Solid-supported amines having low molecular weight branched poly(ethylenimine) (PEI) physically impregnated into porous solid supports are promising adsorbents for CO2 capture. Co-impregnating short-chain poly(ethylene glycol) (PEG) together with PEI alters the performance of the adsorbent, delivering improved amine efficiency (AE, mol CO2 sorbed / mol N) and faster CO2 uptake rates. To uncover the physical basis for this improved gas capture performance, we probed the distribution and mobility of the polymers in the pores via small angle neutron scattering (SANS), solid-state NMR, and molecular dynamic (MD) simulation studies. SANS and MD simulations reveal that PEG displaces wall-bound PEI, making amines more accessible for CO2 sorption. Solid-state NMR and MD simulation suggest intercalation of PEG into PEI domains, separating PEI domains and reducing amine-amine interactions, providing potential PEG-rich and amine-poor interfacial domains that bind CO2 weakly via physisorption while providing facile pathways for CO2 diffusion. Contrary to a prior literature hypothesis, no evidence is obtained for PEG facilitating PEI mobility in solid supports. Instead, the data suggest that PEG chains coordinate to PEI, form larger bodies with reduced mobility compared to PEI alone. We also demonstrate promising CO2 uptake and desorption kinetics at varied temperatures, given by favorable amine distribution.
Cell membranes are responsible for a range of biological processes that require interactions between lipids and proteins. While the effects of lipids on proteins are becoming better understood, our knowledge of how protein conformational changes influence membrane dynamics remains rudimentary. Here, we performed experiments and computer simulations to study the dynamic response of a lipid membrane to changes in the conformational state of pH-low insertion peptide (pHLIP), which transitions from a surface-associated (SA) state at neutral or basic pH to a transmembrane (TM) α-helix under acidic conditions. Our results show that TM-pHLIP significantly slows down membrane thickness fluctuations due to an increase in effective membrane viscosity. Our findings suggest a possible membrane regulatory mechanism, where the TM helix affects lipid chain conformations, and subsequently alters membrane fluctuations and viscosity.
Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatibilization, chemical transformations, and separations. s-BCPs have chain architectures where three or more linear diblock copolymer arms comprised of two chemically distinct linear polymers, e.g., solvophobic and solvophilic chains, are covalently joined at one point. The chemical composition of each of the subunit polymer chains comprising the arms, their molecular weights, and the number of arms can be varied to tailor the surface and interfacial activity of these architecturally unique molecules. This makes identification of the optimal s-BCP design nontrivial as the total number of plausible s-BCP architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with a reinforcement learning-based Monte Carlo tree search (MCTS) to identify s-BCP designs that minimize the interfacial tension between polar and nonpolar solvents. We first validate the MCTS approach for the design of small- and medium-sized s-BCPs and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified by using the configurations obtained from MD simulations. Chemical insights into the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provides important groundwork for future experimental investigation of s-BCPs for various applications.
A method for characterizing the topological fluctuations in liquids is proposed. This approach exploits the concept of the weighted gyration tensor of a collection of particles and permits the definition of a local configurational unit (LCU). The first principal axis of the gyration tensor serves as the director of the LCU, which can be tracked and analyzed by molecular dynamics simulations. Analysis of moderately supercooled Kob-Andersen mixtures suggests that orientational relaxation of the LCU closely follows viscoelastic relaxation and exhibits a two-stage behavior. The slow relaxing component of the LCU corresponds to the structural, Maxwellian mechanical relaxation. Additionally, it is found that the mean curvature of the LCUs is approximately zero at the Maxwell relaxation time with the Gaussian curvature being negative. This observation implies that structural relaxation occurs when the configurationally stable and destabilized regions interpenetrate each other in a bicontinuous manner. Finally, the mean and Gaussian curvatures of the LCUs can serve as reduced variables for the shear stress correlation, providing a compelling proof of the close connection between viscoelastic relaxation and topological fluctuations in glass-forming liquids.
We present a streamlined method to covalently bond hydroxylated carbon nanotubes (CNOH) within a polyphenol matrix, all achieved through a direct, solvent-free process. Employing an extremely small concentration of CNOH (0.01% w/w) along with topologically contrasting linkers led to a maximum of 5-fold increase in modulus and a 25% enhancement in tensile strength compared to the unaltered matrix, an order of magnitude greater reinforcement (w/w) compared to state-of-the-art melt-processed nanocomposites. Through dynamic mechanical analysis, low field solid-state nuclear magnetic resonance spectroscopy, and molecular dynamics simulations, we uncovered the profound influence of linker's conformational degrees of freedom on the segmental dynamics and therefore the material's properties.
