The emission lifetimes of rhodamine 6G (R6G) were measured under shock compression to 9.1 GPa, with the dual intents of better understanding molecular photophysics in extreme environments and assessing the usefulness of fluorescence lifetime microscopy to measure spatially dependent pressure distributions in shocked microstructured media. R6G was studied as free dye dissolved in poly(methyl methacrylate) (PMMA), or dye encapsulated in silica microparticles suspended in PMMA. Thin layers of these materials in impedance-matched geometries were subjected to planar single-stage shocks created by laser-driven flyer plates. A synchronized femtosecond laser excited the dye at selected times relative to flyer plate arrival and the emission lifetimes were measured with a streak camera. Lifetimes decreased when shocks arrived. The lifetime decrease was attributed to a shock-induced enhancement of R6G nonradiative relaxation. At least part of the relaxation involved shock-enhanced intersystem crossing. For free dye in PMMA, the lifetime decrease during the shock was shown to be a linear function of shock pressure from 0 to 9 GPa, with a slope of -0.22 ns·GPa(-1). The linear relationship makes it simple to convert lifetimes into pressures. Lifetime measurements in shocked microenvironments may be better than emission intensity measurements, because lifetimes are sensitive to the surrounding environment, but insensitive to intensity variations associated with the motion and optical properties of a dynamically changing structure.
The lithium-ion battery is a complicated non-linear system with multi electrochemical processes including mass and charge conservations as well as electrochemical kinetics. The calculation process of the electrochemical model depends on an in-depth understanding of the physicochemical characteristics and parameters, which can be costly and time-consuming. We investigated the electrochemical modeling, reduction, and identification methods of the lithium-ion battery from the electrode-level to the system-level. A reduced 9th order linear model was proposed using electrode-level physicochemical modeling and the cell-level mathematical reduction method. The data-driven predictor-based subspace identification algorithm was presented for the estimation of lithium-ion battery model in the system-level. The effectiveness of the proposed modeling and identification methods was validated in an experimental study based on LiFePO4 cells. The accuracy and dynamic characteristics of the identified model were found to be much more likely related to the operating State of Charge (SOC) range. Experimental results showed that the proposed methods perform well with high precision and good robustness in the SOC range of 90% to 10%, and the tracking error increases significantly within higher (100–90%) or lower (10–0%) SOC ranges. Moreover, to achieve an optimal balance between high-precision and low complexity, statistical analysis revealed that the 6th, 3rd, and 5th order battery model is the optimal choice in the SOC range of 90% to 100%, 90% to 10%, and 10% to 0%, respectively.
Alumina nanostrips were prepared on aluminum plate surface by anodizing in oxalic acid and etching in phosphonic acid sequentially. The alumina nanostrips were characterzed by scanning and transmission electron microscopes for the morphologies structuzes,and crystal structures, by an energy dispersive x-ray spectroscope for the chemical composition, and by a photoluminescence measurement system for the photoluminescence. The results show that the alumina nanostrips are amorphous, have a chemical composition of Al2O3-x , and can emit a blue light about 440nm in photoluminescence.
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