Efficiency and Mechanism of Catalytic Siloxane Exchange in Vitrimer Polymers: Modeling and Density Functional Theory Investigations.

Of late, siloxane-containing vitrimers have gained significant interest due to their fast dynamic characteristics over a reasonable temperature range (180-220 °C), making them well-suited for diverse applications. The exchange reaction pathway in the siloxane vitrimers is accountable for the covalent adaptive network, with the reaction's effectiveness being regulated by either organic or organometallic catalysts. However, directly studying the exchange reaction pathway in the bulk phase using experimental approaches is challenging because of the intricate and interconnected structure of these vitrimers. Here, we perform comprehensive density functional theory (DFT) and experimental investigations to discover the detailed catalytic efficacy of siloxane exchange and provide direction for the reaction process using a 1,5,7-triazabicyclo[4.4.0]dec-5-ene (TBD) catalyst. The calculated transition barrier energy and catalytic efficiency of hexamethyldisiloxane and dihydroxy-dimethylsilane exchange derived from the nudged elastic band with transition-state calculations strongly agree with the experimental findings. In addition, Fukui indices, along with partial charges, are employed to evaluate the nucleophilic and electrophilic behaviors of silanol and siloxane molecules. Our analysis revealed that by utilizing the Fukui indices of both the acid and the base, we can make an approximate estimation of the respective kinetics of the SN2 process in the siloxane exchange reaction mechanism. These findings establish a foundation for comprehending a crucial aspect of the exchange mechanism in siloxane vitrimer systems and could aid in the development of novel catalysts.

Bioinspired Design Rules from Highly Mineralized Natural Composites for Two-Dimensional Composite Design.

Discoveries of two-dimensional (2D) materials, exemplified by the recent entry of MXene, have ushered in a new era of multifunctional materials for applications from electronics to biomedical sensors due to their superior combination of mechanical, chemical, and electrical properties. MXene, for example, can be designed for specialized applications using a plethora of element combinations and surface termination layers, making them attractive for highly optimized multifunctional composites. Although multiple critical engineering applications demand that such composites balance specialized functions with mechanical demands, the current knowledge of the mechanical performance and optimized traits necessary for such composite design is severely limited. In response to this pressing need, this paper critically reviews structure-function connections for highly mineralized 2D natural composites, such as nacre and exoskeletal of windowpane oysters, to extract fundamental bioinspired design principles that provide pathways for multifunctional 2D-based engineered systems. This paper highlights key bioinspired design features, including controlling flake geometry, enhancing interface interlocks, and utilizing polymer interphases, to address the limitations of the current design. Challenges in processing, such as flake size control and incorporating interlocking mechanisms of tablet stitching and nanotube forest, are discussed along with alternative potential solutions, such as roughened interfaces and surface waviness. Finally, this paper discusses future perspectives and opportunities, including bridging the gap between theory and practice with multiscale modeling and machine learning design approaches. Overall, this review underscores the potential of bioinspired design for engineered 2D composites while acknowledging the complexities involved and providing valuable insights for researchers and engineers in this rapidly evolving field.

Recyclability of Vitrimer Materials: Impact of Catalyst and Processing Conditions.

With sustainability at the forefront of material research, recyclable polymers, such as vitrimers, have garnered increasing attention since their introduction in 2011. In addition to a traditional glass-transition temperature (T g), vitrimers have a second topology freezing temperature (T v) above which dynamic covalent bonds allow for rapid stress relaxation, self-healing, and shape reprogramming. Herein, we demonstrate the self-healing, shape memory, and shape reconfigurability properties as a function of experimental conditions, aiming toward recyclability and increased useful lifetime of the material. Of interest, we report the influence of processing conditions, which makes the material vulnerable to degradation. We report a decreased crosslink density with increased thermal cycling and compressive stress. Furthermore, we demonstrate that shape reconfigurability and self-healing are enhanced with increasing compressive stress and catalyst concentration, while their performance as a shape memory material remains unchanged. Though increasing the catalyst concentration, temperature, and compressive stress clearly enhances the recovery performance of vitrimers, we must emphasize its trade-off when considering the material degradation reported here. While vitrimers hold great promise as structural materials, it is vital to understand how experimental parameters impact their properties, stability, and reprocessability before vitrimers reach their true potential.

