dense fluid-like packing and short-range order necessary for transparency. These findings helped inform the design of lens-like hydrogel systems, which are used to monitor and manipulate the loss of transparency under different crowding conditions. When taken together, the findings illustrate the design and characterization of adaptive materials made from lens proteins that can be used to better understand mechanisms regulating transparency.

Due to their remarkable mechanical and chemical properties, Ti–Al-based materials are attracting considerable interest in numerous fields of engineering, such as automotive, aerospace, and defense. With their low density, high strength, and resistance to corrosion and oxidation, these intermetallic alloys and metal-compound composites have found diverse applications. However, additive manufacturing and heat treatment of Ti–Al alloys frequently lead to brittleness and severe formation of defects. The present study delves into the interfacial dynamics of these Ti–Al systems, particularly focusing on the behavior of Ti and Al atoms in the presence of TiAl 3 grain boundaries under experimental heat treatment conditions. Using a combination of molecular dynamics and Markov state modeling, we scrutinize the kinetic processes involved in the formation of TiAl 3 . The molecular dynamics simulation indicates that at the early stage of heat treatment, the predominating process is the diffusion of Al atoms toward the Ti surface through the TiAl 3 grain boundaries. Markov state modeling identifies three distinct dynamic states of Al atoms within the Ti/Al mixture that forms during the process, each exhibiting a unique spatial distribution. Using transition time scales as a qualitative measure of the rapidness of the dynamics, it is observed that the Al dynamics is significantly less rapid near the Ti surface compared to the Al surface. Put together, the results offer a comprehensive understanding of the interfacial dynamics and reveal a three-stage diffusion mechanism. The process initiates with the premelting of Al, proceeds with the prevalent diffusion of Al atoms toward the Ti surface, and eventually ceases as the Ti concentration within the mixture progressively increases. The insights gained from this study could contribute significantly to the control and optimization of manufacturing processes for these high-performing Ti–Al-based materials.

Biofilm growth and transport in confined systems frequently occur in natural and engineered systems. Designing customizable engineered porous materials for controllable biofilm transportation properties could significantly improve the rapid utilization of biofilms as engineered living materials for applications in pollution alleviation, material self-healing, energy production, and many more. We combine Bayesian optimization (BO) and individual-based modeling to conduct design optimizations for maximizing different porous materials' (PM) biofilm transportation capability. We first characterize the acquisition function in BO for designing 2-dimensional porous membranes. We use the expected improvement acquisition function for designing lattice metamaterials (LM) and 3-dimensional porous media (3DPM). We find that BO is 92.89% more efficient than the uniform grid search method for LM and 223.04% more efficient for 3DPM. For all three types of structures, the selected characterization simulation tests are in good agreement with the design spaces approximated with Gaussian process regression. All the extracted optimal designs exhibit better biofilm growth and transportability than unconfined space without substrates. Our comparison study shows that PM stimulates biofilm growth by taking up volumetric space and pushing biofilms' upward growth, as evidenced by a 20% increase in bacteria cell numbers in unconfined space compared to porous materials, and 128% more bacteria cells in the target growth region for PM-induced biofilm growth compared with unconfined growth. Our work provides deeper insights into the design of substrates to tune biofilm growth, analyzing the optimization process and characterizing the design space, and understanding biophysical mechanisms governing the growth of biofilms.

Topological materials are at the forefront of quantum materials research, offering tremendous potential for next-generation energy and information devices. However, current investigation of these materials remains largely focused on performance and often neglects the crucial aspect of sustainability. Recognizing the pivotal role of sustainability in addressing global pollution, carbon emissions, resource conservation, and ethical labor practices, we present a comprehensive evaluation of topological materials based on their sustainability and environmental impact. Our approach involves a hierarchical analysis encompassing cost, toxicity, energy demands, environmental impact, social implications, and resilience to imports. By applying this framework to over 16,000 topological materials, we establish a sustainable topological materials database. Our endeavor unveils environmental-friendly topological materials candidates which have been previously overlooked, providing insights into their environmental ramifications and feasibility for industrial scalability. The work represents a critical step toward industrial adoption of topological materials, offering the potential for significant technological advancements and broader societal benefits.

