Scale-Dependent Miscibility of Polylactide and Polyhydroxybutyrate: Molecular Dynamics Simulations

A Temperature-Transferable Coarse-Grained Model for Poly(lactic Acid) Melts

We present a temperature-transferable coarse-grained (CG) model for poly(lactic acid) (PLA), specifically designed to replicate its volumetric properties and solubility parameter in the molten state. The CG-bonded potentials were derived by using the iterative Boltzmann inversion (IBI) optimization method to match structural properties from detailed atomistic models. A parametrization workflow was employed to determine nonbonded interaction parameters with temperature-dependent corrections that provide agreement with the target properties across the melting temperature range. The CG model successfully replicates key features of the PLA melt. It satisfactory reproduces the density and solubility parameter, maintains the dependence of chain conformation on molecular weight, and captures the dynamic behavior through agreement in scaled mean squared displacement and diffusion coefficients with the atomistic model. Additionally, the CG model offers much faster equilibration compared with the atomistic model. The proposed model is expected to be particularly useful for investigating the miscibility characteristics of PLA in various blends and composites that remain challenging to explore using fully atomistic simulations or experiments.

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers

Multiscale modeling of complex molecular systems, such as macromolecules, encompasses methods that combine information from fine and coarse representations of molecules to capture material properties over a wide range of spatiotemporal scales. Being able to exchange information between different levels of resolution is essential for the effective transfer of this information. The inverse problem of reintroducing atomistic degrees of freedom in coarse-grained (CG) molecular configurations is particularly challenging as, from a mathematical point of view, it is an ill-posed problem; the forward mapping from the atomistic to the CG description is typically defined via a deterministic operator ("one-to-one" problem), whereas the reversed mapping from the CG to the atomistic model refers to creating one representative configuration out of many possible ones ("one-to-many" problem). Most of the backmapping methods proposed so far balance accuracy, efficiency, and general applicability. This is particularly important for macromolecular systems with different types of isomers, i.e., molecules that have the same molecular formula and sequence of bonded atoms (constitution) but differ in the three-dimensional configurations of their atoms in space. Here, we introduce a versatile deep learning approach for backmapping multicomponent CG macromolecules with chiral centers, trained to learn structural correlations between polymer configurations at the atomistic level and their corresponding CG descriptions. This method is intended to be simple and flexible while presenting a generic solution for resolution transformation. In addition, the method is aimed to respect the structural features of the molecule, such as local packing, capturing therefore the physical properties of the material. As an illustrative example, we apply the model on linear poly(lactic acid) (PLA) in melt, which is one of the most popular biodegradable polymers. The framework is tested on a number of model systems starting from homopolymer stereoisomers of PLA to copolymers with randomly placed chiral centers. The results demonstrate the efficiency and efficacy of the new approach.

Design of sustainable 3D printable polylactic acid composites with high lignin content

In this study, we report the development of a sustainable polymer system with 50 wt% lignin content, suitable for additive manufacturing and high value-added utilization of lignin. The plasticized polylactic acid (PLA) was incorporated with lignin to develop the bendable and malleable green composites with excellent 3D printing adaptability. The biocomposites exhibit increases of 765.54 % and 125.27 % in both elongation and toughness, respectively. The plasticizer enhances the dispersion of lignin and the molecular mobility of the PLA chains. The good dispersion of lignin particles within the structure and the reduction of chemical cross-linking promote the local relaxation of the polymer chains. The good local relaxation of the polymer chains and the high flexibility allow to obtain a better integration between the printed layers with good printability. This research demonstrates the promising potential of this composite system for sustainable manufacturing and provides insights into novel material design for high-value applications of lignin.

Molecular-Scale Exploration of Mechanical Properties and Interactions of Poly(lactic acid) with Cellulose and Chitin

Poly(lactic acid) (PLA), one of the pillars of the current overarching displacement trend switching from fossil- to natural-based polymers, is often used in association with polysaccharides to increase its mechanical properties. However, the use of PLA/polysaccharide composites is greatly hampered by their poor miscibility, whose underlying nature is still vastly unexplored. This work aims to shed light on the interactions of PLA and two representative polysaccharide molecules (cellulose and chitin) and reveal structure-property relationships from a fundamental perspective using atomistic molecular dynamics. Our computational strategy was able to reproduce key experimental mechanical properties of pure and/or composite materials, reveal a decrease in immiscibility in PLA/chitin compared to PLA/cellulose associations, assert PLA-oriented polysaccharide reorientations, and explore how less effective PLA-polysaccharide hydrogen bonds are related to the poor PLA/polysaccharide miscibility. The connection between the detailed chemical interactions and the composite behavior found in this work is beneficial to the discovery of new biodegradable and natural polymer composite mixtures that can provide needed performance characteristics.