HYPOTHESIS: The formation of distorted lamellar phases, distinguished by their arrangement of crumpled, stacked layers, is frequently accompanied by the disruption of long-range order, leading to the formation of interconnected network structures commonly observed in the sponge phase. Nevertheless, traditional scattering functions grounded in deterministic modeling fall short of fully representing these intricate structural characteristics. Our hypothesis posits that a deep learning method, in conjunction with the generalized leveled wave approach used for describing structural features of distorted lamellar phases, can quantitatively unveil the inherent spatial correlations within these phases. EXPERIMENTS AND SIMULATIONS: This report outlines a novel strategy that integrates convolutional neural networks and variational autoencoders, supported by stochastically generated density fluctuations, into a regression analysis framework for extracting structural features of distorted lamellar phases from small angle neutron scattering data. To evaluate the efficacy of our proposed approach, we conducted computational accuracy assessments and applied it to the analysis of experimentally measured small angle neutron scattering spectra of AOT surfactant solutions, a frequently studied lamellar system. FINDINGS: The findings unambiguously demonstrate that deep learning provides a dependable and quantitative approach for investigating the morphology of wide variations of distorted lamellar phases. It is adaptable for deciphering structures from the lamellar to sponge phase including intermediate structures exhibiting fused topological features. This research highlights the effectiveness of deep learning methods in tackling complex issues in the field of soft matter structural analysis and beyond.
Recently, two-dimensional (2D) FeSe-like anti-MXenes (or XMenes), composed of late d-block transition metal M and p-block nonmetal X elements, have been both experimentally and theoretically investigated. Here, we select three 2D borides FeB, CoB and IrB for a deeper investigation by including strong correlation effects, as a fertile ground for understanding and applications. Using a combination of Hubbard corrected first-principles calculations and Monte Carlo simulations, FeB and CoB are found to be ferro- and anti-ferro magnetic, contrasting with the non-magnetic nature of IrB. The metallic FeB XMene monolayer, superior to most of the MXenes or MBenes, exhibits robust ferromagnetism, driven by intertwined direct-exchange and super-exchange interactions between adjacent Fe atoms. The predicted Curie temperature (TC) of the FeB monolayer via the Heisenberg model reaches an impressive 425 K, with the easy-axis oriented out-of-plane and high magnetic anisotropic energy (MAE). The asymmetry in the spin-resolved transmission spectrum induces a thermal spin current, providing an opportunity for spin filtration. This novel 2D FeB material is expected to hold great promise as an information storage medium and find applications in emerging spintronic devices.
We present results from explicit-solvent coarse-grained molecular dynamics (MD) simulations of fully charged, salt-free, and unentangled polyelectrolytes in semidilute solutions. The inclusion of a polar solvent in the model allows for a more physical representation of these solutions at concentrations, where the assumptions of a continuum dielectric medium and screened hydrodynamics break down. The collective dynamic structure factor of polyelectrolytes, S(q, t), showed that at [Formula: see text], where [Formula: see text] is the polyelectrolyte peak in the structure factor S(q) and [Formula: see text] is the correlation length, the relaxation time obtained from fits to stretched exponential was [Formula: see text], which describes unscreened Zimm-like dynamics. This is in contrast to implicit-solvent simulations using a Langevin thermostat where [Formula: see text]. At [Formula: see text], a crossover region was observed that eventually transitions to another inflection point [Formula: see text] at length scales larger than [Formula: see text] for both implicit- and explicit-solvent simulations. The simulation results were also compared to scaling predictions for correlation length, [Formula: see text], specific viscosity, [Formula: see text], and diffusion coefficient, [Formula: see text], where [Formula: see text] is the polyelectrolyte concentration. The scaling prediction for [Formula: see text] holds; however, deviations from the predictions for [Formula: see text] and D were observed for systems at higher [Formula: see text], which are in qualitative agreements with recent experimental results. This study highlights the importance of explicit-solvent effects in molecular dynamics simulations, particularly in semidilute solutions, for a better understanding of polyelectrolyte solution behavior.
