Publications

The performance of organic photovoltaic (OPV) devices is currently limited by modest short-circuit current densities. Approaches toward improving this output parameter may provide new avenues to advance OPV technologies and the basic science of charge transfer in organic semiconductors. This work highlights how steric control of the charge separation interface can be effectively tuned in OPV devices. By introducing an octylphenyl substituent onto the investigated polymer backbones, the thermally relaxed charge-transfer state, and potentially excited charge-transfer states, can be raised in energy. This decreases the barrier to charge separation and results in increased photocurrent generation. This finding is of particular significance for nonfullerene OPVs, which have many potential advantages such as tunable energy levels and spectral breadth, but are prone to poor exciton separation efficiencies. Computational, spectroscopic, and synthetic methods were combined to develop a structure-property relationship that correlates polymer substituents with charge-transfer state energies and, ultimately, device efficiencies.

Methods to access chiral sulfur (VI) pharmacophores are of interest in medicinal and synthetic chemistry. We report the desymmetrization of unprotected sulfonimidamides via asymmetric acylation with a cinchona-phosphinate catalyst. The desired products are formed in excellent yield and enantioselectivity with no observed bis-acylation. A data science-driven approach to substrate scope evaluation was coupled to high throughput experimentation (HTE) to facilitate statistical modeling in order to inform mechanistic studies. Reaction kinetics, catalyst structural studies, and density functional theory (DFT) transition state analysis elucidated the turnover-limiting step to be the collapse of the tetrahedral intermediate and provided key insights into the catalyst-substrate structure-activity relationships responsible for the origin of enantioselectivity. This study offers a reliable method for accessing enantioenriched sulfonimidamides to propel their application as pharmacophores and serves as an example of the mechanistic insight that can be gleaned from integrating data science and traditional physical organic techniques.

Significance The cobra mutants of Arabidopsis , such as cob-6 , have impaired growth associated with a defect in cellulose synthesis. Mutations in MEDIATOR16 ( MED16 ) reduce the number of misregulated genes in cob-6 mutants and suppress the phenotypes. This observation implicates MED16 in transcriptional responses to cell wall defects. Ectopic expression of two pectin methylesterase inhibitors (PMEIs) identified in a suppressor screen partially suppressed the growth defect in the cob-6 mutant. The results confirm that the PMEIs have significant in vivo activity, and provide evidence that pectin esterification can modulate cell wall properties.

Abstract A Pd‐catalyzed enantioselective synthesis of azaspirocycles is developed.

Sum frequency generation surface vibrational spectroscopy and kinetic measurements using gas chromatography have been used to systematically study the adsorption and hydrogenation of furan over Pt(111) and Pt(100) single-crystal surfaces and size-controlled 1.0-nm, 3.5-nm and 7.0-nm Pt nanoparticles at Torr pressures (10 Torr of furan, 100 Torr of H(2)) to form dihydrofuran, tetrahydrofuran, and the ring-cracking products butanol and propylene. As determined by SFG, the furan ring lies parallel to all Pt surfaces studied under hydrogenation conditions. Upright THF and the oxametallacycle intermediate are observed over the nanoparticle catalysts under reaction conditions. Butoxy increases in surface concentration over Pt(111) with increasing temperature in agreement with selectivity trends.

Multipartite entanglement refers to the simultaneous entanglement between multiple subsystems of a many-body quantum system. While multipartite entanglement can be difficult to quantify analytically, it is known that it can be witnessed through the Quantum Fisher information (QFI), a quantity that can also be related to dynamical Kubo response functions. In this work, we first show that the finite temperature QFI can generally be expressed in terms of a static structure factor of the system, plus a correction that vanishes as $T\rightarrow 0$. We argue that this implies that the static structure factor witnesses multipartite entanglement near quantum critical points at temperatures below a characteristic energy scale that is determined by universal properties, up to a non-universal amplitude. Therefore, in systems with a known static structure factor, we can deduce finite temperature scaling of multipartite entanglement and low temperature entanglement depth without knowledge of the full dynamical response function of the system. This is particularly useful to study 1D quantum critical systems in which sub-power-law divergences can dominate entanglement growth, where the conventional scaling theory of the QFI breaks down. The 1D spin-$\frac{1}{2}$ antiferromagnetic Heisenberg model is an important example of such a system, and we show that multipartite entanglement in the Heisenberg chain diverges non-trivially as $\sim \log(1/T)^{3/2}$. We verify these predictions with calculations of the QFI using conformal field theory and matrix product state simulations. Finally we discuss the implications of our results for experiments to probe entanglement in quantum materials, comparing to neutron scattering data in KCuF$_3$, a material well-described by the Heisenberg chain.

Reversible cluster formation can be observed with good reproducibility. In this study, we can also notice the importance of the initial structure and the sequence of adsorption: - ethylene is more likely to induce step break-up on Pt(332) than on Pt(557)- after a subsequent dosing of CO to the system, no drastic change can be detected- When ethylene is added to the system, CO-induced clusters are stable on Pt(557), but on Pt(332) the clusters are replaced by a new kind of structure. HP-STM proved to be an efficient technique to study the structural changes of a catalyst under realistic conditions (pressure and temperature).

Inadequate reliability is a well-known issue for reinforcement learning (RL) algorithms. This problem has gained increasing attention in recent years, and efforts to improve it have grown substantially. To aid RL researchers and production users with the evaluation and improvement of reliability, we propose a novel set of metrics that quantitatively measure different aspects of reliability. In this work, we address variability and risk, both during training and after learning (on a fixed policy). We designed these metrics to be general-purpose, and we also designed complementary statistical tests to enable rigorous comparisons on these metrics. In this paper, we first describe the desired properties of the metrics and their design, the aspects of reliability that they measure, and their applicability to different scenarios. We then describe the statistical tests and make additional practical recommendations for reporting results. Finally, we apply our metrics to a set of common RL algorithms and environments, compare them, and analyze the results.