Lithium-sulfur batteries represent an attractive option for energy storage applications. A deeper understanding of the multistep lithium-sulfur reactions and the electrocatalytic mechanisms are required to develop advanced, high-performance batteries. We have systematically investigated the lithium-sulfur redox processes catalyzed by a cobalt single-atom electrocatalyst (Co-SAs/NC) via operando confocal Raman microscopy and x-ray absorption spectroscopy (XAS). The real-time observations, based on potentiostatic measurements, indicate that Co-SAs/NC efficiently accelerates the lithium-sulfur reduction/oxidation reactions, which display zero-order kinetics. Under galvanostatic discharge conditions, the typical stepwise mechanism of long-chain and intermediate-chain polysulfides is transformed to a concurrent pathway under electrocatalysis. In addition, operando cobalt K-edge XAS studies elucidate the potential-dependent evolution of cobalt's oxidation state and the formation of cobalt-sulfur bonds. Our work provides fundamental insights into the mechanisms of catalyzed lithium-sulfur reactions via operando methods, enabling a deeper understanding of electrocatalysis and interfacial dynamics in electrical energy storage systems.

Amorphous silica (a-SiO 2 ) is a widely used inorganic material. Interestingly, the relationship between the local atomic structures of a-SiO 2 and their effects on ductility and fracture is seldom explored. Here, we combine large-scale molecular dynamics simulations and machine learning methods to examine the molecular deformations and fracture mechanisms of a-SiO 2 . By quenching at high pressures, we demonstrate that densifying a-SiO 2 increases the ductility and toughness. Through theoretical analysis and simulation results, we find that changes in local bonding topologies greatly facilitate energy dissipation during plastic deformation, particularly if the coordination numbers decrease. The appearance of fracture can then be accurately located based on the spatial distribution of the atoms. We further observe that the static unstrained structure encodes the propensity for local atomic coordination to change during applied strain, hence a distinct connection can be made between the initial atomic configurations before loading and the final far-from-equilibrium atomic configurations upon fracture. These results are essential for understanding how atomic arrangements strongly influence the mechanical properties and structural features in amorphous solids and will be useful in atomistic design of functional materials.

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTSpecial Issue on Engineering Bioinspired and Biological MaterialsJingjie Yeo*Jingjie Yeo*E-mail: [email protected]More by Jingjie Yeohttps://orcid.org/0000-0002-6462-7422, Rong YangRong YangMore by Rong Yanghttps://orcid.org/0000-0001-6427-026X, Ellan SperoEllan SperoMore by Ellan Spero, and Shengjie LingShengjie LingMore by Shengjie Linghttps://orcid.org/0000-0003-1156-0479Cite this: ACS Biomater. Sci. Eng. 2023, 9, 7, 3725–3728Publication Date (Web):July 10, 2023Publication History Received14 June 2023Published online10 July 2023Published inissue 10 July 2023https://pubs.acs.org/doi/10.1021/acsbiomaterials.3c00786https://doi.org/10.1021/acsbiomaterials.3c00786editorialACS PublicationsCopyright © Published 2023 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views942Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) Get e-AlertscloseSUBJECTS:Bioengineering and biotechnology,Biomaterials,Cells,Interfaces,Materials Get e-Alerts

Due to their remarkable mechanical and chemical properties, Ti-Al based materials are attracting considerable interest in numerous fields of engineering, such as automotive, aerospace, and defense. With their low density, high strength, and resistance to corrosion and oxidation, these intermetallic alloys and compound metal-metallic composites have found diverse applications. The present study delves into the interfacial dynamics of these Ti-Al systems, particularly focusing on the behavior of Ti and Al atoms in the presence of TiAl$_3$ grain boundaries under experimental heat treatment conditions. Using a combination of Molecular Dynamics and Markov State Model analyses, we scrutinize the kinetic processes involved in the formation of TiAl$_3$. The Molecular Dynamics simulation indicates that at the early stage of heat treatment, the predominating process is the diffusion of Al atoms towards the Ti surface through the TiAl$_3$ grain boundaries. The Markov State Modeling identifies three distinct dynamic states of Al atoms within the Ti/Al mixture that forms during the process, each exhibiting a unique spatial distribution. Using transition timescales as a qualitative measure of the rapidness of the dynamics, it is observed that the Al dynamics is significantly less rapid near the Ti surface compared to the Al surface. Put together, the results offer a comprehensive understanding of the interfacial dynamics and reveals a three-stage diffusion mechanism. The process initiates with the premelting of Al, proceeds with the prevalent diffusion of Al atoms towards the Ti surface, and eventually ceases as the Ti concentration within the mixture progressively increases. The insights gained from this study could contribute significantly to the control and optimization of manufacturing processes for these high-performing Ti-Al based materials.