The collective density-density and hydrostatic pressure-pressure correlations of glass-forming liquids are spatiotemporally mapped out using molecular dynamics simulations. It is shown that the sharp rise of structural relaxation time below the Arrhenius temperature coincides with the emergence of slow, nonhydrodynamic collective dynamics on mesoscopic scales. The observed long-range, nonhydrodynamic mode is independent of wave numbers and closely coupled to the local structural dynamics. Below the Arrhenius temperature, it dominates the slow collective dynamics on length scales immediately beyond the first structural peak in contrast to the well-known behavior at high temperatures. These results highlight a key connection between the qualitative change in mesoscopic two-point collective dynamics and the dynamic crossover phenomenon.
Conversion of plastic wastes to fatty acids is an attractive means to supplement the sourcing of these high-value, high-volume chemicals. We report a method for transforming polyethylene (PE) and polypropylene (PP) at ~80% conversion to fatty acids with number-average molar masses of up to ~700 and 670 daltons, respectively. The process is applicable to municipal PE and PP wastes and their mixtures. Temperature-gradient thermolysis is the key to controllably degrading PE and PP into waxes and inhibiting the production of small molecules. The waxes are upcycled to fatty acids by oxidation over manganese stearate and subsequent processing. PP êµ-scission produces more olefin wax and yields higher acid-number fatty acids than does PE êµ-scission. We further convert the fatty acids to high-value, large-market-volume surfactants. Industrial-scale technoeconomic analysis suggests economic viability without the need for subsidies.
Polyzwitterions (PZs) are considered as model synthetic analogs of intrinsically disordered proteins. Based on this analogy, PZs in dilute aqueous solutions are expected to attain either globular (i.e. molten, compact) or random coil conformations. Addition of salt is expected to open these conformations. To the best of our knowledge, these hypotheses about conformations of PZs have never been verified. In this study, we test these hypotheses by studying effects of added salt [potassium bromide (KBr)] on gyration and hydrodynamic radii of poly(sulfobetaine methacrylate) in dilute aqueous solutions using dynamic light scattering and small-angle X-ray scattering, respectively. Effects of zwitteration are revealed by direct comparisons of the PZs with the polymers of the same backbone but containing (1) no explicit charges on side groups such as poly(2-dimethylaminoethyl methacrylate)s and (2) explicit cationic side groups with tertiary amino bromide pendants. Zeta-potential measurements, transmission electron microscopy, and ab initio molecular dynamics simulations reveal that the PZs acquire net positive charge in near salt-free conditions due to protonation but retain coiled conformations. Added KBr leads to nonmonotonic changes exhibiting an increase followed by a decrease in radius of gyration (and hydrodynamic radius), which are called antipolyelectrolyte and polyelectrolyte effects, respectively. Charge regulation and screening of charge-charge interactions are discussed in relation to the antipolyelectrolyte and polyelectrolyte effects, respectively, which highlight the importance of salt in affecting net charge and conformations of PZs.
Plastic represents an essential material in our society; however, a major imbalance between their high production and end-of-life management is leading to unrecovered energy, economic hardship, and a high carbon footprint. The adoption of plastic recycling has been limited, mainly due to the difficulty of recycling mixed plastics. Here, we report a versatile organocatalyst for selective glycolysis of diverse consumer plastics and their mixed waste streams into valuable chemicals. The developed organocatalyst selectively deconstructs condensation polymers at a specific temperature, and additives or other polymers such as polyolefin or cellulose can be readily separated from the mixed plastics, providing a chemical recycling path for many existing mixed plastics today. The Life Cycle Assessment indicates that the production of various condensation polymers from the deconstructed monomers will result in a significant reduction in greenhouse gas emissions and energy input, opening a new paradigm of plastic circularity toward a net-zero carbon society.
Moving toward a future of efficient, accessible, and less carbon-reliant energy devices has been at the forefront of energy research innovations for the past 30 years. Metal-halide perovskite (MHP) thin films have gained significant attention due to their flexibility of device applications and tunable capabilities for improving power conversion efficiency. Serving as a gateway to optimize device performance, consideration must be given to chemical synthesis processing techniques. Therefore, how does common substrate processing techniques influence the behavior of MHP phenomena such as ion migration and strain? Here, we demonstrate how a hybrid approach of chemical bath deposition (CBD) and nanoparticle SnO2 substrate processing significantly improves the performance of (FAPbI3)0.97(MAPbBr3)0.03 by reducing micro-strain in the SnO2 lattice, allowing distribution of K+ from K-Cl treatment of substrates to passivate defects formed at the interface and produce higher current in light and dark environments. X-ray diffraction reveals differences in lattice strain behavior with respect to SnO2 substrate processing methods. Through use of conductive atomic force microscopy (c-AFM), conductivity is measured spatially with MHP morphology, showing higher generation of current in both light and dark conditions for films with hybrid processing. Additionally, time-of-flight secondary ionization mass spectrometry (ToF-SIMS) observed the distribution of K+ at the perovskite/SnO2 interface, indicating K+ passivation of defects to improve the power conversion efficiency (PCE) and device stability. We show how understanding the role of ion distribution at the SnO2 and perovskite interface can help reduce the creating of defects and promote a more efficient MHP device.
