Eight samples of personal care and cosmetic products (PCCPs) available on the market were analyzed to isolate and characterize intentionally added polymer particles. In four of the samples, particles of synthetic polymers (microplastics, MP) were detected. Among the various types of PCCPs, the most abundant MP sources were glitter gels and hand cleaning pastes. Results demonstrate that the combination of FTIR and Raman spectroscopy, which is the most widely used approach for MP identification, is not always sufficient for the accurate detection and differentiation of synthetic polymers in the products analyzed. The use of independent methods (solubility tests and DSC thermal analysis) allowed us to achieve additional important information and helped prevent incorrect conclusions regarding the presence of microplastics in PCCPs. DSC thermal analysis proved to be particularly useful in cases wherein a combination of spectroscopic methods was ineffective in MP identification.

Eight different types of tea bags were investigated in this work using dynamic light scattering, electrophoretic mobility and nanoparticle tracking analysis methods to determine the concentration and size of released particles from the bag materials at different temperatures and times. Infrared spectroscopy and calorimetric methods confirmed that the bag material consisted of synthetic (nylon or polypropylene) or natural polymers (cellulose). The size of the released particles lies in the range of 200 nm–1 µm with an initial bimodal distribution and with an average diameter of about 600 nm. The concentration of released particles increases with increasing temperature and brewing time. The released particles of synthetic polymers remain quite stable and are not affected by natural enzymes, while cellulose particles are easily degraded by the proteolytic complex Morikrase. When analyzing the electrophoretic mobility, it was found that the released particles have a negative surface charge, which probably determines the absence of cytotoxicity established on the epithelial cell line Caco-2 even at the maximum values of the observed particle concentrations (14 × 109 particle/L for synthetic polymers and 170 × 109 particle/L for cellulose).

Eight samples of personal care and cosmetic products (PCCPs) available on the market were analyzed to isolate and characterize intentionally added polymer particles. In four of the samples, particles of synthetic polymers (microplastics, MP) were detected. Among the various types of PCCPs, the most abundant MP sources were glitter gels and hand cleaning pastes. Results demonstrate that the combination of FTIR and Raman spectroscopy, which is the most widely used approach for MP identification, is not always sufficient for the accurate detection and differentiation of synthetic polymers in the products analyzed. The use of independent methods (solubility tests and DSC thermal analysis) allowed us to achieve additional important information and helped prevent incorrect conclusions regarding the presence of microplastics in PCCPs. DSC thermal analysis proved to be particularly useful in cases wherein a combination of spectroscopic methods was ineffective in MP identification.

The manufacture of nanocomposite materials based on polyolefins and paraffins is a promising approach for developing materials with enhanced performance characteristics for a wide range of applications. However, a more profound understanding is required to identify how the material properties depend on the type of filler and its concentration, as well as on the structure of the composite matrix. In this study, the effect of adding several types of polycyclic aromatic hydrocarbons (PAH) with different molecular sizes-coronene, ovalene, and hexabenzocoronene (HBC) at concentrations ranging from 10 to 40 wt%-to composites based on polyethylene (PE) or paraffin was studied using all-atomistic computer simulation. Our study revealed how the size of the PAH molecules influences their aggregate behavior, phase transitions, and the structure of the composite matrix. It was shown that coronene exhibits weak aggregation in paraffin and prevents its crystallization, which results in a pronounced decrease in the crystallization temperature of both PE and paraffin in the composites. Ovalene and HBC demonstrate a stronger tendency to aggregate, forming substantially larger aggregates than coronene. Both these PAHs increase the crystallization temperature of PE or paraffin in the composites. At the same time, up to a certain concentration, ovalene can integrate into the paraffin crystal structure at low temperature, promoting its crystallization. In contrast, HBC forms extended columnar aggregates at both high and low temperatures, creating steric hindrances to the formation of the crystalline matrix structure and ultimately reducing the degree of crystallinity of PE or paraffin in the composites. Thus, by adding aromatic nanofillers, it becomes possible to control the microstructure and phase behavior of polyolefin-based composites.

