Effect of inorganic material surface chemistry on structures and fracture behaviours of epoxy resin.

The mechanisms underlying the influence of the surface chemistry of inorganic materials on polymer structures and fracture behaviours near adhesive interfaces are not fully understood. This study demonstrates the first clear and direct evidence that molecular surface segregation and cross-linking of epoxy resin are driven by intermolecular forces at the inorganic surfaces alone, which can be linked directly to adhesive failure mechanisms. We prepare adhesive interfaces between epoxy resin and silicon substrates with varying surface chemistries (OH and H terminations) with a smoothness below 1 nm, which have different adhesive strengths by ~13 %. The epoxy resins within sub-nanometre distance from the surfaces with different chemistries exhibit distinct amine-to-epoxy ratios, cross-linked network structures, and adhesion energies. The OH- and H-terminated interfaces exhibit cohesive failure and interfacial delamination, respectively. The substrate surface chemistry impacts the cross-linked structures of the epoxy resins within several nanometres of the interfaces and the adsorption structures of molecules at the interfaces, which result in different fracture behaviours and adhesive strengths.

Chain-Level Analysis of Reinforced Polyethylene through Stretch-Induced Crystallization.

Herein, we propose a large-scale simulation approach to perform the stretch-induced crystallization of entangled polyethylene (PE) melts. Sufficiently long (1000 ns) united-atom molecular dynamics (UAMD) simulations for 16000 chains of 1000 consecutive CH2 united-atom particles under periodic boundary conditions were performed to achieve the crystallinity observed in experiments. Before the isothermal crystallization process, we applied uniaxial stretching as pre-elongation to the embedded strain memory on the entangled PE melts. We confirmed significant differences in the morphologies of crystal domains and scattering patterns for pre-elongation ratios of 400% and 800%. The obtained scattering patterns were consistent with the experimental results. Uniaxial stretching MD simulations revealed that the elastic modulus at 800% pre-elongation was stronger than that at 400% pre-elongation. From this observation, we can derive the structure-property relationship, wherein the magnitude of the pre-elongation governs the crystal domain structures and mechanical properties.

Rotaxane Formation of Multicyclic Polydimethylsiloxane in a Silicone Network: A Step toward Constructing "Macro-Rotaxanes" from High-Molecular-Weight Axle and Wheel Components.

Rotaxanes consisting of a high-molecular-weight axle and wheel components (macro-rotaxanes) have high structural freedom, and are attractive for soft-material applications. However, their synthesis remains underexplored. Here, we investigated macro-rotaxane formation by the topological trapping of multicyclic polydimethylsiloxanes (mc-PDMSs) in silicone networks. mc-PDMS with different numbers of cyclic units and ring sizes was synthesized by cyclopolymerization of a α,ω-norbornenyl-functionalized PDMS. Silicone networks were prepared in the presence of 10-60 wt % mc-PDMS, and the trapping efficiency of mc-PDMS was determined. In contrast to monocyclic PDMS, mc-PDMSs with more cyclic units and larger ring sizes can be quantitatively trapped in the network as macro-rotaxanes. The damping performance of a 60 wt % mc-PDMS-blended silicone network was evaluated, revealing a higher tan δ value than the bare PDMS network. Thus, macro-rotaxanes are promising as non-leaching additives for network polymers.

Prediction of the Ground-State Electronic Structure from Core-Loss Spectra of Organic Molecules by Machine Learning.

The core-loss spectrum reflects the partial density of states (PDOS) of the unoccupied states at the excited state and is a powerful analytical technique to investigate local atomic and electronic structures of materials. However, various molecular properties governed by the ground-state electronic structure of the occupied orbital cannot be directly obtained from the core-loss spectra. Here, we constructed a machine learning model to predict the ground-state carbon s- and p-orbital PDOS in both occupied and unoccupied states from the C K-edge spectra. We also attempted an extrapolation prediction of the PDOS of larger molecules using a model trained by smaller molecules and found that the extrapolation prediction performance can be improved by excluding tiny molecules. Besides, we found that using smoothing preprocess and training by specific noise data can improve the PDOS prediction for noise-contained spectra, which pave a way for the application of the prediction model to the experimental data.

Molecular Dynamics Simulations on Epoxy/Silica Interfaces Using Stable Atomic Models of Silica Surfaces.