The Role of Interchain Force and/or Chain Entanglement in the Melt Strength and Ductility of PLA-Based Materials

As an eco-friendly material, PLA was a desirable alternative to polyethylene and polypropylene films due to its biodegradability. The preferable melt strength of PLA-based materials was a key factor in ensuring its processing using extrusion blow. This paper focuses on the influence of interchain force and/or chain entanglement on the melt strength and ductility of PLA-based materials in recent years. In addition, the preparation of PLA-based materials via physical blending or reactive processing was also summarized. The blending of PLA with a flexible heteropolymer, driven by the interchain force and/or chain entanglements, were characterized as a practicable method for toughening PLA-based materials. Also, the restructuring of PLA chains, by branching based on chain entanglement, was suitable for increasing chain entanglements in PLA matrix, yielding satisfactory melt strength and ductility. This review aims to elucidate the relationship between interchain forces and/or entanglement with the melt strength and ductility of PLA-based materials. An essential and systematic understanding of the tailoring melt strength and rheological properties of PLA by interchain forces and/or entanglement was apt to improve and perfect the processing technology of the extrusion blow, and consequently improve the tensile strength and toughness of PLA films.

Biomass carbon mining to develop nature-inspired materials for a circular economy

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.

Mechanical and Gas Barrier Properties of Poly(Lactic Acid) Modified by Blending with Poly(Butylene 2,5-Furandicarboxylate): Based on Molecular Dynamics

Three blends of Poly(butylene 2,5-furandicarboxylate) (PBF) and Poly(lactic acid) (PLA) blends were modeled using molecular dynamics simulations, with PBF contents of 10%, 20%, and 30%, respectively. The study investigated the compatibilities of the blends, as well as the mechanical and gas barrier properties of the composite systems. The molecular dynamics simulation results show that: (1) PLA and PBF have good compatibility in the blend system; (2) the optimal toughness modification was achieved with a 20% PBF content, resulting in a 17.3% increase in toughness compared to pure PLA; (3) the barrier properties of the blend for O2, CO2, and N2 increased when increasing the PBF content. Compared to pure PLA, the diffusion coefficients of the O2, CO2, and N2 of the blends with 30% PBF decreased by 75%, 122%, and 188%, respectively. Our simulation results are in good agreement with the actual experimental results.

Molecular dynamics simulations of copolymer compatibilizers for polylactide/poly(butylene succinate) blends

The effects of the molecular architecture of polylactide (PLA)-poly(butylene succinate) (PBS) copolymers on their compatibilization efficiency in immiscible PLA/PBS blends were studied using atomistic molecular-dynamics simulations. The results showed that the diblock copolymer is the most efficient at mixing with PLA and PBS homopolymers and reducing the interfacial tension of the blend. Tensile properties of the compatibilized blends were examined using uniaxial deformation simulations. Deformation behaviours of the blends at the molecular level were revealed. Both the structure (i.e. linear and branch) and the monomer sequence of the copolymers were found to affect the tensile performance. The system with a diblock copolymer showed a much greater tensile strength and elastic modulus than the other compatibilized blends. Comparing between triblock and graft copolymers, the former yielded the blends with better tensile performance than the latter. Our results suggest that copolymers with a linear structure, especially the diblock one, could be good candidates for the compatibilizer of PLA/PBS blends.