Machine learning strategies for the structure-property relationship of copolymers.

Establishing the structure-property relationship is extremely valuable for the molecular design of copolymers. However, machine learning (ML) models can incorporate both chemical composition and sequence distribution of monomers, and have the generalization ability to process various copolymer types (e.g., alternating, random, block, and gradient copolymers) with a unified approach are missing. To address this challenge, we formulate four different ML models for investigation, including a feedforward neural network (FFNN) model, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, and a combined FFNN/RNN (Fusion) model. We use various copolymer types to systematically validate the performance and generalizability of different models. We find that the RNN architecture that processes the monomer sequence information both forward and backward is a more suitable ML model for copolymers with better generalizability. As a supplement to polymer informatics, our proposed approach provides an efficient way for the evaluation of copolymers.

Polyimide Hybrid Nanocomposites with Controlled Polymer Filling and Polymer-Matrix Interaction.

Polyimide hybrid nanocomposites with the polyimide confined at molecular length scales exhibit enhanced fracture resistance with excellent thermal-oxidative stability at low density. Previously, polyimide nanocomposites were fabricated by infiltration of a polyimide precursor into a nanoporous matrix followed by sequential thermally induced imidization and cross-linking of the polyimide under nanometer-scale confinement. However, byproducts formed during imidization became volatile at the cross-linking temperature, limiting the polymer fill level and degrading the nanocomposite fracture resistance. This is solved in the present work with an easier approach where the nanoporous matrix is filled with shorter preimidized polyimide chains that are cross-linked while in the pores to eliminate the need for confined imidization reactions, which produces better results compared to the previous study. In addition, we selected a preimidized polyimide that has a higher chain mobility and a stronger interaction with the matrix pore surface. Consequently, the toughness achieved with un-cross-linked preimidized polyimide chains in this work is equivalent to that achieved with the cross-linking of the previously used polyimide chains and is doubled when preimidized polyimide chains are cross-linked. The increased chain mobility enables more efficient polymer filling and higher polymer fill levels. The higher polymer-pore surface interaction increases the energy dissipation during polyimide molecular bridging, increasing the nanocomposite fracture resistance. The combination of the higher polymer fill and the stronger polymer-surface interaction is shown to provide significant improvements to the nanocomposite fracture resistance and is validated with a molecular bridging model.

Recent Updates on Applications of Lipid-Based Nanoparticles for Site- Specific Drug Delivery.

BACKGROUND: Site-specific drug delivery is a widespread and demanding area nowadays. Lipid-based nanoparticulate drug delivery systems have shown promising effects for targeting drugs among lymphatic systems, brain tissues, lungs, and skin. Recently, lipid nanoparticles have been used for targeting the brain via the mucosal route for local therapeutic effects. Lipid nanoparticles (LNPs) can help in enhancing the efficacy and lowering the toxicities of anticancer drugs to treat the tumors, particularly in lymph after metastases of tumors. LNPs contain a nonpolar core that can improve the absorption of lipophilic drugs into the lymph node and treat tumors. Cellular uptake of drugs can also be enhanced using LNPs and therefore, LNPs are the ideal carrier for treating intracellular infections, such as leishmaniasis, tuberculosis and parasitic infection in the brain, etc. Furthermore, specific surface modifications with molecules like mannose, or PEG could improve the macrophage uptake and hence effectively eradicate parasites hiding in macrophages. METHODS: An electronic literature search was conducted to update the advancements in the field of site-specific drug delivery utilizing lipid-based nanoparticles. A search of the Scopus database (https://www.scopus.com/home.uri) was conducted using the following keywords: lipid-based nanoparticles; site-specific delivery. CONCLUSION: Solid lipid nanoparticles have shown site-specific targeted delivery to various organs including the liver, oral mucosa, brain, epidermis, pulmonary and lymphatic systems. These lipidbased systems showed improved bioavailability as well as reduced side effects. Therefore, the focus of this article is to review the recent research studies on LNPs for site-specific or targeting drug delivery.

Unified machine learning protocol for copolymer structure-property predictions.

