An efficient pathway to high persistence length helicenes from scalable [4]-helicene synthons.

A convergent synthetic strategy to high-persistence length helicenes a regioselective and scalable menthyloxycarbonato-[4]-helicene synthon accessed in 84% yield is reported. To demonstrate the utility of the [4]-helicene building block, bis(menthyloxycarbonato)-[11]-helicene diastereomers were prepared palladium-mediated cross-couplings, followed by a Mallory-type photo-induced annulation reaction. Our synthetic strategy leverages Stille and Heck couplings to synthesize bis(aryl)ethene precursors that exhibit preferential formation of helical products 6π-electrocyclization.

Assessing the Effectiveness of Neural Networks and Molecular Dynamics Simulations in Predicting Viscosity of Small Organic Molecules.

Viscosity is a crucial material property that influences a wide range of applications, including three-dimensional (3D) printing, lubricants, and solvents. However, experimental approaches to measuring viscosity face challenges such as handling multiple samples, high costs, and limited compound availability. To address these limitations, we have developed computational models for viscosity prediction of small organic molecules, utilizing machine learning (ML) and nonequilibrium molecular dynamics (NEMD) simulations.

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning.

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress-strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships.

Investigating the structure-property correlations of pyrolyzed phenolic resin as a function of degree of carbonization.

Carbon-carbon (C/C) composites are attractive materials for high-speed flights and terrestrial atmospheric reentry applications due to their insulating thermal properties, thermal resistance, and high strength-to-weight ratio. It is important to understand the evolving structure-property correlations in these materials during pyrolysis, but the extreme laboratory conditions required to produce C/C composites make it difficult to quantify the properties . This work presents an atomistic modeling methodology to pyrolyze a crosslinked phenolic resin network and track the evolving thermomechanical properties of the skeletal matrix during simulated pyrolysis.

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.

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.

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 ( ), vitrimers have a second topology freezing temperature ( ) 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.

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.

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.

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.

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 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 prediction.

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.

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 ()-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.

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).

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 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.

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 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.

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.

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.

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.

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.