Machine Learning with Enormous "Synthetic" Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks

In the present work, we address the problem of utilizing machine learning (ML) methods to predict the thermal properties of polymers by establishing "structure-property" relationships. Having focused on a particular class of heterocyclic polymers, namely polyimides (PIs), we developed a graph convolutional neural network (GCNN), being one of the most promising tools for working with big data, to predict the PI glass transition temperature T _g as an example of the fundamental property of polymers. To train the GCNN, we propose an original methodology based on using a "transfer learning" approach with an enormous "synthetic" data set for pretraining and a small experimental data set for its fine-tuning. The "synthetic" data set contains more than 6 million combinatorically generated repeating units of PIs and theoretical values of their T _g values calculated using the well-established Askadskii's quantitative structure-property relationship (QSPR) computational scheme. Additionally, an experimental data set for 214 PIs was also collected from the literature for training, fine-tuning, and validation of the GCNN. Both "synthetic" and experimental data sets are included into a PolyAskInG database (Polymer Askadskii's Intelligent Gateway). By using the PolyAskInG database, we developed GCNN which allows estimation of T _g of PI with a mean absolute error (MAE) of about 20 K, which is 1.5 times lower than in the case of Askadskii QSPR analysis (33 K). To prove the efficiency and usability of the proposed GCNN architecture and training methodology for predicting polymer properties, we also employed "transfer learning" to develop alternative GCNN pretrained on proxy-characteristics taken from the popular quantum-chemical QM9 database for small compounds and fine-tuned on an experimental T _g values data set from PolyAskInG database. The obtained results indicate that pretraining of GCNN on the "synthetic" polymer data set provides MAE which is almost twice as low as that in the case of using the QM9 data set in the pretraining stage (∼41 K). Furthermore, we address the questions associated with the influence of the differences in the size of the experimental and "synthetic" data sets (so-called "reality gap" problem), as well as their chemical composition on the training quality. Our results state the overall priority of using polymer data sets for developing deep neural networks, and GCNN in particular, for efficient prediction of polymer properties. Moreover, our work opens up a challenge for the theoretically supported generation of large "synthetic" data sets of polymer properties for the training of the complex ML models. The proposed methodology is rather versatile and may be generalized for predicting other properties of different polymers and copolymers synthesized through the polycondensation reaction.

Influence of Asphaltene Modification on Structure of P3HT/Asphaltene Blends: Molecular Dynamics Simulations

Further development and commercialization of bulk heterojunction (BHJ) solar cells require the search for novel low-cost materials. The present study addresses the relations between the asphaltenes' chemical structure and the morphology of the poly(3-hexylthiohene) (P3HT)/asphaltene blends as potential materials for the design of BHJ solar cells. By means of all-atom molecular dynamics simulations, the formation of heterophase morphology is observed for the P3HT-based blends with carboxyl-containing asphaltenes, as well as the aggregation of the asphaltenes into highly ordered stacks. Although the π-π interactions between the polyaromatic cores of the asphaltenes in solutions are sufficient for the molecules to aggregate into ordered stacks, in a blend with a conjugated polymer, additional stabilizing factors are required, such as hydrogen bonding between carboxyl groups. It is found that the asphaltenes' aliphatic side groups may improve significantly the miscibility between the polymer and the asphaltenes, thereby preventing the formation of heterophase morphology. The results also demonstrate that the carboxyl-containing asphaltenes/P3HT ratio should be at least 1:1, as a decrease in concentration of the asphaltenes leads to the folding of the polymer chains, lower ordering in the polymer phase and the destruction of the interpenetrating 3D structure formed by P3HT and the asphaltene phases. Overall, the results of the present study for the first time reveal the aggregation behavior of the asphaltenes of varying chemical structures in P3HT, as well the influence of their presence and concentration on the polymer phase structure and blend morphology, paving the way for future development of BHJ solar cells based on the conjugated polymer/asphaltene blends.

Comparative experimental and modelling study of the thermal and thermo-mechanical properties of LLDPE/wax blends

The interactions and morphology of molecules in a polymer blend influence the physical properties of the blend. However, little is known about the influence of molecular interaction and morphology on the thermal and mechanical properties of LLDPE/wax blends. Although cooling rate can be used to investigate blends' thermal and mechanical properties, it is inadequate to determine interactions between the molecules in the LLDPE/wax blends. However, since the morphology is related to the thermal and mechanical properties of polymer blends and could be related to the cooling rate, LLDPE/wax samples prepared by melt mixing were cooled at different rates. The thermal and mechanical properties of the LLDPE/wax blends were modelled through molecular dynamic simulations. The modelled transitions were compared to experimentally determined mechanical relaxations of LLDPE/wax blends to investigate the effect of wax addition on the blend crystallinity. The crystallization behaviour of the blends was studied by differential scanning calorimetry, dynamic mechanical behaviour by dynamic mechanical analysis, and differences in crystallinity by X-ray diffraction. There were no significant differences between the results for the slow- and quench-cooled samples, confirming the rapid crystallization of both the LLDPE and the wax. Experiments and molecular dynamics simulations confirmed the cocrystallization of wax with LLDPE.