Structure-property relationships are extremely valuable when predicting the properties of polymers. This protocol demonstrates a step-by-step approach, based on multiple machine learning (ML) architectures, which is capable of processing copolymer types such as alternating, random, block, and gradient copolymers. We detail steps for necessary software installation and construction of datasets. We further describe training and optimization steps for four neural network models and subsequent model visualization and comparison using training and test values. For complete details on the use and execution of this protocol, please refer to Tao et al. (2022).1.

Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature.

In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the glass transition temperature Tg and other properties of polymers has attracted extensive attention. This data-centric approach is much more efficient and practical than the laborious experimental measurements when encountered a daunting number of polymer structures. Various ML models are demonstrated to perform well for Tg prediction. Nevertheless, they are trained on different data sets, using different structure representations, and based on different feature engineering methods. Thus, the critical question arises on selecting a proper ML model to better handle the Tg prediction with generalization ability. To provide a fair comparison of different ML techniques and examine the key factors that affect the model performance, we carry out a systematic benchmark study by compiling 79 different ML models and training them on a large and diverse data set. The three major components in setting up an ML model are structure representations, feature representations, and ML algorithms. In terms of polymer structure representation, we consider the polymer monomer, repeat unit, and oligomer with longer chain structure. Based on that feature, representation is calculated, including Morgan fingerprinting with or without substructure frequency, RDKit descriptors, molecular embedding, molecular graph, etc. Afterward, the obtained feature input is trained using different ML algorithms, such as deep neural networks, convolutional neural networks, random forest, support vector machine, LASSO regression, and Gaussian process regression. We evaluate the performance of these ML models using a holdout test set and an extra unlabeled data set from high-throughput molecular dynamics simulation. The ML model's generalization ability on an unlabeled data set is especially focused, and the model's sensitivity to topology and the molecular weight of polymers is also taken into consideration. This benchmark study provides not only a guideline for the Tg prediction task but also a useful reference for other polymer informatics tasks.

Transesterification in Vitrimer Polymers Using Bifunctional Catalysts: Modeled with Solution-Phase Experimental Rates and Theoretical Analysis of Efficiency and Mechanisms.

Recently, thermoset vitrimer polymers have shown significant promise for structural applications because of their ability to be reshaped and remolded due to their covalent adaptive network (CAN). In these vitrimers, the transesterification reaction is responsible for the CAN, where the efficiency of the reaction is controlled either by organic or by organometallic catalysts. Understanding the mechanism of the transesterification reaction in the bulk phase using direct experimental techniques is extremely difficult due to the highly cross-linked complex structure of thermosetting vitrimers. Therefore, we use solution-phase experiments to investigate the catalytic efficiency and to guide density functional theory (DFT) simulations of the transesterification reaction mechanism with catalysts triazabicyclodecene (TBD), zinc acetate (Zn(OAc)2), 1-methylimidazole (1-MI), and dibutyltin oxide (DBTO). The estimated catalytic efficiency from the detailed DFT reaction path calculations follows the order TBD ≳ DBTO ≳ Zn(OAc)2 > 1-MI, which agrees with the experimental results. In addition to reaction path modeling, the mechanism and the relative rates of the transesterification reaction are analyzed with the assistance of Fukui indices as a measure of electrophilicity and nucleophilicity of atomic sites and with partial charges. It was found that the sum of the nucleophilicity index of the base and the electrophilicity index of the acid of the bifunctional catalysts correlates with the SN2 transition state and tetrahedral intermediate energies, which are related to the barrier of the rate-limiting step. This correlation provides a hypothesis for computational prescreening of potentially better catalysts that have an index in a range of values. These results provide a basis for understanding an important part of the mechanism of transesterification in vitrimer systems and may assist with designing new catalysts.

Breaking the bottleneck: stilbene as a model compound for optimizing 6π e- photocyclization efficiency.

TEMPO was more suitable at photocyclizing stilbene than iodine. As stilbene concentration increased, TEMPO produced a higher yield of phenanthrene at shorter times and significantly reduced the potential for undesired [2+2] cycloadditions. Iodine retarded phenanthrene formation because it promoted isomerization to (E)-stilbene which encouraged [2+2] cycloaddition.

Emerging Applications of Elemental 2D Materials.