Biofilm growth and transport in confined systems frequently occur in natural and engineered systems. Designing customizable engineered porous materials for controllable biofilm transportation properties could significantly improve the rapid utilization of biofilms as engineered living materials for applications in pollution alleviation, material self-healing, energy production, and many more. We combine Bayesian optimization (BO) and individual-based modeling to conduct design optimizations for maximizing different porous materials' (PM) biofilm transportation capability. We first characterize the acquisition function in BO for designing 2-dimensional porous membranes. We use the expected improvement acquisition function for designing lattice metamaterials (LM) and 3-dimensional porous media (3DPM). We find that BO is 92.89% more efficient than the uniform grid search method for LM and 223.04% more efficient for 3DPM. For all three types of structures, the selected characterization simulation tests are in good agreement with the design spaces approximated with Gaussian process regression. All the extracted optimal designs exhibit better biofilm growth and transportability than unconfined space without substrates. Our comparison study shows that PM stimulates biofilm growth by taking up volumetric space and pushing biofilms' upward growth, as evidenced by a 20% increase in bacteria cell numbers in unconfined space compared to porous materials, and 128% more bacteria cells in the target growth region for PM-induced biofilm growth compared with unconfined growth. Our work provides deeper insights into the design of substrates to tune biofilm growth, analyzing the optimization process and characterizing the design space, and understanding biophysical mechanisms governing the growth of biofilms.

Abstract Materials are more easily damaged during accidents that involve rapid deformation. Here, a design strategy is described for electronic materials comprised of conducting polymers that defies this orthodox property, making their extensibility and toughness dynamically adaptive to deformation rates. This counterintuitive property is achieved through a morphology of interconnected nanoscopic core–shell micelles, where the chemical interactions are stronger within the shells than the cores. As a result, the interlinked shells retain material integrity under strain, while the rate of dissociation of the cores controls the extent of micelle elongation, which is a process that adapts to deformation rates. A prototype based on polyaniline shows a 7.5‐fold increase in ultimate elongation and a 163‐fold increase in toughness when deformed at increasing rates from 2.5 to 10 000% min −1 . This concept can be generalized to other conducting polymers and highly conductive composites to create “self‐protective” soft electronic materials with enhanced durability under dynamic movement or deformation.

Sampling of potential energy surfaces (PES) lies at the core of soft matter self-assembly and material property characterization using molecular simulations. Yet, PES roughness and poor sampling efficiency must be overcome with advanced molecular dynamics (MD) techniques. We introduce the PES concept and detail the current PES-sampling progress using multiscale simulations, including methods in quantum mechanics, all-atomistic MD, and coarse-grained MD. Advanced techniques such as data-driven, machine-learned, and statistical-modeling methods are also introduced. We then focus on several enhanced sampling methods to accurately characterize the PES for probing the material properties of supramolecular soft matter. A variety of simulation software is also showcased to guide hands-on implementation. Finally, we summarize some recent characterization of soft material phenomena using MD methods with enhanced PES sampling. This chapter overviews methods for PES sampling and aims to stimulate broader accessibility of enhanced PES sampling of soft matter self-assembly and characterization.

Ionically conductive polymers are commonly made of monomers containing high polarity moieties to promote high ion dissociation, like poly(ethylene oxide) (PEO), polyvinylidene difluoride (PVDF), and poly(vinyl alcohol) (PVA). However, the glass transition temperatures ( T g ) of these polymers are relatively high, and therefore, these polymers are in a glassy state at room temperature, which limits the mechanical flexibility of the material. Although polydimethylsiloxane (PDMS) has many attractive physical and chemical properties, including a low glass transition temperature, mechanical flexibility, and good biocompatibility, its low dielectric constant suppresses ion dissociation. In this Letter, we overcome this shortcoming by functionalizing the PDMS with ligands that can form labile coordination with metal ions, which greatly promotes ion dissociation and improves the ionic conductivity by orders of magnitude. By combining an experimental study with a fully atomistic molecular dynamics simulation, we systematically investigated the ion transport mechanisms in this low T g material.

