Assessment of the partial saddle point approximation in field-theoretic polymer simulations.

Field-theoretic simulations are numerical treatments of polymer field theory models that go beyond the mean-field self-consistent field theory level and have successfully captured a range of mesoscopic phenomena. Inherent in molecularly-based field theories is a "sign problem" associated with complex-valued Hamiltonian functionals. One route to field-theoretic simulations utilizes the complex Langevin (CL) method to importance sample complex-valued field configurations to bypass the sign problem. Although CL is exact in principle, it can be difficult to stabilize in strongly fluctuating systems. An alternate approach for blends or block copolymers with two segment species is to make a "partial saddle point approximation" (PSPA) in which the stiff pressure-like field is constrained to its mean-field value, eliminating the sign problem in the remaining field theory, allowing for traditional (real) sampling methods. The consequences of the PSPA are relatively unknown, and direct comparisons between the two methods are limited. Here, we quantitatively compare thermodynamic observables, order-disorder transitions, and periodic domain sizes predicted by the two approaches for a weakly compressible model of AB diblock copolymers. Using Gaussian fluctuation analysis, we validate our simulation observations, finding that the PSPA incorrectly captures trends in fluctuation corrections to certain thermodynamic observables, microdomain spacing, and location of order-disorder transitions. For incompressible models with contact interactions, we find similar discrepancies between the predictions of CL and PSPA, but these can be minimized by regularization procedures such as Morse calibration. These findings mandate caution in applying the PSPA to broader classes of soft-matter models and systems.

Improved Elastic Recovery from ABC Triblock Terpolymers.

The promise of ABC triblock terpolymers for improving the mechanical properties of thermoplastic elastomers is demonstrated by comparison with symmetric ABA/CBC analogs having similar molecular weights and volume fraction of B and A/C domains. The ABC architecture enhances elasticity (up to 98% recovery over 10 cycles) in part through essentially full chain bridging between discrete hard domains leading to the minimization of mechanically unproductive loops. In addition, the unique phase space of ABC triblocks also enables the fraction of hard-block domains to be higher (fhard ≈ 0.4) while maintaining elasticity, which is traditionally only possible with non-linear architectures or highly asymmetric ABA triblock copolymers. These advantages of ABC triblock terpolymers provide a tunable platform to create materials with practical applications while improving our fundamental understanding of chain conformation and structure-property relationships in block copolymers.

Complete Photonic Band Gaps with Nonfrustrated ABC Bottlebrush Block Polymers.

Bottlebrush block polymers are a promising platform for self-assembled photonic materials, yet most work has been limited to one-dimensional photonic crystals based on the lamellar phase. Here we demonstrate with simulation that nonfrustrated ABC bottlebrush block polymers can be used to self-assemble three-dimensional photonic crystals with complete photonic band gaps. To show this, we have developed a computational approach that couples self-consistent field theory (SCFT) simulations to Maxwell's equations, thereby permitting a direct link between molecular design, self-assembly, and photonic band structures. Using this approach, we calculate the phase diagram of nonfrustrated ABC bottlebrush block polymers and identify regions where the alternating gyroid and alternating diamond phases are stable. By computing the photonic band structures of these phases, we demonstrate that complete band gaps can be found in regions of thermodynamic stability, thereby suggesting a route to realize these photonic materials experimentally. Furthermore, we demonstrate that gap size depends on volume fraction, segregation strength, and polymer architecture, and we identify a design strategy based on symmetry breaking that can achieve band gaps for lower values of refractive index contrast. Taken together, the approach presented here provides a powerful and flexible tool for predicting both the self-assembly and photonic band structures of polymeric materials.

Long-term calibration models to estimate ozone concentrations with a metal oxide sensor.

Ozone (O3) is a potent oxidant associated with adverse health effects. Low-cost O3 sensors, such as metal oxide (MO) sensors, can complement regulatory O3 measurements and enhance the spatiotemporal resolution of measurements. However, the quality of MO sensor data remains a challenge. The University of Utah has a network of low-cost air quality sensors (called AirU) that primarily measures PM2.5 concentrations around the Salt Lake City valley (Utah, U.S.). The AirU package also contains a low-cost MO sensor ($8) that measures oxidizing/reducing species. These MO sensors exhibited excellent laboratory response to O3 although they exhibited some intra-sensor variability. Field performance was evaluated by placing eight AirUs at two Division of Air Quality (DAQ) monitoring stations with O3 federal equivalence methods for one year to develop long-term multiple linear regression (MLR) and artificial neural network (ANN) calibration models to predict O3 concentrations. Six sensors served as train/test sets. The remaining two sensors served as a holdout set to evaluate the applicability of the new calibration models in predicting O3 concentrations for other sensors of the same type. A rigorous variable selection method was also performed by least absolute shrinkage and selection operator (LASSO), MLR and ANN models. The variable selection indicated that the AirU's MO oxidizing species and temperature measurements and DAQ's solar radiation measurements were the most important variables. The MLR calibration model exhibited moderate performance (R2 = 0.491), and the ANN exhibited good performance (R2 = 0.767) for the holdout set. We also evaluated the performance of the MLR and ANN models in predicting O3 for five months after the calibration period and the results showed moderate correlations (R2s of 0.427 and 0.567, respectively). These low-cost MO sensors combined with a long-term ANN calibration model can complement reference measurements to understand geospatial and temporal differences in O3 levels.

Bioelectrocatalytic NAD+/NADH inter-conversion: transformation of an enzymatic fuel cell into an enzymatic redox flow battery.

Diaphorase and a benzylpropylviologen redox polymer were combined to create a bioelectrode that can both oxidize NADH and reduce NAD+. We demonstrate how bioelectrocatalytic NAD+/NADH inter-conversion can transform a glucose/O2 enzymatic fuel cell (EFC) with an open circuit potential (OCP) of 1.1 V into an enzymatic redox flow battery (ERFB), which can be rapidly recharged by operation as an EFC.

NAD-dependent dehydrogenase bioelectrocatalysis: the ability of a naphthoquinone redox polymer to regenerate NAD.

Electron mediation between NAD-dependent enzymes using quinone moieties typically requires the use of a diaphorase as an intermediary enzyme. The ability for a naphthoquinone redox polymer to independently oxidize enzymatically-generated NADH is demonstrated for application to glucose/O2 enzymatic fuel cells.