As elemental main group materials (i.e., silicon and germanium) have dominated the field of modern electronics, their monolayer 2D analogues have shown great promise for next-generation electronic materials as well as potential game-changing properties for optoelectronics, energy, and beyond. These atomically thin materials composed of single atomic variants of group III through group VI elements on the periodic table have already demonstrated exciting properties such as near-room-temperature topological insulation in bismuthene, extremely high electron mobilities in phosphorene and silicone, and substantial Li-ion storage capability in borophene. Isolation of these materials within the postgraphene era began with silicene in 2010 and quickly progressed to the experimental identification or theoretical prediction of 15 of the 18 main group elements existing as solids at standard pressure and temperatures. This review first focuses on the significance of defects/functionalization, discussion of different allotropes, and overarching structure-property relationships of 2D main group elemental materials. Then, a complete review of emerging applications in electronics, sensing, spintronics, plasmonics, photodetectors, ultrafast lasers, batteries, supercapacitors, and thermoelectrics is presented by application type, including detailed descriptions of how the material properties may be tailored toward each specific application.

Thermal Reshaping Dynamics of Gold Nanorods: Influence of Size, Shape, and Local Environment.

The thermal reshaping of gold nanorods in a polymer matrix is an important phenomenon for many potential applications. However, a fundamental understanding of the various mechanisms that govern the nanorod reshaping dynamics is still lacking. Here, we provide evidence for a phenomenological model of the gold nanorod shape transformation based on the measurements and detailed analysis of the time-resolved thermal reshaping for a variety of gold nanorods having different geometries (aspect ratio, volume, diameter) in a cross-linked epoxy matrix at application relevant temperatures (120-220 °C). Our analysis suggests that (a) the nanorod reshaping dynamics consist of two temporal regimes that are governed by different phenomena and (b) the ultimate amount of reshaping at a given temperature depends strongly on the initial particle geometry and the mechanical stiffness of its surroundings. At short times, the shape transformation is dominated by a curvature-induced surface diffusion process, in which the activation energy for diffusion depends on curvature. At long times, however, the surrounding environment plays a key role in slowing the diffusion and stabilizing the nanorod shape. We show that the long-time behavior can be well described using a modified surface diffusion model that takes into account the slowing of atomic diffusivity as a result of external forces arising from mechanical constraints. The ability to tune both the final shape and the reshaping dynamics in nanocomposites opens up new possibilities in tailoring the optical properties of these materials.

Developing nanotube junctions with arbitrary specifications.

Experimentally synthesized carbon nanotube (CNTs) junctions (either single or with 2D/3D CNT network topology) are expected to have random orientation of defect sites (non-hexagonal rings) around the junction. This random and irregular nature of the junction topology and defect characteristics is expected to affect their strength and durability as well as impact the associated mesoscopic and macroscopic properties. On the contrary, theoretical and computational studies often investigate structure-property relationships of pristine and regular junctions of carbon nanostructures. In this study, we developed a computational framework to model a variety of junction structures between CNTs with arbitrary spatial (orientation and degree of overlap) and intrinsic (chirality) specifications. The developed computational model also has the ability to tune the degree of topological defects around the junction via a variety of defect annihilation approaches. Our method makes use of the primal/dual meshing concept, where the development and manipulation of the junction nodes occur using triangular meshes (primal mesh), which is eventually converted to its dual mesh (honeycomb mesh) to render a fully covalently bonded CNT junction. Here each carbon atom has 3 bonded neighbors (mimicking sp2 hybridization). Under a given set of CNT orientation, overlap and chirality specifications, the approach creates a number of CNT junction configurations with varying degrees of energetic stability, offering an opportunity to investigate the effect of topological arrangement of defects around the junction on mechanical, electrical and thermal properties. In addition, it is shown via few examples that the discussed methodology can easily be extended to create multi-junction nanotube clusters, multi-wall nanotube junctions, as well as true 3D random network structures.

In situ mechanical investigation of carbon nanotube-graphene junction in three-dimensional carbon nanostructures.