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In this study, we have conducted a comparative analysis of the structural ordering of short oligoetherimide chains (dimers) near the bounding surface, depending on the structure of that surface. In order to clarify the possibility of oligoetherimide ordering along the symmetry axes of graphene, two types of bounding surfaces were considered: graphene, with a regular discrete position of interaction centers (carbon atoms), and a smooth, structureless impermeable wall. The chemical structures of the considered dimers consist of two repeating units of BPDA-P3, ODPA-P3, or aBPDA-P3 thermoplastic polyetherimides. Using all-atom molecular dynamics simulations, the process of structural ordering of the dimers near the surface of the graphene or wall was established. The ODPA-P3 and BPDA-P3 dimers form an ordered state near the graphene surface, while the aBPDA-P3 dimers do not demonstrate structural ordering. The simulation results confirmed that the ordering direction of the BPDA-P3 and ODPA-P3 dimers near the graphene surface is chosen randomly. Comparison of the oligoetherimide structure formed near the attracting wall without a symmetrical location of the interaction centers shows the similarity of the ordering of dimers near the graphene surface and the wall. As in the case of the graphene surface, the ordering of oligoetherimide molecules near the structureless wall demonstrates one direction of ordering. Therefore, we confirmed that the key factor for the onset of ordering is the presence of a confining surface, rather than the symmetrical arrangement of interaction centers in the substrate structure.

In the present work, we investigate the solubility of various gases (CH4, CO2, O2, N2, and He) in a thermoplastic polyimide R-BAPB at room temperature by means of extensive molecular dynamics (MD) simulations, focusing on the effects of thermal prehistory during cooling of polymer melts with different cooling velocities. We show that the cooling velocity employed in MD simulations is an important factor affecting the solubility of gases. Lowering the cooling velocity by 4 orders of magnitude (from 1000 to 0.25 K/ns) reduces the simulated gas solubilities by a factor of 2, with no indication of saturation to steady solubility values. In order to extrapolate the dependence of the gas solubility values to experimental cooling velocities, we use an analytical phenomenological approach based on Struik's theory of isothermal free volume evolution in polymer aging. As a result, we are able to estimate gas solubility values in MD-simulated polymer samples prepared at experimentally relevant cooling velocities. We verify our approach by comparing the direct MD solubility data, the theory-based extrapolation, and the limited experimental data available for R-BAPB polyimide. For additional verification, we extend our approach to ULTEM polyimide, which is much better studied experimentally. We show that the theory-based extrapolation considerably improves the correspondence between modeling and experiment in the case of ULTEM. By combining extensive all-atom MD simulations with an analytical theory, we capture the effects of thermal history on the observed physical properties (specific volume and solubility of gases) and provide the means to bridge a huge gap between the experimental and computational time domains.

-The approach to the prediction of permeability of polymer membranes based on polyimides and polyamidoimides towards helium is described. According to the approach, the activation energy of helium penetration is expressed by a relationship involving the van der Waals volume of the repeat unit and a set of atomic parameters characterizing the contribution of each of the atoms and intermolecular interaction types into the value of activation energy. The contributions of the imide cycles, type of the connection (meta-, para-, or ortho-), and of the CF3, CH3, CO, Cl, F, and SO2 polar groups have been accounted for. Repeated solution of the redundant set of equations obtained on the basis of the proposed relationship has afforded the parameters giving the correspondence of the calculated values and the experimental data on the membranes permeability with correlation coefficient 0.965. Hence, the possibility to search for the structures of polyimides and polyamidoimides with the target permeability without laborious and expensive experiments has been demonstrated.

Asphaltenes represent a novel class of carbon nanofillers that are of potential interest for many applications, including polymer nanocomposites, solar cells, and domestic heat storage devices. In this work, we developed a realistic coarse-grained Martini model that was refined against the thermodynamic data extracted from atomistic simulations. This allowed us to explore the aggregation behavior of thousands of asphaltene molecules in liquid paraffin on a microsecond time scale. Our computational findings show that native asphaltenes with aliphatic side groups form small clusters that are uniformly distributed in paraffin. The chemical modification of asphaltenes via cutting off their aliphatic periphery changes their aggregation behavior: modified asphaltenes form extended stacks whose size increases with asphaltene concentration. At a certain large concentration (44 mol. %), the stacks of modified asphaltenes partly overlap, leading to the formation of large, disordered super-aggregates. Importantly, the size of such super-aggregates increases with the simulation box due to phase separation in the paraffin-asphaltene system. The mobility of native asphaltenes is systematically lower than that of their modified counterparts since the aliphatic side groups mix with paraffin chains, slowing down the diffusion of native asphaltenes. We also show that diffusion coefficients of asphaltenes are not very sensitive to the system size: enlarging the simulation box results in some increase in diffusion coefficients, with the effect being less pronounced at high asphaltene concentrations. Overall, our findings provide valuable insight into the aggregation behavior of asphaltenes on spatial and time scales that are normally beyond the scales accessible for atomistic simulations.