Here, we report synergistic nanostructured surfaces combining bactericidal and bacteria-releasing properties. A polystyrene-block-poly(methyl methacrylate) (PS-block-PMMA) diblock copolymer is used to fabricate vertically oriented cylindrical PS structures ("PS nanopillars") on silicon substrates. The results demonstrate that the PS nanopillars (with a height of about 10 nm, size of about 50 nm, and spacing of about 70 nm) exhibit highly effective bactericidal and bacteria-releasing properties ("dual properties") against Escherichia coli for at least 36 h of immersion in an E. coli solution. Interestingly, the PS nanopillars coated with a thin layer (≈3 nm thick) of titanium oxide (TiO2) ("TiO2 nanopillars") show much improved dual properties against E. coli (a Gram-negative bacterium) compared to the PS nanopillars. Moreover, the dual properties emerge against Listeria monocytogenes (a Gram-positive bacterium). To understand the mechanisms underlying the multifaceted property of the nanopillars, coarse-grained molecular dynamics (MD) simulations of a lipid bilayer (as a simplified model for E. coli) in contact with a substrate containing hexagonally packed hydrophilic nanopillars were performed. The MD results demonstrate that when the bacterium-substrate interaction is strong, the lipid heads adsorb onto the nanopillar surfaces, conforming the shape of a lipid bilayer to the structure/curvature of nanopillars and generating high stress concentrations within the membrane (i.e., the driving force for rupture) at the edge of the nanopillars. Membrane rupture begins with the formation of pores between nanopillars (i.e., bactericidal activity) and ultimately leads to the membrane withdrawal from the nanopillar surface (i.e., bacteria-releasing activity). In the case of Gram-positive bacteria, the adhesion area to the pillar surface is limited due to the inherent stiffness of the bacteria, creating higher stress concentrations within a bacterial cell wall. The present study provides insight into the mechanism underlying the "adhesion-mediated" multifaceted property of nanosurfaces, which is crucial for the development of next-generation antibacterial surface coatings for relevant medical applications.
Escherichia coli , Lipid Bilayers , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/chemistry , Bacteria , Surface Properties
Many computational models have been developed to predict the rates of atomic displacements in two-dimensional (2D) materials under electron beam irradiation. However, these models often drastically underestimate the displacement rates in 2D insulators, in which beam-induced electronic excitations can reduce the binding energies of the irradiated atoms. This bond softening leads to a qualitative disagreement between theory and experiment, in that substantial sputtering is experimentally observed at beam energies deemed far too small to drive atomic dislocation by many current models. To address these theoretical shortcomings, this paper develops a first-principles method to calculate the probability of beam-induced electronic excitations by coupling quantum electrodynamics (QED) scattering amplitudes to density functional theory (DFT) single-particle orbitals. The presented theory then explicitly considers the effect of these electronic excitations on the sputtering cross section. Applying this method to 2D hexagonal BN and MoS2 significantly increases their calculated sputtering cross sections and correctly yields appreciable sputtering rates at beam energies previously predicted to leave the crystals intact. The proposed QED-DFT approach can be easily extended to describe a rich variety of beam-driven phenomena in any crystalline material.
Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materials under a programmable dynamic environment. Specially designed software controls the experiment and performs on-the-fly extensive data analysis and dynamic modeling, real-time iterative feedback for dynamic control of experimental conditions, and rapid visualization of experimental results. The system operates with minimal human intervention and enables time-efficient characterization of complex dynamic multifunctional environmental responses of materials with simultaneous data processing and analytics. The system provides a viable platform for artificial intelligence (AI)-centered material characterization, which, when coupled with an AI-controlled synthesis system, could lead to accelerated discovery of multifunctional materials.