Polyhydroxybutyrate blends: A solution for biodegradable packaging?

Poly (3-hydroxybutyrate) (PHB) is a valuable bio-based and biodegradable polymer that may substitute common polymers in packaging and biomedical applications provided that the production cost is reduced and some properties improved. Blending PHB with other biodegradable polymers is the most simple and accessible route to reduce costs and to improve properties. This review provides a comprehensive overview on the preparation, properties and application of the PHB blends with other biodegradable polyesters such as medium-chain-length polyhydroxyalkanoates, poly(ε-caprolactone), poly(lactic acid), poly(butylene succinate), poly(propylene carbonate) and poly (butylene adipate-co-terephthalate) or polysaccharides and their derivatives. A special attention has been paid to the miscibility of PHB with these polymers and the compatibilizing methods used to improve the dispersion and interface. The changes in the PHB morphology, thermal, mechanical and barrier properties induced by the second polymer have been critically analyzed in view of industrial application. The biodegradability and recyclability strategies of the PHB blends were summarized along with the processing techniques adapted to the intended application. This review provides the tools for a better understanding of the relation between the micro/nanostructure of PHB blends and their properties for the further development of PHB blends as solutions for biodegradable packaging.

Predicting the Mechanical Response of Polyhydroxyalkanoate Biopolymers Using Molecular Dynamics Simulations

Polyhydroxyalkanoates (PHAs) have emerged as a promising class of biosynthesizable, biocompatible, and biodegradable polymers to replace petroleum-based plastics for addressing the global plastic pollution problem. Although PHAs offer a wide range of chemical diversity, the structure-property relationships in this class of polymers remain poorly established. In particular, the available experimental data on the mechanical properties is scarce. In this contribution, we have used molecular dynamics simulations employing a recently developed forcefield to predict chemical trends in mechanical properties of PHAs. Specifically, we make predictions for Young's modulus, and yield stress for a wide range of PHAs that exhibit varying lengths of backbone and side chains as well as different side chain functional groups. Deformation simulations were performed at six different strain rates and six different temperatures to elucidate their influence on the mechanical properties. Our results indicate that Young's modulus and yield stress decrease systematically with increase in the number of carbon atoms in the side chain as well as in the polymer backbone. In addition, we find that the mechanical properties were strongly correlated with the chemical nature of the functional group. The functional groups that enhance the interchain interactions lead to an enhancement in both the Young's modulus and yield stress. Finally, we applied the developed methodology to study composition-dependence of the mechanical properties for a selected set of binary and ternary copolymers. Overall, our work not only provides insights into rational design rules for tailoring mechanical properties in PHAs, but also opens up avenues for future high throughput atomistic simulation studies geared towards identifying functional PHA polymer candidates for targeted applications.

Nanocomposite of Fullerenes and Natural Rubbers: MARTINI Force Field Molecular Dynamics Simulations

The mechanical properties of natural rubber (NR) composites depend on many factors, including the filler loading, filler size, filler dispersion, and filler-rubber interfacial interactions. Thus, NR composites with nano-sized fillers have attracted a great deal of attention for improving properties such as stiffness, chemical resistance, and high wear resistance. Here, a coarse-grained (CG) model based on the MARTINI force field version 2.1 has been developed and deployed for simulations of cis-1,4-polyisoprene (cis-PI). The model shows qualitative and quantitative agreement with the experiments and atomistic simulations. Interestingly, only a 0.5% difference with respect to the experimental result of the glass transition temperature (T_g) of the cis-PI in the melts was observed. In addition, the mechanical and thermodynamical properties of the cis-PI-fullerene(C₆₀) composites were investigated. Coarse-grained molecular dynamics (MD) simulations of cis-PI-C₆₀ composites with varying fullerene concentrations (0-32 parts per hundred of rubber; phr) were performed over 200 microseconds. The structural, mechanical, and thermal properties of the composites were determined. The density, bulk modulus, thermal expansion, heat capacity, and T_g of the NR composites were found to increase with increasing C₆₀ concentration. The presence of C₆₀ resulted in a slight increasing of the end-to-end distance and radius of the gyration of the cis-PI chains. The contribution of C₆₀ and cis-PI interfacial interactions led to an enhancement of the bulk moduli of the composites. This model should be helpful in the investigations and design of effective fillers of NR-C₆₀ composites for improving their properties.