Over the past few decades, the enhancement of polymer thermal conductivity has attracted considerable attention in the scientific community due to its potential for the development of new thermal interface materials (TIM) for both electronic and electrical devices. The mechanical elongation of polymers may be considered as an appropriate tool for the improvement of heat transport through polymers without the necessary addition of nanofillers. Polyimides (PIs) in particular have some of the best thermal, dielectric, and mechanical properties, as well as radiation and chemical resistance. They can therefore be used as polymer binders in TIM without compromising their dielectric properties. In the present study, the effects of uniaxial deformation on the thermal conductivity of thermoplastic PIs were examined for the first time using atomistic computer simulations. We believe that this approach will be important for the development of thermal interface materials based on thermoplastic PIs with improved thermal conductivity properties. Current research has focused on the analysis of three thermoplastic PIs: two semicrystalline, namely BPDA-P3 and R-BAPB; and one amorphous, ULTEMTM. To evaluate the impact of uniaxial deformation on the thermal conductivity, samples of these PIs were deformed up to 200% at a temperature of 600 K, slightly above the melting temperatures of BPDA-P3 and R-BAPB. The thermal conductivity coefficients of these PIs increased in the glassy state and above the glass transition point. Notably, some improvement in the thermal conductivity of the amorphous polyimide ULTEMTM was achieved. Our study demonstrates that the thermal conductivity coefficient is anisotropic in different directions with respect to the deformation axis and shows a significant increase in both semicrystalline and amorphous PIs in the direction parallel to the deformation. Both types of structural ordering (self-ordering of semicrystalline PI and mechanical elongation) led to the same significant increase in thermal conductivity coefficient.

Atomistic computer simulations suggest that asphaltenes with enlarged aromatic cores can improve the performance of heat storage devices based on organic phase change materials.

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 Tg 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 Tg 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 Tg 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 Tg 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.

Adding carbon nanoparticles into organic phase change materials (PCMs) such as paraffin is a common way to enhance their thermal conductivity and to improve the efficiency of heat storage devices. However, the sedimentation stability of such blends can be low due to aggregation of aromatic carbon nanoparticles in the aliphatic paraffin environment. In this paper, we explore whether this important issue can be resolved by the introduction of a polymer agent such as poly(3-hexylthiophene) (P3HT) into the paraffin-nanoparticle blends: P3HT could ensure the compatibility of aromatic carbon nanoparticles with aliphatic paraffin chains. We employed a combination of experimental and computational approaches to determine the impact of P3HT addition on the properties of organic PCMs composed of paraffin and carbon nanoparticles (asphaltenes). Our findings clearly show an increase in the sedimentation stability of paraffin-asphaltene blends, when P3HT is added, through a decrease in average size of asphaltene aggregates as well as in an increase of the blends' viscosity. We also witness the appearance of the yield strength and gel-like behavior of the mixtures. At the same time, the presence of P3HT in the blends has almost no effect on their thermophysical properties. This implies that all properties of the blends, which are critical for heat storage applications, are well preserved. Thus, we demonstrated that adding polyalkylthiophenes to paraffin-asphaltene mixtures led to significant improvement in the performance characteristics of these systems. Therefore, the polymer additives can serve as promising compatibilizers for organic PCMs composed of paraffins and asphaltenes and other types of carbon nanoparticles.

The effect of polymer chain ordering on the transport properties of the polymer membrane was examined for the semi-crystalline heterocyclic polyetherimide (PEI) BPDA-P3 based on 3,3′,4,4′-biphenyltetracarboxylic dianhydride (BPDA) and diamine 1,4-bis [4-(4-aminophenoxy)phenoxy]benzene (P3). All-atom Molecular Dynamics (MD) simulations were used to investigate the gas diffusion process carried out through the pores of a free volume several nanometers in size. The long-term (~30 μs) MD simulations of BPDA-P3 were performed at T = 600 K, close to the experimental value of the melting temperature (Tm ≈ 577 K). It was found during the simulations that the transition of the PEI from an amorphous state to an ordered one occurred. We determined a decrease in solubility for both gases examined (CO2 and CH4), caused by the redistribution of free volume elements occurring during the structural ordering of the polymer chains in the glassy state (Tg ≈ 487 K). By analyzing the diffusion coefficients in the ordered state, the presence of gas diffusion anisotropy was found. However, the averaged values of the diffusion coefficients did not differ from each other in the amorphous and ordered states. Thus, permeability in the observed system is primarily determined by gas solubility, rather than by gas diffusion.