Amorphous silica ($a-SiO_2$) is a widely used inorganic material. Interestingly, the relationship between the local atomic structures of $a-SiO_2$ and their effects on ductility and fracture is seldom explored. Here, we combine large-scale molecular dynamics simulations and machine learning methods to examine the molecular deformations and fracture mechanisms of $a-SiO_2$. By quenching at high pressures, we demonstrate that densifying $a-SiO_2$ increases the ductility and toughness. Through theoretical analysis and simulation results, we find that changes in local bonding topologies greatly facilitate energy dissipation during plastic deformation, particularly if the coordination numbers decrease. The appearance of fracture can then be accurately located based on the spatial distribution of the atoms. We further observe that the static unstrained structure encodes the propensity for local atomic coordination to change during applied strain, hence a distinct connection can be made between the initial atomic configurations before loading and the final far-from-equilibrium atomic configurations upon fracture. These results are essential for understanding how atomic arrangements strongly influence the mechanical properties and structural features in amorphous solids and will be useful in atomistic design of functional materials.

A transition from a linear to a circular economy is the only alternative to reduce current pressures in natural resources. Our society must redefine our material sources, rethink our supply chains, improve our waste management, and redesign materials and products. Valorizing extensively available biomass wastes, as new carbon mines, and developing biobased materials that mimic nature's efficiency and wasteless procedures are the most promising avenues to achieve technical solutions for the global challenges ahead. Advances in materials processing, and characterization, as well as the rise of artificial intelligence, and machine learning, are supporting this transition to a new materials' mining. Location, cultural, and social aspects are also factors to consider. This perspective discusses new alternatives for carbon mining in biomass wastes, the valorization of biomass using available processing techniques, and the implementation of computational modeling, artificial intelligence, and machine learning to accelerate material's development and process engineering.

The thermo-mechanical coupling mechanism of graphene fracture under thermal gradients possesses rich applications whereas is hard to study due to its coupled non-equilibrium nature. We employ non-equilibrium molecular dynamics to study the fracture of graphene by applying a fixed strain rate under different thermal gradients by employing different potential fields. It is found that for AIREBO and AIREBO-M, the fracture stresses do not strictly follow the positive correlations with the initial crack length. Strain-hardening effects are observed for “REBO-based” potential models of small initial defects, which is interpreted as blunting effect observed for porous graphene. The temperature gradients are observed to not show clear relations with the fracture stresses and crack propagation dynamics. Quantized fracture mechanics verifies our molecular dynamics calculations. We provide a unique perspective that the transverse bond forces share the loading to account for the nonlinear increase of fracture stress with shorter crack length. Anomalous kinetic energy transportation along crack tips is observed for “REBO-based” potential models, which we attribute to the high interatomic attractions in the potential models. The fractures are honored to be more “brittle-liked” carried out using machine learning interatomic potential (MLIP), yet incapable of simulating post fracture dynamical behaviors. The mechanical responses using MLIP are observed to be not related to temperature gradients. The temperature configuration of equilibration simulation employing the dropout uncertainty neural network potential with a dropout rate of 0.1 is reported to be the most accurate compared with the rest. This work is expected to inspire further investigation of non-equilibrium dynamics in graphene with practical applications in various engineering fields.

Abstract A thick layer of mucus covering the gastrointestinal tract acts as an innate barrier guarding the epithelial surface. The high molecular weight and cross-linked glycoproteins (mucins), the major building blocks of mucus, can effectively obstruct or trap invading noxious substances, such as detrimental bacteria and virus. The mucus layer as well as any trapped material can be regularly removed by the friction force from food flow and gastrointestinal peristalsis, the process of which primarily relies on the viscoelastic and shear-thinning properties. Conversely, the process by which beneficial substances, such as drug nanoparticles, cross the mucus layer and contact the epithelium is also influenced by the chemical and rheological properties of the mucus layer. Gastrointestinal disorders, most notably colitis, are often accompanied by changes to the mucosal structure. In this study, we experimentally characterized the viscoelasticity and dynamic viscosity of mucus collected from human intestinal cells. In addition, we developed a bi-component mesoscopic-scale mucus model that contained Muc2, the dominant mucin secreted in healthy individuals, and Muc5AC, which is secreted by intestinal goblet cells in certain intestinal disorders. This model enabled us to study the effects of cross-linking and mucin concentration on rheological properties of mucus. Furthermore, we quantified changes in the diffusion dynamics of nanoparticles in mucus networks caused by factors such as the size of nanoparticles, nanoparticle-mucin interactions, and the degree of mucin cross-linking.

Solid polymer electrolytes (SPEs) offer a safer battery electrolyte alternative but face design challenges. This review highlights applications of machine learning alongside theory-based models to improve SPE design.