The impact of surface chemistry on interfacial molecular orientation is studied for polymeric organic field-effect transistor (oFET) assemblies using vibrational sum frequency generation (VSFG) spectroscopy. The carbonÀ carbon vibrational modes on the backbone of side-chain deuterated poly(3hexylthiophene) (DP3HT) are demonstrated to be an excellent handle for molecular orientation. DP3HT is utilized to avoid overlap in this spectral region with alkyl CH bending vibrations. Raman and FTIR spectroscopies are used to characterize the vibrational spectra of the thin films, and band assignments are confirmed by DFT calculations. Organosilane self-assembled monolayers are used to prepare oFET dielectrics with a range of surface energies, as measured by their water contact angles. The surface chemistry is found to have a profound influence on the field-effect carrier mobilities with lower surface energies producing higher mobilities. Polarization selective VSFG spectroscopy is then used to determine the relative orientation of the CdC symmetric stretching mode with respect to the surface normal. High field-effect mobilities for DP3HT on low surface energy functionalized dielectrics are directly correlated with the relative orientations of this vibrational transition dipole moment as measured by VSFG. The connection between this nonlinear spectroscopic observable and molecular structure enables this approach to confirm that low surface energy dielectrics lead to edge-on orientation of DP3HT conjugated chains with consequently higher mobilities.

We introduce a novel grafting-through polymerization strategy to synthesize dynamic bottlebrush polymers and elastomers in one step using light to construct a disulfide-containing backbone. The key starting materialα-lipoic acid (LA)is commercially available, inexpensive, and biocompatible. When installed on the chain end(s) of poly(dimethylsiloxane) (PDMS), the cyclic disulfide unit derived from LA polymerizes under ultraviolet (UV) light in ambient conditions. Significantly, no additives such as initiator, solvent, or catalyst are required for efficient gelation. Formulations that include bis-LAfunctionalized cross-linker yield bottlebrush elastomers with high gel fractions (83−98%) and tunable, supersoft shear moduli in the ∼20− 200 kPa range. An added advantage of these materials is the dynamic disulfide bonds along each bottlebrush backbone, which allow for light-mediated self-healing and on-demand chemical degradation. These results highlight the potential of simple and scalable synthetic routes to generate unique bottlebrush polymers and elastomers based on PDMS.

The effect of molecular weight on organic photovoltaic device performance is investigated for a series of low-band-gap (ca. 1.65 eV) poly(3-hexadecylthienylene vinylene)s (C16-PTVs) prepared by acyclic diene metathesis (ADMET) polymerization. By utilizing monomers of varying cis:trans (Z:E) content, seven C16-PTVs were prepared with a number-average molecular weight range of 6–30 kg/mol. Polymers were characterized by size-exclusion chromatography, 1H NMR spectroscopy, ultraviolet–visible spectroscopy, thermogravimetric analysis, wide-angle X-ray scattering, and differential scanning calorimetry. C16-PTVs were integrated into bulk-heterojunction (BHJ) solar cells with [6,6]-phenyl-C61-butyric acid methyl ester (PCBM), and conversion efficiency was found to increase with increasing molecular weight. This observation is attributable to an increase in polymer aggregation in the solid state and a corresponding increase in hole mobility. Finally, phase behavior and morphology of the C16-PTV:PCBM active layers were investigated by differential scanning calorimetry and atomic force microscopy, respectively.

The effects of olefin content and alkyl chain placement on the properties of two poly(thienylene vinylene)- (PTV-) based polymer series were investigated. Polymers were prepared by ruthenium-catalyzed acyclic diene metathesis (ADMET) polymerization of four dipropenyl monomers. All polymers were thoroughly characterized with a variety of spectroscopic, thermal, and electronic techniques. Tuning the olefin content had direct impacts on optical and organic solar cell (OSC) behavior while systematic changes to alkyl substitution patterns manifested as differences in optical, thermal, and microstructural properties. Density functional theory (DFT) analysis provided support for the observed differences among all polymers. In addition, 13C NMR and IR analysis of selected polymers helped to confirm the stereochemistry of the PTV backbone.

Achieving thermodynamic faithfulness and transferability across state points is an outstanding challenge in the bottom-up coarse graining of molecular models, with many efforts focusing on augmenting the form of coarse-grained interaction potentials to improve transferability. Here, we revisit the critical role of the simulation ensemble and the possibility that even simple models can be made more predictive through a smarter choice of ensemble. We highlight the efficacy of coarse graining from ensembles where variables conjugate to the thermodynamic quantities of interest are forced to respond to applied perturbations. For example, to learn activity coefficients, it is natural to coarse grain from ensembles with spatially varying external potentials applied to one species to force local composition variations and fluctuations. We apply this strategy to coarse grain both an atomistic model of water and methanol and a binary mixture of spheres interacting via Gaussian repulsions and demonstrate near-quantitative capture of activity coefficients across the whole composition range. Furthermore, the approach is able to do so without explicitly measuring and targeting activity coefficients during the coarse graining process; activity coefficients are only computed after-the-fact to assess accuracy. We hypothesize that ensembles with applied thermodynamic potentials are more “thermodynamically informative.” We quantify this notion of informativeness using the Fisher information metric, which enables the systematic design of optimal bias potentials that promote the learning of thermodynamically faithful models. The Fisher information is related to variances of structural variables, highlighting the physical basis underlying the Fisher information’s utility in improving coarse-grained models.