Temperature Dependence of Laser Induced Gratings EU-Doped Glasses

Department of Physic

Experiment Design and Training Data Quality of Inverse Model for Short-term Building Energy Forecasting

For data-driven building energy forecasting modeling, the quality of training data strongly affects a model’s accuracy and cost-effectiveness. In order to obtain high-quality training data within a short time period, experiment design, active learning, or excitation is becoming increasingly important, especially for nonlinear systems such as building energy systems. Experiment design and system excitation have been widely studied and applied in fields such as robotics and automobile industry for their model development. But these methods have hardly been applied for building energy modeling. This paper presents an overall discussion on the topic of applying system excitation for developing building energy forecasting models. For gray-box and white-box models, a model’s physical representations and theories can be applied to guide their training data collections. However, for black-box (pure-data-driven) models, the training data’s quality is sensitive to the model structure, leading to a fact that there is no universal theory for data training.  The focus of black-box modeling has traditionally been on how to represent a data set well. The impact of how such a data set represents the real system and how the quality of a training data set affect the performances of black-box models have not been well studied. In this paper, the system excitation method, which is used in system identification area, is used to excite zone temperature set-points to generate training data. These training data from system excitation are then used to train a variety of black-box building energy forecasting models. The models’ performances (accuracy and extendibility) are compared among different model structures. For the same model structure, its performances are also compared between when it is trained using typical building operational data and when it is trained using exited training data. Results show that the black-box models trained by normal operation data achieve better performance than that trained by excited training data but have worse model extendibility; Training data obtained from excitation will help to improve performances of system identification models

Rapid thermally processed hierarchical titania-based hollow fibres with tunable physicochemical and photocatalytic properties

A series of photocatalytic TiO2–carbon composite hollow fibres (HFs) was prepared in this study by a wet-dry phase inversion spinning method followed by a rapid thermal processing (RTP). The RTP method consists of two stages: (1) calcination at 800 °C for 15 min encased in a quartz tube followed by (2) a short open heating exposure at 800 °C for 0 to 7.5 min in air. The innovative two-stage RTP method led to a time saving of more than 90%. Results revealed that the pyrolysis conditions during the second stage of HF fabrication were essential to the final physical and chemical properties of resultant TiO2-carbon HFs, such as TiO2 crystallinity and carbon content, mechanical, textural and electronic properties, as well as photocatalytic reactivity. The best results show that HFs pyrolysed for a short duration (< 2 min) in the second stage produced a high microporous surface area of 217.8 m2·g−1, a good mechanical strength of 11 MPa and a TiO2 anatase-to-rutile (A/R) ratio of 1.534 on the HF surface. The HFs also achieved a 68% degradation of acid orange 7 dye with a kapp of 0.0147 min−1 based on a Langmuir-Hinshelwood model during the photocatalysis under UV light. Thus, this work provides a new synthesis protocol with significant time and cost savings to produce high-quality HFs for wastewater treatment

Rational design and synthesis of molecular-sieving, photocatalytic, hollow fiber membranes for advanced water treatment applications

Photocatalytic, hollow fiber membranes based on nanocomposites of titania nanoparticles and carbonaceous char were simultaneously fabricated in a single calcination step, and then optimized for the photo-degradation of pollutants and water recovery in an integrated membrane operation in this study. The physicochemical, mechanical and photocatalytic properties along with separation performance of two series of membranes were finely-tuned by systematically changing the calcination temperature (series 1: 500–1000 °C for 8 h holding time) and calcination time (series 2: 2–8 h at 600 °C). The calcined membranes were extensively characterized for morphology, thermal stability, microstructure, modulus and chemical compositions. Both constituents of titania and char are essential in deriving the desirable hollow fiber properties and membrane performance for photocatalysis and water recovery. By controlling the calcination conditions, membranes prepared at 600 °C for the 3 and 6 h duration displayed an optimal balance between enhanced mechanical strength (34 MPa) and high photo-degradation of acid orange 7 (90.4%). Membrane performance demonstrated water fluxes of 6.9 (H2O/dark), 12.9 (H2O/UV) 4.8 (AO7/dark) and 7.9 L m–2 h–1 (AO7/UV) with excellent organic dye rejection. Both membranes exhibited photo-induced super-hydrophilicity and defouling potential under the influence of UV light due to the photo-activation of exposed TiO2 nanoparticles on the membrane surface. The detailed mechanism of property correlation and separation performance for the photocatalytic hollow fibers is proposed and elucidated. This work offers an innovative material for the research avenue of photocatalytic, hollow fiber membrane reactors for advanced membrane treatment applications

Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics

Feynman's path integral approach is to sum over all possible spatio-temporal paths to reproduce the quantum wave function and the corresponding time evolution, which has enormous potential to reveal quantum processes in classical view. However, the complete characterization of quantum wave function with infinite paths is a formidable challenge, which greatly limits the application potential, especially in the strong-field physics and attosecond science. Instead of brute-force tracking every path one by one, here we propose deep-learning-performed strong-field Feynman's formulation with pre-classification scheme which can predict directly the final results only with data of initial conditions, so as to attack unsurmountable tasks by existing strong-field methods and explore new physics. Our results build up a bridge between deep learning and strong-field physics through the Feynman's path integral, which would boost applications of deep learning to study the ultrafast time-dependent dynamics in strong-field physics and attosecond science, and shed a new light on the quantum-classical correspondence

Tempol Protects Against Acetaminophen Induced Acute Hepatotoxicity by Inhibiting Oxidative Stress and Apoptosis

Acetaminophen (APAP)-induced acute hepatotoxicity is the leading cause of drug-induced acute liver failure. The aim of this study was to evaluate the effects of 4-hydroxy-2,2,6,6-tetramethylpiperidine-N-oxyl (tempol) on the protection of APAP-induced hepatotoxicity in mice. Mice were pretreated with a single dose of tempol (20 mg/kg per day) orally for 7 days. On the seventh day, mice were injected with a single dose of APAP (300 mg/kg) to induce acute hepatotoxicity. Our results showed that tempol treatment markedly improved liver functions with alleviations of histopathological damage induced by APAP. Tempol treatment upregulated levels of antioxidant proteins, including superoxide dismutase, catalase, and glutathione. Also, phosphorylation of phosphoinositide 3-kinase (PI3K) and protein kinase B (Akt) and protein expression of nuclear factor erythroid 2-related factor (Nrf 2) and heme oxygense-1 (HO-1) were all increased by tempol, which indicated tempol protected against APAP-induced hepatotoxicity via the PI3K/Akt/Nrf2 pathway. Moreover, tempol treatment decreased pro-apoptotic protein expressions (cleaved caspase-3 and Bax) and increased anti-apoptotic Bcl-2 in liver, as well as reducing apoptotic cells of TUNEL staining, which suggested apoptotic effects of tempol treatment. Overall, we found that tempol normalizes liver function in APAP-induced acute hepatotoxicity mice via activating PI3K/Akt/Nrf2 pathway, thus enhancing antioxidant response and inhibiting hepatic apoptosis