The adhesion between silica surfaces and epoxy resins was investigated via molecular dynamics (MD) simulations with stable atomic models of silica substrates prepared by density functional theory (DFT) calculations and reactive force field (ReaxFF) MD simulations. We aimed to develop reliable atomic models for evaluating the effect of nanoscale surface roughness on adhesion. Three consecutive simulations were performed: (i) stable atomic modeling of silica substrates; (ii) network modeling of epoxy resins by pseudo-reaction MD simulations; and (iii) virtual experiments via MD simulations with deformations. We prepared stable atomic models of OH- and H-terminated silica surfaces based on a dense surface model to consider the native thin oxidized layers on silicon substrates. Moreover, a stable silica surface grafted with epoxy molecules as well as nano-notched surface models were constructed. Cross-linked epoxy resin networks confined between frozen parallel graphite planes were prepared by pseudo-reaction MD simulations with three different conversion rates. Tensile tests using MD simulations indicated that the shape of the stress-strain curve was similar for all models up to near the yield point. This behavior indicated that the frictional force originated from chain-to-chain disentanglements when the adhesion between the epoxy network and silica surfaces was sufficiently strong. MD simulations for shear deformation indicated that the friction pressures in the steady state for the epoxy-grafted silica surface were higher than those for the OH- and H-terminated surfaces. The slope of the stress-displacement curve was steeper for surfaces with deeper notches (approximately 1 nm in depth), although the friction pressures for the examined notched surfaces were similar to those for the epoxy-grafted silica surface. Thus, nanometer-scale surface roughness is expected to have a large impact on the adhesion between polymeric materials and inorganic substrates.

Miscibility and exchange chemical potential of ring polymers in symmetric ring-ring blends.

Generally, differences of polymer topologies may affect polymer miscibility even with the same repeated units. In this study, the topological effect of ring polymers on miscibility was investigated by comparing symmetric ring-ring and linear-linear polymer blends. To elucidate the topological effect of ring polymers on mixing free energy, the exchange chemical potential of binary blends was numerically evaluated as a function of composition ϕ by performing semi-grand canonical Monte Carlo and molecular dynamics simulations of a bead-spring model. For ring-ring blends, an effective miscibility parameter was evaluated by comparing the exchange chemical potential with that of the Flory-Huggins model for linear-linear polymer blends. It was confirmed that in the mixed states satisfying χN > 0, ring-ring blends are more miscible and stable than the linear-linear blends with the same molecular weight. Furthermore, we investigated finite molecular weight dependence on the miscibility parameter, which reflected the statistical probability of interchain interactions in the blends. The simulation results revealed that the molecular weight dependence on the miscibility parameter was smaller in ring-ring blends. The effect of the ring polymers on miscibility was verified to be consistent with the change in the interchain radial distribution function. In ring-ring blends, it was indicated that the topology affected miscibility by reducing the effect of the direct interaction between the components of the blends.

Efficient compressed database of equilibrated configurations of ring-linear polymer blends for MD simulations.

To effectively archive configuration data during molecular dynamics (MD) simulations of polymer systems, we present an efficient compression method with good numerical accuracy that preserves the topology of ring-linear polymer blends. To compress the fraction of floating-point data, we used the Jointed Hierarchical Precision Compression Number - Data Format (JHPCN-DF) method to apply zero padding for the tailing fraction bits, which did not affect the numerical accuracy, then compressed the data with Huffman coding. We also provided a dataset of well-equilibrated configurations of MD simulations for ring-linear polymer blends with various lengths of linear and ring polymers, including ring complexes composed of multiple rings such as polycatenane. We executed 109 MD steps to obtain 150 equilibrated configurations. The combination of JHPCN-DF and SZ compression achieved the best compression ratio for all cases. Therefore, the proposed method enables efficient archiving of MD trajectories. Moreover, the publicly available dataset of ring-linear polymer blends can be employed for studies of mathematical methods, including topology analysis and data compression, as well as MD simulations.

Molecular dynamics studies of entropic elasticity of condensed lattice networks connected with uniform functionality f = 4.