Effect of amorphous cellulose on the deformation behavior of cellulose composites: molecular dynamics simulation

This study was aimed at predicting and enhancing the properties of the blend, as well as exploring the mechanism, of a polylactic acid (PLA)/amorphous cellulose composite system through molecular characterization. The static properties of the amorphous cellulose/PLA blend model and the mechanical response of the material under uniaxial tension were studied by molecular dynamics simulation to establish the structure-property relationship. PLA and cellulose showed poor miscibility, the change in the compatibility of the mixture can be attributed to the hydrogen bond interaction between the cellulose and PLA functional groups. The radius of gyration, interaction and free volume of the molecular chain in the blend were analyzed. The conformational changes under tensile deformation indicated that the load-bearing role of cellulose in the system was the main reason for increasing the strength of the material. The yield process was considered to be the infiltration of free volume caused by deformation.

Blending Ferulic Acid Derivatives and Polylactic Acid into Biobased and Transparent Elastomeric Materials with Shape Memory Properties

Thanks to its remarkable properties such as sustainability, compostability, biocompatibility, and transparency, poly-l-lactic acid (PLA) would be a suitable replacement for oil-based polymers should it not suffer from low flexibility and poor toughness, restricting its use to rigid plastic by excluding elastomeric applications. Indeed, there are few fully biobased and biodegradable transparent elastomers-PLA-based or not-currently available. In the last decades, many strategies have been investigated to soften PLA and enhance its toughness and elongation at break by using plasticizers, oligomers, or polymers. This work shows how a ferulic acid-derived biobased additive (BDF) blends with a common rigid and brittle commercial grade of polylactic acid to provide a transparent non-covalently cross-linked elastomeric material with shape memory behavior exhibiting an elongation at break of 434% (vs 6% for pristine PLA). Through a structure-activity relationship analysis conducted with BDF analogues and a modeling study, we propose a mechanism based on π-π stacking to account for the elastomeric properties. Blending ferulic acid derivatives with polylactic acid generates a new family of fully sustainable transparent elastomeric materials with functional properties such as shape memory.

Performance and multi-scale investigation on the phase miscibility of poly(lactic acid)/amided silica nanocomposites

In this work, amino-functionalized nano-SiO₂ (m@g-SiO₂) was synthesized through coupling reaction on the surface of nano-SiO₂. Moreover, the optimum preparation conditions of m@g-SiO₂ were selected via orthogonal experiments as follows: reaction temperature of 80 °C, reaction time of 8 h, the mass ratio of stearic acid, N,N'‑carbonyldiimidazole, imidazole hydrochloride and g-SiO₂ of 0.5:0.7:0.7:1. Fourier transform infrared spectroscopy, static angle measurement and X-ray photoelectron spectroscopy unanimously confirmed the formation of m@g-SiO₂. Furthermore, poly(lactic acid)(PLA)/m@g-SiO₂ nanocomposites was prepared with m@g-SiO₂ as fillers to improve the comprehensive performance of PLA. Then, the mechanical properties and crystallization behavior of PLA/m@g-SiO₂ nanocomposites were studied, which showed that the impact strength and elongation-at-break of PLA/m@g-SiO₂ (0.3 wt%) nanocomposites were increased by 78.05% and 1148%, respectively, and its crystallinity was increased by 26.46%. Simultaneously, thermal gravimetric analysis indicated that the thermal stability of PLA/m@g-SiO₂ nanocomposites was improved. Eventually, the multi-scale investigation on the phase miscibility of PLA/m@g-SiO₂ nanocomposites was probed by rheological behaviors analysis and the molecular dynamics simulations, which confirmed that surface modification of SiO₂ greatly enhanced the interaction energy and miscibility between the filler and PLA bulk.

