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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Inelastic Neutron Scattering from Thin Film Biaxially Oriented Polyethylene Terephthalate
Recent interest in emerging processes for polymer manufacturing and bio-based chemistries for direct chemical recycling/upcycling has motivated new research focused on a deeper understanding of atomic-scale polymer properties and how they influence macroscopic phenomena. Uncovering the fundamental properties of polymers that give rise to macroscopic behavior could enable new pathways for improved recyclability or utilization of alternative “greener” polymer analogues. In this study, the neutron vibrational spectrum was measured for a film of biaxially oriented polyethylene terephthalate (BoPET) using inelastic neutron scattering (INS), to investigate the relationship between the structure and dynamics of a widely used polymer. Compared to conventional spectroscopic techniques, the use of INS is advantageous for polymeric materials due to the absence of selection rules (i.e., all transitions are allowed), broad-band energy range, and considerable sensitivity to hydrogen modes. In order to distinguish the vibrational modes caused by trans and gauche rotational isomerism, the normal modes of vibration were calculated from a density functional theory-optimized structure of crystalline PET (cPET), representative of the all-trans state, and compared with INS from “highly crystalline” PET powder. Although in- and out-of-plane wagging of hydrogens on the ring structure exhibit significant contribution to both BoPET and cPET spectra, the wagging, rocking, and twisting modes of hydrogen on the ethylene glycol group are, in most cases, conformation-specific. These results were further rationalized by investigating the role of hyperconjugation in stabilizing both conformations using the natural bond order method. Here, through comparison of experimental and calculated INS results, this work provides the fundamental basis for discovering the role of structure and dynamics in shaping the macroscopic properties of PET and polymer analogues.
Interplay between Intermolecular and Intramolecular Singlet Fission in Thin Films of a Covalently Li
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Proton-Coupled Electron Transfer of Cerium Oxide Nanoparticle Thin-Film Electrodes
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Morphology, Deformations, and Photocatalytic Activity of Thermally Treated Brookite Titanium Dioxide Thin Films
Metastable states, in which the coupling between long-range lattice deformations and electronic properties can be controlled, provide a pathway to tailoring the behavior of photocatalytic materials by directing the flow of photoinduced charge carriers. Brookite is a metastable polymorph of earth-abundant TiO 2 that exhibits photocatalytic function and, due to its high energy relative to the anatase and rutile polymorphs, may serve as a precursor for the formation of transitional metastable structures. In this work, facile thermal annealing is employed to promote the formation of predominantly brookite-phase films, regulate the brookite lattice distortions, and determine the effect of these distortions on charge separation, ultimately directed at enhancing photocatalytic activity. Profile fitting of X-ray diffraction patterns and peak shifts in Raman spectra revealed structural distortions of the brookite lattice. Structural defects, including lattice gliding, dislocations, stacking faults, and twin boundaries, were observed using scanning transmission electron microscopy. First-principles simulations reveal how the lattice distortions associated with stacking faults induce band bending, thus increasing the photocatalytic activity of brookite. In conclusion, this study provides insight into the microstructural tuning of metastable phases to enhance their unique functionalities.
Redox Properties of TiO 2 Thin Films Grown on Mesoporous Silica by Atomic Layer Deposition
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Studying Transient Phenomena in Thin Films with Reinforcement Learning
Neutron reflectometry has long been a powerful tool to study the interfacial properties of energy materials. Recently, time-resolved neutron reflectometry has been used to better understand transient phenomena in electrochemical systems. Those measurements often comprise a large number of reflectivity curves acquired over a narrow q range, with each individual curve having lower information content compared to a typical steady-state measurement. In this work, we present an approach that leverages existing reinforcement learning tools to model time-resolved data to extract the time evolution of structure parameters. Further, by mapping the reflectivity curves taken at different times as individual states, we use the Soft Actor-Critic algorithm to optimize the time series of structure parameters that best represent the evolution of an electrochemical system. We show that this approach constitutes an elegant solution to the modeling of time-resolved neutron reflectometry data.