We use molecular dynamics simulations to investigate the effect of polypeptoid sequence on the structure and dynamics of its hydration waters. Polypeptoids provide an excellent platform to study small-molecule hydration in disordered polymers, as they can be precisely synthesized with a variety of sidechain chemistries. We examine water behavior near a set of peptoid oligomers in which the number and placement of nonpolar versus polar sidechains are systematically varied.To do this, we leverage a new computational workflow enabling accurate sampling of polypeptoid conformations. We find that the hydration waters are less dense, are more tetrahedral, and have slower dynamics compared to bulk water. The magnitude of these shifts increases with the number of nonpolar groups. We also find that shifts in the water structure and dynamics are strongly correlated, suggesting that experimental insight into the dynamics of hydration water obtained by Overhauser dynamic nuclear polarization (ODNP) also contains information about water structural properties. We then demonstrate the ability of ODNP to probe site-specific dynamics of hydration water near these model peptoid systems.

As the need for renewable and degradable alternative plastics grows, efforts have been made to develop biobased polymer architectures with tunable properties. We developed the synthesis of a new, biobased, and degradable graft copolymer using a grafting-through approach. A one-pot strategy was developed for the synthesis of telechelic poly­(l-lactide) (PLLA) with a polymerizable lactone group at one chain-end. Using mild conditions, we obtained the lactone-functionalized polymer after three steps. Conditions were optimized, and complete conversion was reached in each step. The polyesters were characterized by 1H and 13C nuclear magnetic resonance (NMR) spectroscopies, size exclusion chromatography (SEC), and matrix-assisted laser desorption ionization–time-of-flight (MALDI-TOF) mass spectrometry. The macromonomers were then copolymerized with γ-methyl-ε-caprolactone (γMCL) to prepare fully aliphatic polyester graft copolymers. Using optimized conditions, we analyzed a series of graft copolymers with graft length, backbone length, and graft density variations by NMR spectroscopy, SEC, thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). Mechanical properties were also evaluated, and the corresponding structure–property relationships were studied. Materials with highly tunable mechanical properties were obtained. One of the graft polymers with 30 wt % PLLA showed impressive elastomeric behavior with about 17 MPa stress at break and 1400% strain at break and a residual strain at 25% after the second cycle and 40% after the 10th cycle. This study opens the door to the use of ring-opening transesterification polymerization (ROTEP) for the synthesis of new fully biobased graft copolymers.

Water’s unique thermophysical properties and how it mediates aqueous interactions between solutes have long been interpreted in terms of its collective molecular structure. The seminal work of Errington and Debenedetti [Nature 2001, 409, 318–321] revealed a striking hierarchy of relationships among the thermodynamic, dynamic, and structural properties of water, motivating many efforts to understand (1) what measures of water structure are connected to different experimentally accessible macroscopic responses and (2) how many such structural metrics are adequate to describe the collective structural behavior of water. Diffusivity constitutes a particularly interesting experimentally accessible equilibrium property to investigate such relationships because advanced NMR techniques allow the measurement of bulk and local water dynamics in nanometer proximity to molecules and interfaces, suggesting the enticing possibility of measuring local diffusivities that report on water structure. Here, we apply statistical learning methods to discover persistent structure–dynamic correlations across a variety of simulated aqueous mixtures, from alcohol–water to polypeptoid–water systems. We investigate a variety of molecular water structure metrics and find that an unsupervised statistical learning algorithm (namely, sequential feature selection) identifies only two or three independent structural metrics that are sufficient to predict water self-diffusivity accurately. Surprisingly, the translational diffusivity of water across all mixed systems studied here is strongly correlated with a measure of tetrahedral order given by water’s triplet angle distribution. We also identify a separate small number of structural metrics that well predict an important thermodynamic property, the excess chemical potential of an idealized methane-sized hydrophobe in water. Ultimately, we offer a Bayesian method of inferring water structure by using only structure–dynamics linear regression models with experimental Overhauser dynamic nuclear polarization (ODNP) measurements of water self-diffusivity. This study thus quantifies the relationships among several distinct structural order parameters in water and, through statistical learning, reveals the potential to leverage molecular structure to predict fundamental thermophysical properties. In turn, these findings suggest a framework for solving the inverse problem of inferring water’s molecular structure using experimental measurements such as ODNP studies that probe local water properties.