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Surface functionalized barium titanate (BaTiO 3 ) nanocrystals have been explored for highly tunable chemical and electronic properties, potentially of use in ceramic-polymer composites for flexible ferroelectric device applications, directed synthesis of ferroelectric thin films or other nano-architectures, and other potential applications. The detailed temperature dependent local structure evolution of BaTiO 3 nanocubes capped with nonpolar oleic acid (OA) and polar tetrafluoroborate (BF 4 − ) ligands are investigated using in situ synchrotron X-ray diffraction and pair distribution function (PDF) analysis, in conjunction with piezoresponse force microscopy (PFM) and 137 Ba nuclear magnetic resonance (NMR) spectroscopy measurements. Diffraction analysis reveals that nanocubes capped by polar BF 4 − ligands undergo sharper ferroelectric to paraelectric phase transitions than nanocubes capped with nonpolar OA ligands, with the smallest ∼12 nm nanocubes displaying no transition. Local non-centrosymmetric symmetry is observed by PDF analysis and confirmed by NMR, persisting across the phase transition temperature. Local distortion analysis, manifested in tetragonality ( c / a ) and Ti off-centering ( z Ti ) parameters, reveals distinct temperature and length-scale dependencies with particle size and capping group. Ferroelectric order is increased by polar BF 4 − ligands, which is corroborated by an enhancement of PFM response.

Video streaming commonly uses Dynamic Adaptive Streaming over HTTP (DASH) to deliver good Quality of Experience (QoE) to users. Videos used in DASH are predominantly encoded by single-layered video coding such as H.264/AVC. In comparison, multi-layered video coding such as H.264/SVC provides more flexibility for up- grading the quality of buffered video segments and has the potential to further improve QoE. However, there are two challenges for us- ing SVC in DASH: (i) the complexity in designing ABR algorithms; and (ii) the negative impact of SVC’s coding overhead. In this work, we propose a deep reinforcement learning method called Grad for designing ABR algorithms that take advantage of the quality up- grade mechanism of SVC. Additionally, we quantify the impact of coding overhead on the achievable QoE of SVC in DASH, and propose jump-enabled hybrid coding (HYBJ) to mitigate the impact. Through emulation, we demonstrate that Grad-HYBJ, an ABR algo- rithm for HYBJ learned by Grad, outperforms the best performing state-of-the-art ABR algorithm by 17% in QoE.