To study the linear region of entropic elasticity, we considered the simplest physical model possible and extracted the linear entropic regime by using the least squares fit and the minimum of the mean absolute error. With regard to the effect of the fluctuation of the strand length Ns, the strand length with fluctuation was set to a form proportional to (1.0 + C (R - 0.5)), where R is a uniform random number between 0 and 1 and C is the amplitude of fluctuation. This form enabled us to analytically calculate the fluctuation dependence of the elastic modulus G. To reveal the linear regions of entropic elasticity as a function of the strand length between neighboring nodes in lattices, molecular dynamics (MD) simulations of condensed lattice networks with harmonic bonds without the excluded volume interactions were performed. Stress-strain curves were estimated by performing uniaxial stretching MD simulations under periodic boundary conditions with a bead number density of 0.85. First, we used a diamond lattice with functionality f = 4. The linear region of the entropic elasticity was found to become larger with the increasing number of beads in a strand Ns. For Ns = 100, the linear region had a strain of up to 8 for a regular diamond lattice. We investigated the effect of strand length fluctuation on the diamond lattice, and we confirmed that the equilibrium shear modulus G increases as the obtained analytical prediction and the linear entropic region in the stress-strain curves becomes narrower with increasing fluctuation of Ns. To investigate the difference in network topology with the same functionality f and uniform strand length Ns, we performed MD simulations on regular networks of the BC-8 structure with f = 4 prepared from the ab initio DFT calculations of carbon at high pressure. We found that the elastic behavior depends on the network connectivity (i.e., topology). This indicates that the network topology plays an important role in the emergence of nonlinearity owing to the crossover from entropic to energetic elasticity.

Improving the strength and toughness of macroscale double networks by exploiting Poisson's ratio mismatch.

We propose a new concept that utilizes the difference in Poisson's ratio between component materials as a strengthening mechanism that increases the effectiveness of the sacrificial bond toughening mechanism in macroscale double-network (Macro-DN) materials. These Macro-DN composites consist of a macroscopic skeleton imbedded within a soft elastic matrix. We varied the Poisson's ratio of the reinforcing skeleton by introducing auxetic or honeycomb functional structures that results in Poisson's ratio mismatch between the skeleton and matrix. During uniaxial tensile experiments, high strength and toughness were achieved due to two events: (1) multiple internal bond fractures of the skeleton (like sacrificial bonds in classic DN gels) and (2) significant, biaxial deformation of the matrix imposed by the functional skeleton. The Macro-DN composite with auxetic skeleton exhibits up to 4.2 times higher stiffness and 4.4 times higher yield force than the sum of the component materials. The significant improvement in mechanical performance is correlated to the large mismatch in Poisson's ratio between component materials, and the enhancement is especially noticeable in the low-stretch regime. The strengthening mechanism reported here based on Poisson's ratio mismatch can be widely used for soft materials regardless of chemical composition and will improve the mechanical properties of elastomer and hydrogel systems.

Deep learning-based estimation of Flory-Huggins parameter of A-B block copolymers from cross-sectional images of phase-separated structures.

In this study, deep learning (DL)-based estimation of the Flory-Huggins χ parameter of A-B diblock copolymers from two-dimensional cross-sectional images of three-dimensional (3D) phase-separated structures were investigated. 3D structures with random networks of phase-separated domains were generated from real-space self-consistent field simulations in the 25-40 χN range for chain lengths (N) of 20 and 40. To confirm that the prepared data can be discriminated using DL, image classification was performed using the VGG-16 network. We comprehensively investigated the performances of the learned networks in the regression problem. The generalization ability was evaluated from independent images with the unlearned χN. We found that, except for large χN values, the standard deviation values were approximately 0.1 and 0.5 for A-component fractions of 0.2 and 0.35, respectively. The images for larger χN values were more difficult to distinguish. In addition, the learning performances for the 4-class problem were comparable to those for the 8-class problem, except when the χN values were large. This information is useful for the analysis of real experimental image data, where the variation of samples is limited.

Correction: Particle-mesh two-dimensional pattern reverse Monte Carlo analysis on filled-gels during uniaxial expansion.

Correction for 'Particle-mesh two-dimensional pattern reverse Monte Carlo analysis on filled-gels during uniaxial expansion' by Katsumi Hagita, Soft Matter, 2019, 15, 7237-7249.

Particle-mesh two-dimensional pattern reverse Monte Carlo analysis on filled-gels during uniaxial expansion.

