Manipulating refractive index, homogeneity and spectroscopy of Yb3+^{3+}-doped silica-core glass towards high-power large mode area photonic crystal fiber lasers

Output power scaling of single mode large mode area (LMA) photonic crystal fiber (PCF) amplifiers urgently requires the low refractive index of Yb³⁺-doped silica glasses whilst maintaining high optical homogeneity. In this paper, we report on a promising alternative Yb³⁺/Al³⁺/F¯/P⁵⁺-co-doped silica core-glass (YAFP), which is prepared by modified sol-gel method developed by our group and highly suitable for fabricating high power LMA PCF amplifiers. By controlling the doping combinations of Al³⁺/F¯/P⁵⁺ in Yb³⁺- doped silica glass,it not only ensures low refractive index (RI) but also maintains the excellent optical homogeneity and spectroscopic properties of Yb³⁺. The spectroscopic properties of Yb³⁺ ions have not deteriorated by the co-doping of F¯ and P⁵⁺ in YAFP glass compared with that of Yb³⁺/Al³⁺ co-doped silica glass. A large-size (⌀5 mm × 90 mm) YAFP silica-core glass rod with low average RI difference of 2.6 × 10¯⁴ (with respect to pure silica glass), and low radial and axial RI fluctuations of ~2 × 10¯⁴, was prepared. A LMA PCF with 50 μm core diameter was obtained by stack-capillary-draw techniques using YAFP core glass. Its core NA is 0.027. An average amplified power of 97 W peaking at 1030 nm and light-light efficiency of 54% are achieved from a 6.5 m long PCF in the pulse amplification laser experiment. Meanwhile, quasi-single-mode transmission is obtained with laser beam quality factor M² of 1.4

Research on Predisposition and Perceived Function of the Third-person Effect-A Case Study of the Zero Sugar Drinks Advertisement

The sugar-free beverage advertisement emerged in 1980s. Recently, with the general health trend disseminating away, sugar-free beverage consumption has become a new fashion among the youth, of which the advertisement spread on social media has increased its effects in the Gen Z. While it has been extensive and unique on the aspect of advertising, this papaer mainly revealed that the methods of text analysis and literature analysis have been applied here, for what has been explored to depict how the third-person effect did work on explaining the advertising communication of sugar-free beverages. This paper finds that the path of advertising attaches importance to attracting the audience with the third-person effect, which directly affects other types of beverage advertisements. This paper depicts that the main value is to provide reference or other kinds of contribution to the research on beverage advertising

Study of multi-component spray and combustion and their optimization with machine learning in internal combustion engines