Hierarchically organized three-dimensional (3D) carbon nanotubes/graphene (CNTs/graphene) hybrid nanostructures hold great promises in composite and battery applications. Understanding the junction strength between CNTs and graphene is crucial for utilizing such special nanostructures. Here, in situ pulling an individual CNT bundle out of graphene is carried out for the first time using a nanomechanical tester developed in-house, and the measured junction strength of CNTs/graphene is 2.23 ± 0.56 GPa. The post transmission electron microscopy (TEM) analysis of remained graphene after peeling off CNT forest confirms that the failure during pull-out test occurs at the CNT-graphene junction. Such a carefully designed study makes it possible to better understand the interfacial interactions between CNTs and graphene in the 3D CNTs/graphene nanostructures. The coupled experimental and computational effort suggests that the junction between the CNTs and the graphene layer is likely to be chemically bonded, or at least consisting of a mixture of chemical bonding and van der Waals interactions.

Hydrogenation of Penta-Graphene Leads to Unexpected Large Improvement in Thermal Conductivity.

Penta-graphene (PG) has been identified as a novel two-dimensional (2D) material with an intrinsic bandgap, which makes it especially promising for electronics applications. In this work, we use first-principles lattice dynamics and iterative solution of the phonon Boltzmann transport equation (BTE) to determine the thermal conductivity of PG and its more stable derivative, hydrogenated penta-graphene (HPG). As a comparison, we also studied the effect of hydrogenation on graphene thermal conductivity. In contrast to hydrogenation of graphene, which leads to a dramatic decrease in thermal conductivity, HPG shows a notable increase in thermal conductivity, which is much higher than that of PG. Considering the necessity of using the same thickness when comparing thermal conductivity values of different 2D materials, hydrogenation leads to a 63% reduction in thermal conductivity for graphene, while it results in a 76% increase for PG. The high thermal conductivity of HPG makes it more thermally conductive than most other semiconducting 2D materials, such as the transition metal chalcogenides. Our detailed analyses show that the primary reason for the counterintuitive hydrogenation-induced thermal conductivity enhancement is the weaker bond anharmonicity in HPG than PG. This leads to weaker phonon scattering after hydrogenation, despite the increase in the phonon scattering phase space. The high thermal conductivity of HPG may inspire intensive research around HPG and other derivatives of PG as potential materials for future nanoelectronic devices. The fundamental physics understood from this study may open up a new strategy to engineer thermal transport properties of other 2D materials by controlling bond anharmonicity via functionalization.

In silico carbon molecular beam epitaxial growth of graphene on the h-BN substrate: carbon source effect on van der Waals epitaxy.

Against the presumption that hexagonal boron-nitride (h-BN) should provide an ideal substrate for van der Waals (vdW) epitaxy to grow high quality graphene films, carbon molecular beam epitaxy (CMBE) techniques using solid carbon sublimation have reported relatively poor quality of the graphene. In this article, the CMBE growth of graphene on the h-BN substrate is numerically studied in order to identify the effect of the carbon source on the quality of the graphene film. The carbon molecular beam generated by the sublimation of solid carbon source materials such as graphite and glassy carbon is mostly composed of atomic carbon, carbon dimers and carbon trimers. Therefore, the graphene film growth becomes a complex process involving various deposition characteristics of a multitude of carbon entities. Based on the study of surface adsorption and film growth characteristics of these three major carbon entities comprising graphite vapour, we report that carbon trimers convey strong traits of vdW epitaxy prone to high quality graphene growth, while atomic carbon deposition is a surface-reaction limited process accompanied by strong chemisorption. The vdW epitaxial behaviour of carbon trimers is found to be substantial enough to nucleate and develop into graphene like planar films within a nanosecond of high flux growth simulation, while reactive atomic carbons tend to impair the structural integrity of the crystalline h-BN substrate upon deposition to form an amorphous interface between the substrate and the growing carbon film. The content of reactive atomic carbons in the molecular beam is suspected to be the primary cause of low quality graphene reported in the literature. A possible optimization of the molecular beam composition towards the synthesis of better quality graphene films is suggested.

Thermal Conductivity of Wurtzite Zinc-Oxide from First-Principles Lattice Dynamics--a Comparative Study with Gallium Nitride.