A particle-mesh-based two-dimensional pattern reverse Monte Carlo (RMC) analysis method (PM-2DpRMC) is proposed for analyzing two-dimensional small-angle-scattering (2D-SAS) patterns. The results of analyzing such patterns in expanded gel networks filled with spherical nano-particles during uniaxial elongation are provided. Previously, characteristic 2D-SAS patterns, such as the two-point bar plate and figure-8 patterns, were observed to possess high stretching ratios in a coarse-grained molecular dynamics (CGMD) simulation, and the patterns were found to depend on the properties of the networks, such as whether they were topologically percolated or not. To establish real-space visualizations of the changes in the particle-configurations corresponding to the 2D-SAS patterns, the on-the-fly PM-2DpRMC method was employed to model the morphology changes of the filler particles during stretching in a series of 2D-SAS patterns. The use of quasi-dynamics to express deformation was validated by evaluating the accuracy of the elongation speed and the number of RMC trials predicted from the behavior of the converged χ2. The results confirm that the obtained sequences of 3D configurations were similar to the original configurations obtained in the CGMD simulations. Further, a demonstrative test using actual experimental data was conducted to verify the completeness of the confirmation.

Study of Commodity VR for Computational Material Sciences.

Recent advancements in virtual reality (VR) devices and software environments make it possible to easily incorporate this technology for many applications, including computational materials science. For studying three-dimensional (3D) structure models and related chemical information, we focused on using a commodity VR device (VIVE) and an authoring tool (Unity). To visualize 3D chemical structures, disturbances like judder due to dropped frames should be eliminated from the VR experience to improve simulations. We propose a simple evaluation method that is straightforward for the nonexpert or novice VR user. We examine the major visualization representations including ball, ball and stick, and isosurface systems. For systematic benchmark measurements, a pendulum from the VR device was used to generate periodic oscillatory motion during measurements of a time series in frames per second (fps). For VIVE with a refresh rate of 90 Hz, judder occurred when less than 90 fps. We demonstrated the system size limitations for the results of molecular dynamics simulations of phase separation of ABA block copolymers and experimental observations of filler morphologies in rubber.

An accelerated united-atom molecular dynamics simulation on the fast crystallization of ring polyethylene melts.

To investigate crystallinities based on trans-structures, we determined the differences in the crystallization properties of ring and linear polymers by performing united-atom-model molecular dynamics (MD) simulations of homogeneous polyethylene melts of equal length, N, which refers to the number of monomers per chain. Modified parameters based on the DREIDING force field for the CH2 units were used in order to accelerate the crystallization process. To detect polymer crystallization, we introduced some local-order parameters that relate to trans-segments in addition to common crystallinities using neighboring bond orders. Through quenching MD simulations at 5 K/ns, we roughly determined temperature thresholds, Tth, at which crystallization is observed although it was hard to determine the precise Tth as observed in the laboratory time frame with the present computing resources. When N was relatively small (100 and 200), Tth was determined to be 320 and 350 K for the linear- and ring-polyethylene melts, respectively, while Tth was found to be 330 and 350 K, respectively, when N was 1000. Having confirmed that the crystallization of a ring-polyethylene melt occurs faster than that of the analogous linear melt, we conclude that the trans-segment-based crystallinities are effective for the analysis of local crystal behavior.

Scattering patterns and stress-strain relations on phase-separated ABA block copolymers under uniaxial elongating simulations.

To develop molecularly based interpretations of the two-dimensional scattering patterns (2DSPs) of phase-separated block copolymers (BCPs), we performed coarse-grained molecular dynamics simulations of ABA tri-BCPs under uniaxial stretching for block-fractions where the A-segment (glassy domain) is smaller than the B-segment (rubbery domain), and estimated the behaviour of their 2DSPs. In BCP stretching experiments, mechanical properties are generally evaluated using a stress-strain curve. We obtained 2DSPs with different contrasts for the A- and B-segments, which are indicative of the differences between X-ray and neutron scattering experiments. The small- and wide-angle behaviours of the 2DSPs originate from the morphologies of the phase-separated domains and local bond orientations, respectively. When the block-fractions are changed for a constant stress value on the stress-strain (SS) curve, the brightness of the spots in the wide-angle region of the A- and B-segment-dominant 2DSPs decreases and increases with increasing strain, respectively. We can regard the systematic changes in the small-angle 2DSPs of the glassy domain and the wide-angle 2DSPs of the rubbery domain with changes in the SS-curve as a structure-property relationship.

Multipoint segmental repulsive potential for entangled polymer simulations with dissipative particle dynamics.

