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Charge transfer is a fundamental interface process that can be harnessed for light detection, photovoltaics, and photosynthesis. Recently, charge transfer was exploited in nanophotonics to alter plasmon polaritons by involving additional non-polaritonic materials to activate the charge transfer. Yet, direct charge transfer between polaritonic materials has not been demonstrated. We report the direct charge transfer in pure polaritonic van der Waals (vdW) heterostructures of α-MoO3/graphene. We extracted the Fermi energy of 0.6 eV for graphene by infrared nano-imaging of charge transfer hyperbolic polaritons in the vdW heterostructure. This unusually high Fermi energy is attributed to the charge transfer between graphene and α-MoO3. Moreover, we have observed charge transfer hyperbolic polaritons in multiple energy–momentum dispersion branches with a wavelength elongation of up to 150%. With the support from the density functional theory calculation, we find that the charge transfer between graphene and α-MoO3, absent in mechanically assembled vdW heterostructures, is attributed to the relatively pristine heterointerface preserved in the epitaxially grown vdW heterostructure. The direct charge transfer and charge transfer hyperbolic polaritons demonstrated in our work hold great promise for developing nano-optical circuits, computational devices, communication systems, and light and energy manipulation devices. 

The Student Pathways in Engineering and Computing for Transfers (SPECTRA) program is a newly funded S-STEM program in South Carolina, expected to run through 2026. The program is envisioned to provide a streamlined academic pathway for transfer students from 2-year programs within South Carolina into Clemson University, and provide programming to aid their academic success and social integration. To achieve this, SPECTRA will create cohorts of students at two community/technical colleges (Spartanburg Community College and Trident Technical College) and then support that cohort as they transitioned together into Clemson University. This cohort would then be mentored in how to navigate Clemson University’s academic environment, utilizing available programming such as academic tutoring, field trips to see local engineering companies, etc. A unique component of the SPECTRA program is the requirement that scholarship recipients at Clemson University enroll in two semesters of research, in addition to their participation in social and academic programing. Through this Work in Progress paper, the experience in designing and facilitating these research courses while matriculating through their graduate programs is documented by the authors. Specifically, the design constraints of the research courses, the topics developed for the 2021-2022 cohorts and the envisioned assessment are discussed. 

We demonstrate using theoretical, computational, and experimental studies a morphological instability, in which a polycrystalline nanorod breaks up at grain boundaries (GBs) into an array of isolated domains. Our theoretical model is used to establish a neutral stability surface demarcating stable and unstable perturbations. It is shown that GBs play a destabilizing role in which the critical wavelength for the instability decreases with the increase in the GB energy. We carry out phase field simulations, which reveal accelerated pinch-off kinetics with the increase in the GB energy and predict temporal evolution of interfacial profiles in quantitative agreement with experimental observations.

 

Algorithmic decisions made by machine learning models in high-stakes domains may have lasting impacts over time. However, naive applications of standard fairness criterion in static settings over temporal domains may lead to delayed and adverse effects. To understand the dynamics of performance disparity, we study a fairness problem in Markov decision processes (MDPs). Specifically, we propose return parity, a fairness notion that requires MDPs from different demographic groups that share the same state and action spaces to achieve approximately the same expected time-discounted rewards. We first provide a decomposition theorem for return disparity, which decomposes the return disparity of any two MDPs sharing the same state and action spaces into the distance between group-wise reward functions, the discrepancy of group policies, and the discrepancy between state visitation distributions induced by the group policies. Motivated by our decomposition theorem, we propose algorithms to mitigate return disparity via learning a shared group policy with state visitation distributional alignment using integral probability metrics. We conduct experiments to corroborate our results, showing that the proposed algorithm can successfully close the disparity gap while maintaining the performance of policies on two real-world recommender system benchmark datasets.