Wurtzite Zinc-Oxide (w-ZnO) is a wide bandgap semiconductor that holds promise in power electronics applications, where heat dissipation is of critical importance. However, large discrepancies exist in the literature on the thermal conductivity of w-ZnO. In this paper, we determine the thermal conductivity of w-ZnO using first-principles lattice dynamics and compare it to that of wurtzite Gallium-Nitride (w-GaN)--another important wide bandgap semiconductor with the same crystal structure and similar atomic masses as w-ZnO. However, the thermal conductivity values show large differences (400 W/mK of w-GaN vs. 50 W/mK of w-ZnO at room temperature). It is found that the much lower thermal conductivity of ZnO originates from the smaller phonon group velocities, larger three-phonon scattering phase space and larger anharmonicity. Compared to w-GaN, w-ZnO has a smaller frequency gap in phonon dispersion, which is responsible for the stronger anharmonic phonon scattering, and the weaker interatomic bonds in w-ZnO leads to smaller phonon group velocities. The thermal conductivity of w-ZnO also shows strong size effect with nano-sized grains or structures. The results from this work help identify the cause of large discrepancies in w-ZnO thermal conductivity and will provide in-depth understanding of phonon dynamics for the design of w-ZnO-based electronics.

Modeling the Role of Bulk and Surface Characteristics of Carbon Fiber on Thermal Conductance across the Carbon-Fiber/Matrix Interface.

The rapid heating of carbon-fiber-reinforced polymer matrix composites leads to complex thermophysical interactions which not only are dependent on the thermal properties of the constituents and microstructure but are also dependent on the thermal transport between the fiber and resin interfaces. Using atomistic molecular dynamics simulations, the thermal conductance across the interface between a carbon-fiber near-surface region and bismaleimide monomer matrix is calculated as a function of the interface and bulk features of the carbon fiber. The surface of the carbon fiber is modeled as sheets of graphitic carbon with (a) varying degrees of surface functionality, (b) varying defect concentrations in the surface-carbon model (pure graphitic vs partially graphitic), (c) varying orientation of graphitic carbon at the interface, (d) varying interface saturation (dangling vs saturated bonds), (e) varying degrees of surface roughness, and (f) incorporating high conductive fillers (carbon nanotubes) at the interface. After combining separately equilibrated matrix system and different surface-carbon models, thermal energy exchange is investigated in terms of interface thermal conductance across the carbon fiber and the matrix. It is observed that modifications in the studied parameters (a-f) often lead to significant modulation of thermal conductance across the interface and, thus, showcases the role of interface tailoring and surface-carbon morphology toward thermal energy exchange. More importantly, the results provide key bounds and a realistic degree of variation to the interface thermal conductance values at fiber/matrix interfaces as a function of different surface-carbon features.

Thermal anisotropy in nano-crystalline MoS2 thin films.

In this work, we grow thin MoS2 films (50-150 nm) uniformly over large areas (>1 cm(2)) with strong basal plane (002) or edge plane (100) orientations to characterize thermal anisotropy. Measurement results are correlated with molecular dynamics simulations of thermal transport for perfect and defective MoS2 crystals. The correlation between predicted (simulations) and measured (experimental) thermal conductivity are attributed to factors such as crystalline domain orientation and size, thereby demonstrating the importance of thermal boundary scattering in limiting thermal conductivity in nano-crystalline MoS2 thin films. Furthermore, we demonstrate that the cross-plane thermal conductivity of the films is strongly impacted by exposure to ambient humidity.

A novel nano-configuration for thermoelectrics: helicity induced thermal conductivity reduction in nanowires.

In this article, we propose a novel helical nano-configuration towards the designing of high ZT thermoelectric materials. Non-equilibrium molecular dynamics (NEMD) simulations for 'model' bi-component nanowires indicate that a significant reduction in thermal conductivity, similar to that of flat superlattice nanostructures, can be achieved using a helical geometric configuration. The reduction is attributed to a plethora of transmissive and reflective phonon scattering events resulting from the steady alteration of phonon propagating direction that emerges from the continuous rotation of the helical interface. We also show that increasing the relative mass ratio of the two components lowers the phonon energy transmission at the interface due to differences in vibrational frequency spectra, thereby relatively 'easing' the phonon energy propagation along the helical pathway. While the proposed mechanisms result in a reduced lattice thermal conductivity, the continuous nature of the bi-component nanowire would not be expected to significantly reduce its electrical counterpart, as often occurs in superlattice/alloy nanostructures. Hence, we postulate that the helical configuration of atomic arrangement provides an attractive and general framework for improved thermoelectric material assemblies independent of the specific chemical composition.