A model is developed for simulating entangled polymers by dissipative particle dynamics (DPD) using the segmental repulsive potential (SRP). In contrast to previous SRP models that define a single-point interaction on each bond, the proposed SRP model applies a dynamically adjustable multipoint on the bond. Previous SRP models could not reproduce the equilibrium properties of Groot and Warren's original DPD model [R. D. Groot and P. B. Warren, J. Chem. Phys. 107, 4423 (1997)] because the introduction of a single SRP induces a large excluded volume, whereas, the proposed multipoint SRP (MP-SRP) introduces a cylindrical effective excluded bond volume. We demonstrate that our MP-SRP model exhibits equilibrium properties similar to those of the original DPD polymers. The MP-SRP model parameters are determined by monitoring the number of topology violations, thermodynamic properties, and the polymer internal structure. We examine two typical DPD polymers with different bond-length distributions; one of them was used in the modified SRP model by Sirk et al. [J. Chem. Phys. 136, 134903 (2012)], whereas the other was used in the original DPD model. We demonstrate that for both polymers, the proposed MP-SRP model captures the entangled behaviors of a polymer melt naturally, by calculating the slowest relaxation time of a chain in the melt and the shear relaxation modulus. The results indicate that the proposed MP-SRP model can be applied to a variety of DPD polymers.

Super-resolution for asymmetric resolution of FIB-SEM 3D imaging using AI with deep learning.

Scanning electron microscopy equipped with a focused ion beam (FIB-SEM) is a promising three-dimensional (3D) imaging technique for nano- and meso-scale morphologies. In FIB-SEM, the specimen surface is stripped by an ion beam and imaged by an SEM installed orthogonally to the FIB. The lateral resolution is governed by the SEM, while the depth resolution, i.e., the FIB milling direction, is determined by the thickness of the stripped thin layer. In most cases, the lateral resolution is superior to the depth resolution; hence, asymmetric resolution is generated in the 3D image. Here, we propose a new approach based on an image-processing or deep-learning-based method for super-resolution of 3D images with such asymmetric resolution, so as to restore the depth resolution to achieve symmetric resolution. The deep-learning-based method learns from high-resolution sub-images obtained via SEM and recovers low-resolution sub-images parallel to the FIB milling direction. The 3D morphologies of polymeric nano-composites are used as test images, which are subjected to the deep-learning-based method as well as conventional methods. We find that the former yields superior restoration, particularly as the asymmetric resolution is increased. Our super-resolution approach for images having asymmetric resolution enables observation time reduction.

Thinning Approximation for Calculating Two-Dimensional Scattering Patterns in Dissipative Particle Dynamics Simulations under Shear Flow.

Modifications to improve thinning approximation (TA) were considered in order to calculate two-dimensional scattering patterns (2DSPs) for dissipative particle dynamics (DPD) simulations of polymer melts under a shear flow. We proposed multipoint TA and adaptive TA because the bond lengths in DPD chains vary widely when compared to those in Kremer⁻Grest (KG) chains, and the effectiveness of these two types of TA for the two major DPD parameter sets were investigated. In this paper, we report our findings on the original DPD model with soft bonds and that with rigid bonds. Based on the behavior of the 2DSPs and the distribution of orientations of the bond vectors, two spot patterns originating from the oriented chain correlations were observed when distinct distributions of the highly oriented bond vectors in the shear direction were obtained. For multipoint TA, we concluded that at least two additional midpoints ( n mid ≥ 2 ) are required to clearly observe the two spot patterns. For adaptive TA, a dividing distance of l ATA ≤ 0.4 is sufficient for clear observation, which is consistent with the requirement of n mid ≥ 2 for multipoint TA.

Topological validation of morphology modeling by extended reverse Monte Carlo analysis.

A combination of reverse Monte Carlo (RMC) and computational homology is examined as a useful approach in connecting scattering experiments to mathematics for 3D morphology modeling. We develop a different method of morphology modeling from multiple two-dimensional (2D) scattering patterns of structure functions by RMC technique using coarse-grained particles. We perform RMC analysis for multiple 2D scattering patterns of the configuration generated from the surface equation of double gyroid morphology. Homology analysis enables us to classify complex three-dimensional morphologies by incorporating topologically invariant quantities, the so-called Betti numbers. It is demonstrated that RMC analysis reconstructs the DG morphology from multiple 2D scattering patterns.

Anomalously broad Raman scattering spectrum due to two-magnon excitation in hexagonal YMnO3.

A strong and broad Raman scattering (RS) spectrum is observed from two-magnon processes in YMnO3. The spectrum is analyzed by taking account (i) the magnon-exciton interaction and (ii) the magnon-phonon coupling in the intermediate state. Anomalous broadening of the RS is attributed to the large superexchange interaction between manganite ions and subsequent modification of magnons under the presence of the exciton and phonons in the intermediate state.