As energy resources dry up and environmental pollution becomes severe, clean and highly efficient internal combustion engine operation has become the need of the hour. Understanding the physical process of the fuel spray evolution is imperative in predicting and improving the performance of applications of modern fuel injection systems. Once fuel is directly injected into the combustion chamber, the proceedings, such as liquid fuel vaporization and formation of the fuel-air mixture, would conspicuously influence the onset of combustion, combustion efficiency and thus the emissions. Studies in this dissertation focus on two phenomena in internal combustion engines: the liquid fuel spray evolution and fuel-air mixture preparation. The computational methodologies which specialized in physics-based and data-driven model development and simulations are utilized in order to predict the performances of internal combustion engines and to seek better solutions for clean energy, higher efficiency, and lower emission design. In this dissertation, the objectives included are: 1) a comprehensive model considering preferential vaporization of a complex fuel mixture using discrete approach; 2) a unified model for spray atomization under the consideration of flash boiling; 3) a new approach of emission reduction in the compression-ignition engine is introduced. By controlling the fuel-air mixing process the burning sequence of the mixture is shifted from traditional ignition behavior; 4) applications of artificial intelligence techniques to help accelerating the model development and engine design processes. Fuel droplet vaporization rates and corresponding flow evolutions are important because they directly influence the onset of combustion, the efficiency of combustion, thus emissions. Therefore, prediction of the real fuel vaporization is the first step of this research project. An efficient and precise multi-component fuel droplet vaporization model has been developed in this work using a discrete approach. A quasi-1D frame for liquid droplet is constructed such that the evolution of the non-uniform mass and energy distribution for liquid droplets can be accurately described by the surface state and bulk mean state. The second objective is to develop a comprehensive model for spray atomization under the consideration of flash boiling. To develop a comprehensive multi-component fuel flash boiling model, three main processes, bubble nucleation, bubble growth, and breakup, are considered. The effect of superheating on breakup is built into a classic aerodynamic breakup model such that the unified breakup model is feasible to be used over all possible engine operation conditions. The new vaporization and breakup model are integrated into a CFD program (KIVA-3V) to further conduct the evaluation of the spray superheating effects. A new approach of NOx reduction in compression-ignition engines is introduced using a fuel that exhibits the “inverted -sensitivity” (IPS) behavior in this work. From 0-D calculations, ethanol, which is also a clean and renewable fuel, is selected as the test fuel for satisfying the criteria considered in this study. Engine conditions that cover from low to high load operations are tested with 3-D simulations. Characteristics of ethanol vapor/air mixture are analyzed to reveal the mechanism of inverted -sensitivity fuel in reducing emissions. Optimal operations for compression-ignition engines with IPS fuel are conducted using the genetic algorithm to exaggerate the superior effect of inverted -sensitivity on engine performance. Advanced modeling methods utilizing machine learning are developed to substitute the high-fidelity CFD simulation and coupled with optimization algorithms to accelerate the design processes and increase predictive capability of models. The underlying patterns and relations between engine control parameters and performances (emissions and efficiency) are revealed by data-driven models. Such compact information processes are assembled to construct a stacked machine learning model. Instead of using CFD models, this machine learning model is called as a function at the genetic algorithm interface for optimization procedures. This work provides the fundamental understandings of liquid fuel spray and corresponding mixture evolutions and combustion processes in internal combustion engines. New technologies for better atomization process and engine emission reduction are provided. Applications of artificial intelligence demonstrate the possibilities of implementing the advanced computational knowledge and addressing the cross-disciplinary work to engine studies.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Determination of Picogram Levels of Diacerein in a Pharmaceutical Formulation by Flow-Injection Chemiluminescence

A simple, sensitive and inexpensive method for determination of diacerein by flow-injection chemiluminescence was proposed, based on the quenching effect of diacerein on the luminol-protein (bovine serum albumin, BSA) reaction. It was found that the decrement of CL intensity was linearly proportional to the logarithm of diacerein concentration ranging from 5.0 to 7.0×103 pg·mL-1 (r = 0.9968), with the limit of detection (LOD) of 1.0 pg·mL-1 (3σ). The proposed procedure was successfully applied to the determination of diacerein in pharmaceutical formulation, human saliva, and serum samples without interference from its potential impurities, with the recoveries ranging from 96.4% to 104.0% and the relative standard deviations (RSDs) less than 4.0% (n=6)

Determination of Picogram Levels of Diacerein in a Pharmaceutical Formulation by Flow-Injection Chemiluminescence

A simple, sensitive and inexpensive method for determination of diacerein by flow-injection chemiluminescence was proposed, based on the quenching effect of diacerein on the luminol-protein (bovine serum albumin, BSA) reaction. It was found that the decrement of CL intensity was linearly proportional to the logarithm of diacerein concentration ranging from 5.0 to 7.0×103 pg·mL-1 (r = 0.9968), with the limit of detection (LOD) of 1.0 pg·mL-1 (3σ). The proposed procedure was successfully applied to the determination of diacerein in pharmaceutical formulation, human saliva, and serum samples without interference from its potential impurities, with the recoveries ranging from 96.4% to 104.0% and the relative standard deviations (RSDs) less than 4.0% (n=6)

Additional file 2 of Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Additional file 2: Supplementary table 1. Univariate and Multivariate analysis of prognostic factors affecting OS

Additional file 8 of Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Additional file 8

Additional file 3 of Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Additional file 3: Supplementary table 2. Univariate and Multivariate analysis of prognostic factors affecting CS

Additional file 1 of Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Additional file 1. Material and Methods