Closer Look at Inverse Electron Demand Diels–Alder and Nucleophilic Addition Reactions on s-Tetrazines Using Enhanced Sampling Methods

Designed and tailor-made double hydrophilic block copolymer-graphene nanoplatelet hybrids for reinforcing epoxy thermosets

Because of their propensity to build micellar nanostructures, amphiphilic block copolymers (ABCs) are an appropriate and unique toughening agent for epoxy systems individually on their own and in grafted form. The presence of epoxiphilic and phobic ends in ABCs is responsible for the self-assembly and the micellar structure. Nanofiller-grafted ABCs can effectively enhance the toughness of epoxy via the synergistic interaction of nanofillers and the ABCs. Even though there is sound literature supporting the effect of ABCs in epoxy, the action of double hydrophilic block copolymers (DHBC) in the epoxy matrix is less handled. Hence, the grafting of nanofillers in DHBCs and their subsequent role in tuning the properties of epoxy is a new concept. Hence this paper tries to bridge the gap via studying the effect of grafted fillers based on DHBCs in epoxy matrix. As a result, the current study focuses on the synthesis of double hydrophilic graphene nanoplatelets (rGO-g-DHBC) via nitrogen oxide-mediated polymerization for epoxy toughening application. The prepared rGO-g-DHBC was effectively utilized for epoxy toughening applications, resulting in a 457% improvement in toughness without compromising its inherent tensile strength. The mechanism behind the improved toughness was elucidated with the help of a scanning electron microscope, and the thermal, and rheological characteristics were studied.

Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space

Collective variables (CVs) are crucial parameters in enhanced sampling calculations and strongly impact the quality of the obtained free energy surface. However, many existing CVs are unique to and dependent on the system they are constructed with, making the developed CV non-transferable to other systems. Herein, we develop a non-instructor-led deep autoencoder neural network (DAENN) for discovering general-purpose CVs. The DAENN is used to train a model by learning molecular representations upon unbiased trajectories that contain only the reactant conformers. The prior knowledge of nonconstraint reactants coupled with the here-introduced topology variable and loss-like penalty function are only required to make the biasing method able to expand its configurational (phase) space to unexplored energy basins. Our developed autoencoder is efficient and relatively inexpensive to use in terms of a priori knowledge, enabling one to automatically search for hidden CVs of the reaction of interest.