Coupled wave propagation in a rod with a dynamic absorber layer

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1991.Includes bibliographical references (leaves 56-59).by Jiulong Meng.M.S

Preparation of Cellulose Nanofibers from Bamboo Using Microwave Liquefaction

Cellulose nanofibers isolated from renewable lignocellulosic biomass are considered highly promising fillers in preparing sustainable composite materials. Although previous technologies on the production of cellulose nanofibers were encouraging, drawbacks such as chemical reagent, high energy consumption, time-consumption, and equipment degradation have limited these techniques for practical applications. In this work, bamboo particles were subjected to microwave liquefaction process and the liquefied bamboo residues were characterized to have a better understanding of the liquefaction behaviors of bamboo. Then, the microwave liquefaction process was optimized for the production of cellulose raw materials and the isolation of cellulose nanofibers. The lignin fraction fractionated from the microwave liquefaction process was also characterized for use in bio-based materials. The overall results revealed that high conversion yield of bamboo to liquid could be archived in mild microwave liquefaction reaction conditions. Lignin and hemicellulose in bamboo could easily undergo decomposition during liquefaction, while cellulose was the main resistance to the liquefaction process. The chemical and morphology analysis results revealed that the liquefied bamboo residues retained fiber structure and cellulose. Bleaching and acid hydrolysis were proved to be effective in purifying the residues for pure white cellulose fibers by removing carboxyl groups and lignin fragments. Long nanofibrils were generated by subjecting the pure cellulose fibers to high-intensity ultrasonic treatment. Good quality fibers with high holocellulose content were successfully produced by removing lignin and extractives from bamboo when the microwave liquefaction temperature was below 120oC. The relative lignin and extractives contents of the liquefied residues from the reaction at 120 oC, 9min were as low as 0.65 and 0.49 %, respectively. Cellulose nanofibers with diameters in the range of 2-30 nm were successfully extracted from the cellulose materials with a subsequent chemical treatment as a purification process and ultrasonication as a nanofibrillation process. The main functionality of the microwave liquefaction process on the nanofiber preparation process was efficiently converting bamboo bundles into micro-sized fibers by almost completely removing lignins and extractives. The isolated cellulose nanofibers have potential application for the fabrication of thermally stable composites because of their high thermal stability. Lignins recovered from the microwave liquefaction system showed high purity and retained their natural structures. The lignin samples were completely soluble in ethanol/water, DMSO, THF, 1, 4-dioxane, and 1mol/L NaOH solution. Polylactic acid (PLA)-lignin composites were successfully fabricated, and the lignin component in the PLA-lignin blends significantly improved the UV light barrier properties of the composites. The utilization of the lignin fraction should enhance the economic value of the microwave liquefaction system on the integrated utilization of bamboo

Development of a novel homogeneous immunoassay using mutant beta-glucuronidase

Βeta-glucuronidase (GUS) catalyzes breakdown of complex carbohydrates, whose activity can be detected quantitatively and sensitively by using fluorogenic and chromogenic substrates. GUS is a tetramer composed of four identical subunits, and assembly of all these subunits is necessary to attain its activity. Based on a previous study, a set of interface mutations (M516K, Y517E) is known to effectively inhibit the assembly and makes it inactive [1]. Usually, the affinity between the two variable region domains (VH and VL) of an antibody recognizing a small molecule is relatively low. However, in the presence of antigen, this affinity becomes higher so that they bind each other more tightly [2]. This gives the idea that a fusion protein system comprising VH and VL of an antibody as the detector each tethered to a mutant GUS subunit (GUSm) as the reporter can be used as a biosensor for small molecules. In this study, we aimed at detecting 4-hydroxy-3-nitrophenylacetyl (NP) and bone Gla protein (BGP) as targets of this novel immunosensor (Fig. 1). Please click Additional Files below to see the full abstract

DEVELOPMENT OF INORGANIC-ORGANIC HYBRID MATERIALS FOR WASTE WATER TREATMENT

Ph.DDOCTOR OF PHILOSOPHY (FOS

Preparation of [C60]Fullerene Nanowhisker-gold Nanoparticle Composites and Reduction of 4-Nitrophenol through Catalysis

A gold nanoparticle solution was prepared by adding sodium borohydride (NaBH4), trisodium citrate dihydrate (C6H5Na3O7⋅2H2O), cetyltrimethyl ammonium bromide (CTAB,(C16H33)N(CH3)3Br), ascorbic acid (C6H8O6), and potassium tetrachloroaurate(III)(KAuCl4) to distilled water and stirring the solution for 15 min. [C60]fullerene nanowhisker-gold nanoparticle composites were synthesized using C60-saturated toluene, the gold nanoparticle solution, and isopropyl alcohol by liquid-liquid interfacial precipitation (LLIP). The product of the nanocomposites was characterized by X-ray diffraction, scanning electron microscopy, Raman spectroscopy, transmission electron microscopy, and solid-state 13C-nuclear magnetic resonance spectroscopy. The catalytic activity of the [C60]fullerene nanowhisker-gold nanoparticle composites was confirmed in 4-nitrophenol reduction by UV-vis spectroscopy

Neural Aesthetic Image Reviewer

Recently, there is a rising interest in perceiving image aesthetics. The existing works deal with image aesthetics as a classification or regression problem. To extend the cognition from rating to reasoning, a deeper understanding of aesthetics should be based on revealing why a high- or low-aesthetic score should be assigned to an image. From such a point of view, we propose a model referred to as Neural Aesthetic Image Reviewer, which can not only give an aesthetic score for an image, but also generate a textual description explaining why the image leads to a plausible rating score. Specifically, we propose two multi-task architectures based on shared aesthetically semantic layers and task-specific embedding layers at a high level for performance improvement on different tasks. To facilitate researches on this problem, we collect the AVA-Reviews dataset, which contains 52,118 images and 312,708 comments in total. Through multi-task learning, the proposed models can rate aesthetic images as well as produce comments in an end-to-end manner. It is confirmed that the proposed models outperform the baselines according to the performance evaluation on the AVA-Reviews dataset. Moreover, we demonstrate experimentally that our model can generate textual reviews related to aesthetics, which are consistent with human perception.Comment: 8 pages, 13 figure

Multi-Disciplinary Design Optimization under Uncertainty for Thermal Protection System Applications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76270/1/AIAA-2006-7002-906.pd

Intelligent modeling with physics-informed machine learning for petroleum engineering problems

The advancement in big data and artificial intelligence has enabled a novel exploration mode for the study of petroleum engineering. Unlike theory-based solution methods, the data-driven intelligent approaches demonstrate superior flexibility, computational efficiency and accuracy for dealing with complex multi-scale, and multi-physics problems. However, these intelligent models often disregard physical laws in pursuit of error minimization, which leads to certain uncertainties. Therefore, physics-informed machine learning approaches have been developed based on data, guided by physics, and supported by machine learning models. This study summarizes four embedding mechanisms for introducing physical information into machine learning models, including input databased embedding, model architecture-based embedding, loss function-based embedding, and model optimization-based embedding mechanism. These “data + physics” dualdriven intelligent models not only exhibit higher prediction accuracy while adhering to physic laws, but also accelerate the convergence to improve computational efficiency. This paradigm will facilitate the guide developments in solving petroleum engineering problems toward a more comprehensive and efficient direction.Cited as: Xie, C., Du, S., Wang, J., Lao, J., Song, H. Intelligent modeling with physics-informed machine learning for petroleum engineering problems. Advances in Geo-Energy Research, 2023, 8(2): 71-75. https://doi.org/10.46690/ager.2023.05.0