Recently, a strong structural ordering of thermoplastic semi-crystalline polyimides near single-walled carbon nanotubes (SWCNTs) was found that can enhance their mechanical properties. In this study, a comparative analysis of the results of microsecond-scale all-atom computer simulations and experimental measurements of thermoplastic semi-crystalline polyimide R-BAPB synthesized on the basis of dianhydride R (1,3-bis-(3′,4-dicarboxyphenoxy) benzene) and diamine BAPB (4,4′-bis-(4″-aminophenoxy) biphenyl) near the SWCNTs on the rheological properties of nanocomposites was performed. We observe the viscosity increase in the SWCNT-filled R-BAPB in the melt state both in computer simulations and experiments. For the first time, it is proven by computer simulation that this viscosity change is related to the structural ordering of the R-BAPB in the vicinity of SWCNT but not to the formation of interchain linkage. Additionally, strong anisotropy of the rheological properties of the R-BAPB near the SWCNT surface was detected due to the polyimide chain orientation. The increase in the viscosity of the polymer in the viscous-flow state and an increase in the values of the mechanical characteristics (Young’s modulus and yield peak) of the SWCNT-R-BAPB nanocomposites in the glassy state are stronger in the directions along the ordering of polymer chains close to the carbon nanofiller surface. Thus, the new experimental data obtained on the R-BAPB-based nanocomposites filled with SWCNT, being extensively compared with simulation results, confirm the idea of the influence of macromolecular ordering near the carbon nanotube on the mechanical characteristics of the composite material.

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.

A molecular-level insight into phase transformations is in great demand for many molecular systems. It can be gained through computer simulations in which cooling is applied to a system at a constant rate. However, the impact of the cooling rate on the crystallization process is largely unknown. To this end, here we performed atomic-scale molecular dynamics simulations of organic phase-change materials (paraffins), in which the cooling rate was varied over four orders of magnitude. Our computational results clearly show that a certain threshold (1.2 × 1011 K/min) in the values of cooling rates exists. When cooling is slower than the threshold, the simulations qualitatively reproduce an experimentally observed abrupt change in the temperature dependence of the density, enthalpy, and thermal conductivity of paraffins upon crystallization. Beyond this threshold, when cooling is too fast, the paraffin’s properties in simulations start to deviate considerably from experimental data: the faster the cooling, the larger part of the system is trapped in the supercooled liquid state. Thus, a proper choice of a cooling rate is of tremendous importance in computer simulations of organic phase-change materials, which are of great promise for use in domestic heat storage devices.

Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications, such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can predict and reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

Publications showing the prospects of combining computer simulation with atomic force microscopy in the study of polymer biological objects and materials for various purposes are analyzed. The problems that arise when these methods are used together are described. Three main directions are presented in which atomic force microscopy is used in conjunction with computer simulation: study of the surface topography of polymer materials; measurement of the force curve of the interaction of the atomic force probe with polymer structures; and determination of the energy of intermolecular interactions.

An accurate in silico evaluation of the thermal conductivity is critical for improving the thermal properties of organic phase-change materials on a rational basis. To explore the impact of a theoretical model on the computed thermal conductivity, here we employed the equilibrium and the non-equilibrium molecular dynamics (MD) simulations to study paraffin (n-eicosane) bulk samples, in both crystalline and liquid states, with the use of 10 atomistic force fields, both all-atom and united-atom ones. Overall, we found that the equilibrium MD method is preferable for computing the thermal conductivity of n-eicosane samples (at least for a 10-nm-size simulation box). For the n-eicosane crystals, the all-atom models provide larger thermal conductivity coefficients than their united-atom counterparts and, correspondingly, a better match with the experimental data. This is most likely because the crystalline lattice of the models with explicit hydrogen atoms is additionally stabilized by the electrostatic interactions. In contrast, in the liquid state, most all-atom models overestimate the experimental data for n-eicosane, providing thereby worse performance as compared to the united-atom force fields. However, when it comes to the experimentally observed increase in the thermal conductivity of n-eicosane samples upon crystallization, only all-atom models are able to reproduce quantitatively the experimental data. Each force field of n-eicosane was also characterized by an overall score which accumulated the deviations of the computed thermal conductivity coefficients from the experimental values, for both crystalline and liquid samples. It turns out that the best performance among 10 atomistic models of n-eicosane is observed for the all-atom GAFF force field. All in all, our study clearly demonstrates that a proper choice of the model for computing the thermal conductivity is a non-trivial task: even for such relatively simple compounds as paraffins (n-alkanes), different models perform quite differently, in equilibrium and in non-equilibrium MD simulations, as well as in crystalline and liquid phases.