Branched versus linear lactide chains for cellulose nanoparticle modification: an atomistic molecular dynamics study

We studied the structure of brushes consisting of branched oligolactide (OLA) chains grafted onto the surface of cellulose nanoparticles (CNPs) in polylactide (PLA) and compared the outcomes to the case of grafting linear OLA chains using atomistic molecular dynamics simulations. The systems were considered in a melt state. The branched model OLA chains comprised one branching point and three branches, while the linear OLA chains examined had a molecular weight similar to the branched chains. It was shown that free branches of the branched OLA chains tend to fold back toward the CNPs due to dipole-dipole interactions within the grafted layer, in contrast to the well-established behavior of the grafted uncharged branched chains. This result, however, is in qualitative agreement with the conformational behavior known for linear OLA chains. At the same time, no significant difference in the effectiveness of covering the filler surface with grafted branched or linear OLA chains was found. In terms of the expelling ability of the grafted chains and the interaction between PLA and CNP or OLA, the linear chains were broadly similar (sparse grafting) or better (intermediate or dense grafting) compared to the branched ones. Thus, the grafted lactide chains with a linear architecture, rather than their branched counterpart, may be preferable for the covalent modification of cellulose nanoparticles.

Predicting the Miscibility and Rigidity of Poly(lactic-co-glycolic acid)/Polyethylene Glycol Blends via Molecular Dynamics Simulations

The addition of polyethylene glycol (PEG) chains to poly(lactic-co-glycolic acid) (PLGA) matrices is extensively used to modulate the biodegradation, drug loading and release, mechanical properties, and chemical stability of the original system. Multiple parameters, including the molecular weight, relative concentration, polarity, and solubility, affect the physicochemical properties of the polymer blend. Here, molecular dynamics simulations with the united-atom 2016H66 force field are used to model the behavior of PLGA and PEG chains and thus predict the overall physicochemical features of the resulting blend. First, the model accuracy is validated against fundamental properties of pure PLGA and PEG samples. In agreement with previous experimental and theoretical observations, the PLGA solubility results to be higher in acetonitrile than in water, with Flory parameters ν_ACN = 0.63 ± 0.01 and ν_W = 0.21 ± 0.02, and the Young’s modulus of PLGA and PEG equal to Y = 2.0 ± 0.43 and 0.32 ± 0.34 GPa, respectively. Next, four PEG/PLGA blending regimes are identified by varying the relative concentrations and molecular weights of the individual polymers. The computational results demonstrate that at low PEG concentrations (<8% w/w), homogeneous blends are generated for both low and high PEG molecular weights. In contrast, at comparable PEG and PLGA concentrations (∼50% w/w), short PEG chains are only partially miscible whereas long PEG chains segregate within the PLGA matrix. This behavior has been confirmed experimentally via differential scanning calorimetry and is in agreement with previous observations. Finally, the computed Young’s modulus of PLGA/PEG blends is observed to decrease with the PEG content returning the lowest values for the partial and fully segregated regimens (Y ≈ 1.3 GPa). This work proposes a computational scheme for predicting the physicochemical properties of PLGA/PEG blends paving the way toward the rational design of polymer mixtures for biomedical applications.

A polyesteramide library from dicarboxylic acids and 2,2′-bis(2-oxazoline): synthesis, characterization, nanoparticle formulation and molecular dynamics simulations

A combined experimental and in silico approach enabled tailoring of polyesteramides with respect to formation of aqueous nanoparticle dispersions.

Grafting-Induced Structural Ordering of Lactide Chains

The structure of a grafted layer of lactide chains in the “dry brush” regime immersed in a melt of chemically similar polymer was examined while varying graft lengths. To this end, microsecond atomistic molecular dynamics simulations were performed. Almost no influence of graft length on the fraction of the grafted chains backfolded to the grafting surface was found. However, a structural ordering was unexpectedly observed in the system when the length of the grafted lactide chains was close to approximately 10 Kuhn segments. This ordering of the grafts is characterized by the formation of helical fragments whose structure is in good agreement with the experimental data for the α crystal of the lactide chains. Both the backfolding and the structural ordering may be viewed as the initial stage of the crystallization of the layer of grafted lactide chains. In contrast to the known behavior for conventional polymer brushes in the “dry brush” regime, the structure of the grafted lactide chains can be either amorphous or ordered, depending on the graft length N and the grafting density σ when their product Nσ is fixed.