To understand and resolve adsorption, reconfiguration, and equilibrium conformations of charged star copolymers, we carried out an integrated experimental and coarse-grained molecular dynamics simulation study of the assembly process at the oil-water interface. This is important to guide development of novel surfactants or amphiphiles for chemical transformations and separations. The star block copolymer consisted of arms that are comprised of hydrophilic-hydrophobic block copolymers that are covalently tethered via the hydrophobic blocks to one point. The hydrophobic core represents polystyrene (PS) chains, while the hydrophilic corona represents quaternized poly(2-vinylpyridine) (P2VP) chains. The P2VP is modeled to become protonated when in contact with an acidic aqueous phase, thereby massively increasing the hydrophilicity of this block, and changing the nature of the star at the oil-water interface. This results in a configurational change whereby the chains comprising the hydrophilic corona are significantly stretched into the aqueous phase, while the hydrophobic core remains solubilized in the oil phase. In the simulations, we followed the kinetics of the anchoring and assembly of the star block copolymer at the interface, monitoring the lateral assembly, and the subsequent reconfiguration of the star via changes in the interfacial tension that varies as the degree-of-protonation increases. At low fractions of protonation, the arm cannot fully partition into the aqueous side of the interface and instead interacts with other arms in the oil phase forming a network near the interface. These insights were used to interpret the non-monotonic dependence of pH with the asymptotic interfacial tension from pendant drop tensiometry experiments and spectral signatures of aromatic stretches seen in vibrational sum frequency generation (SFG) spectroscopy. We describe the relationship of interfacial tension to the star assembly via the Frumkin isotherm, which phenomenologically describes anti-cooperativity in adsorbing stars to the interface due to crowding. Although our model explicitly considers long-range electrostatics, the contribution of electrostatics to interfacial tension is small and brought about by strong counterion condensation at the interface. These results provide key insights into resolving the adsorption, reconfiguration, and equilibrium conformations of charged star block copolymers as surfactants.
Explicit time-dependent electronic structure theory methods are increasingly prevalent in the areas of condensed matter physics and quantum chemistry, with the broad-band optical absorptivity of molecular and small condensed-phase systems nowadays routinely studied with such approaches. In this paper, it is demonstrated that electronic dynamics simulations can similarly be employed to study cross sections for the scattering-induced electronic excitations probed in nonresonant inelastic X-ray scattering and momentum-resolved electron energy loss spectroscopies. A method is put forth for evaluating the electronic dynamic structure factor, which involves the application of a momentum boost-type perturbation and transformation of the resulting reciprocal space density fluctuations into the frequency domain. Good agreement is first demonstrated between the dynamic structure factor extracted from these electronic dynamics simulations and the corresponding transition matrix elements from linear response theory. The method is then applied to some extended (quasi)one-dimensional systems, for which the wave vector becomes a good quantum number in the thermodynamic limit. Finally, the dispersion of many-body excitations in a series of hydrogen-terminated graphene flakes (and twisted bilayers thereof) is investigated to highlight the utility of the presented approach for capturing morphology-dependent effects in the inelastic scattering cross sections of nanostructured and/or noncrystalline materials.
The self-correlation function and corresponding self-intermediate scattering function in Fourier space are important quantities for describing the molecular motions of liquids. This work draws attention to a largely overlooked issue concerning the analysis of these space-time density-density correlation functions of polymers. We show that the interpretation of non-Gaussian behavior of polymers is generally complicated by intrachain averaging of distinct self-dynamics of different segments. By the very nature of the mathematics involved, the averaging process not only conceals critical dynamical information, but also contributes to the observed non-Gaussian dynamics. To fully expose this issue and provide a thorough benchmark of polymer self-dynamics, we perform analyses of coarse-grained molecular dynamics simulations of linear and ring polymer melts as well as several theoretical models using a "two-step" approach, where interchain and intrachain averagings of segmental self-dynamics are separated. While past investigations primarily focused on the average behavior, our results indicate that a more nuanced approach to polymer self-dynamics is clearly required.
We present a quantitative approach to the self-dynamics of polymers under steady flow by employing a set of complementary reference frames and extending the spherical harmonic expansion technique to dynamic density correlations. Application of this method to nonequilibrium molecular dynamics simulations of polymer melts reveals a number of universal features. For both unentangled and entangled melts, the center-of-mass motions in the flow frame are described by superdiffusive, anisotropic Gaussian distributions, whereas the isotropic component of monomer self-dynamics in the center-of-mass frame is strongly suppressed. Spatial correlation analysis shows that the heterogeneity of monomer self-dynamics increases significantly under flow.
