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Semiconductor nanocrystals (NCs) offer prospective use as active optical elements in photovoltaics, light-emitting diodes, lasers, and photocatalysts due to their tunable optical absorption and emission properties, high stability, and scalable solution processing, as well as compatibility with additive manufacturing routes. Over the course of experiments, during device fabrication, or while in use commercially, these materials are often subjected to intense or prolonged electronic excitation and high carrier densities. The influence of such conditions on ligand integrity and binding remains underexplored. Here, we expose CdSe NCs to laser excitation and monitor changes in oleate that is covalently attached to the NC surface using nuclear magnetic resonance as a function of time and laser intensity. Higher photon doses cause increased rates of ligand loss from the particles, with upward of 50% total ligand desorption measured for the longest, most intense excitation. Surprisingly, for a range of excitation intensities, fragmentation of the oleate is detected and occurs concomitantly with formation of aldehydes, terminal alkenes, H2, and water. After illumination, NC size, shape, and bandgap remain constant although low-energy absorption features (Urbach tails) develop in some samples, indicating formation of substantial trap states. The observed reaction chemistry, which here occurs with low photon to chemical conversion efficiency, suggests that ligand reactivity may require examination for improved NC dispersion stability but can also be manipulated to yield desired photocatalytically accessed chemical species. 

Transfer operators offer linear representations and global, physically meaningful features of nonlinear dynamical systems. Discovering transfer operators, such as the Koopman operator, require careful crafted dictionaries of observables, acting on states of the dynamical system. This is ad hoc and requires the full dataset for evaluation. In this paper, we offer an optimization scheme to allow joint learning of the observables and Koopman operator with online data. Our results show we are able to reconstruct the evolution and represent the global features of complex dynamical systems. 

Climate change has increased the frequency and severity of extreme weather events such as hurricanes and winter storms. The complex interplay of floods with tides, runoff, and sediment creates additional hazards – including erosion and the undermining of urban infrastructure – consequently impacting the health of our rivers and ecosystems. Observations of these underwater phenomena are rare, because satellites and sensors mounted on aerial vehicles cannot penetrate the murky waters. Autonomous Surface Vehicles (ASVs) provides a means to track and map these complex and dynamic underwater phenomena. This work highlights preliminary results of high-resolution data gathering with ASVs, equipped with a suite of sensors capable of measuring physical and chemical parameters of the river. Measurements were acquired along the lower Schuylkill River in the Philadelphia area at high-tide and low-tide conditions. The data will be leveraged to improve our understanding of changes in bathymetry due to floods; the dynamics of mixing and stagnation zones and their impact on water quality; and the dynamics of suspension and resuspension of fine sediment. The data will also provide insight into the development of adaptive sampling strategies for ASVs that can maximize the information gain for future field experiments.