Toward Predictive Molecular Dynamics Simulations of Asphaltenes in Toluene and Heptane

The conventional definition of asphaltenes is based on their solubility in toluene and their insolubility in heptane. We have utilized this definition to study the influence of partial charge parametrization on the aggregation behavior of asphaltenes using classical atomistic molecular dynamics simulations performed on the microsecond time scale. Under consideration here are toluene- and heptane-based systems with different partial charges parametrized using the general AMBER force field (GAFF). Systems with standard GAFF partial charges calculated by the AM1-BCC and HF/6-31G*(RESP) methods were simulated alongside systems without partial charges. The partial charges implemented differ in terms of the resulting electrical negativity of the asphaltene polyaromatic core, with the AM1-BCC method giving the greatest magnitude of the total core charge. Based on our analysis of the molecular relaxation and orientation, and on the aggregation behavior of asphaltenes in toluene and heptane, we proposed to use the partial charges obtained by the AM1-BCC method for the study of asphaltene aggregates. A good agreement with available experimental data was observed on the sizes of the aggregates, their fractal dimensions, and the solvent entrainment for the model asphaltenes in toluene and heptane. From the results obtained, we conclude that for a better predictive ability, simulation parameters must be carefully chosen, with particular attention paid to the partial charges owing to their influence on the electrical negativity of the asphaltene core and on the asphaltenes aggregation.

X-ray data from a cyclic tensile study of melt-spun poly(3-hydroxybutyrate) P3HB fibers: A reversible mesophase

Wide-angle x-ray diffraction (WAXD) patterns that show mesophases in core-sheath bicomponent fibers and amorphous fibers are presented in section 1.1 of the article. Section 1.2 presents molecular dynamics simulations and scattered intensity calculations of stretched P3HB chains. Sections 1.3–1.6 summarize WAXD and small-angle x-ray scattering (SAXS) data analysis from a tensile study of melt-spun P3HB fibers. Azimuthal profiles are extracted from 2D WAXD patterns at various angular regions and the positions of equatorial reflections and corresponding d-spacings are summarized. Additionally, the extracted structural parameters from SAXS images are summarized. The tensile stress calculations, crystal orientation calculations, applied intensity corrections, calculations of long spacings, coherence lengths and lamellar diameters are explained in the methods subsections 2.3.1–2.3.7. WAXD and SAXS measurements of P3HB fibers were recorded on a Bruker Nanostar U diffractometer (Bruker AXS, Karlsruhe, Germany). The recorded WAXD/SAXS patterns were analyzed with the evaluation software DIFFRAC.EVA (version 4.2., Bruker AXS, Karlsruhe, Germany) and python codes. For more information see ‘Tensile study of melt-spun poly(3-hydroxybutyrate) P3HB fibers: Reversible transformation of a highly oriented phase’ (Perret et al., 2019).

Electrical and Thermal Conductivity of Polylactic Acid (PLA)-Based Biocomposites by Incorporation of Nano-Graphite Fabricated with Fused Deposition Modeling

The aim of the study was to improve the electrical and thermal conductivity of the polylactic acid/wood flour/thermoplastic polyurethane composites by Fused Deposition Modeling (FDM). The results showed that, when the addition amount of nano-graphite reached 25 pbw, the volume resistivity of the composites decreased to 10⁸ Ω·m, which was a significant reduction, indicating that the conductive network was already formed. It also had good thermal conductivity, mechanical properties, and thermal stability. The adding of the redox graphene (rGO) combined with graphite into the composites, compared to the tannic acid-functionalized graphite or the multi-walled carbon nanotubes, can be an effective method to improve the performance of the biocomposites, because the resistivity reduced by one order magnitude and the thermal conductivity increased by 25.71%. Models printed by FDM illustrated that the composite filaments have a certain flexibility and can be printed onto paper or flexible baseplates.