Microwave-aided synthesis and applications of gold and nickel nanoporous metal forms

"DEC 2013.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri--Columbia In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Dr. Sheila N. Baker.In the field of nanoscience, nanoporous metal foams are a representative type of nanostructured materials, representing the ultimate form factor of a metal. They possess the hybrid properties of metal and nanoarchitectures, including the following properties such as good electrical and thermal conductivity, catalytic activity and high surface area, ultralow density, high strength-to-weight ratio. The outstanding properties bring the nanoporous metal foams to a wide range of applications, especially in the field of sensor system, energy storage and chemical catalyst. A new method of synthesis developed recently is presented for nanoporous metal foams of gold and nickel. The goal of this study is for the synthesis process of NMFs of and some applications in research and realistic life. Gold NMFs were produced by mixing gold chloride with ethylene glycol, ethanol, and reducing agent, and heating at 150 °C for 5 min with a CEM microwave. Both hydrazine and sodium borohydride were applied as the reducing agent for this redox reaction. Nickel NMFs were produced through the similar procedure with a little difference in the heating condition of 50 W, instead of 150 °C, with either hydrazine or sodium borohydride as the reducing agent. Gold NMFs were applied in surface-enhanced Raman spectroscopy (SERS) as a substrate. It is presented that with the presence of gold NMFs, the detection of the rhodamine 6G (R6G), a model analyte, can be enhanced significantly. The limit of detection for rhodamine 6G was found to be 5.2 ₉ 10-7 M in this research. Nickel NMFs was applied to degrade methyl orange (MO). An aqueous MO solution will turn nearly colorless after only 10 h of mixing with 0.025 g of nickel NMFs at room temperature under dark condition. In order to study the kinetics of the degradation reaction, MO solution with different initial concentration were used. This application of Ni NMFs is applicable as waste treatment of industrial water and to protect the environment.Includes bibliographical references (pages 51-54)

Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation

Brain tumor segmentation presents a formidable challenge in the field of Medical Image Segmentation. While deep-learning models have been useful, human expert segmentation remains the most accurate method. The recently released Segment Anything Model (SAM) has opened up the opportunity to apply foundation models to this difficult task. However, SAM was primarily trained on diverse natural images. This makes applying SAM to biomedical segmentation, such as brain tumors with less defined boundaries, challenging. In this paper, we enhanced SAM's mask decoder using transfer learning with the Decathlon brain tumor dataset. We developed three methods to encapsulate the four-dimensional data into three dimensions for SAM. An on-the-fly data augmentation approach has been used with a combination of rotations and elastic deformations to increase the size of the training dataset. Two key metrics: the Dice Similarity Coefficient (DSC) and the Hausdorff Distance 95th Percentile (HD95), have been applied to assess the performance of our segmentation models. These metrics provided valuable insights into the quality of the segmentation results. In our evaluation, we compared this improved model to two benchmarks: the pretrained SAM and the widely used model, nnUNetv2. We find that the improved SAM shows considerable improvement over the pretrained SAM, while nnUNetv2 outperformed the improved SAM in terms of overall segmentation accuracy. Nevertheless, the improved SAM demonstrated slightly more consistent results than nnUNetv2, especially on challenging cases that can lead to larger Hausdorff distances. In the future, more advanced techniques can be applied in order to further improve the performance of SAM on brain tumor segmentation

Semantics-Empowered Communication: A Tutorial-cum-Survey

Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the "what" and "why" questions in semantic transmissions. Afterwards, we present the ecosystems of SemCom, including history, theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content & channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., conventional communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities.Comment: Submitted to an IEEE journal. Copyright might be transferred without further notic

Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+

Recent developments in deep learning have made remarkable progress in speeding up the prediction of quantum chemical (QC) properties by removing the need for expensive electronic structure calculations like density functional theory. However, previous methods learned from 1D SMILES sequences or 2D molecular graphs failed to achieve high accuracy as QC properties primarily depend on the 3D equilibrium conformations optimized by electronic structure methods, far different from the sequence-type and graph-type data. In this paper, we propose a novel approach called Uni-Mol+ to tackle this challenge. Uni-Mol+ first generates a raw 3D molecule conformation from inexpensive methods such as RDKit. Then, the raw conformation is iteratively updated to its target DFT equilibrium conformation using neural networks, and the learned conformation will be used to predict the QC properties. To effectively learn this update process towards the equilibrium conformation, we introduce a two-track Transformer model backbone and train it with the QC property prediction task. We also design a novel approach to guide the model's training process. Our extensive benchmarking results demonstrate that the proposed Uni-Mol+ significantly improves the accuracy of QC property prediction in various datasets. We have made the code and model publicly available at \url{https://github.com/dptech-corp/Uni-Mol}

Synthesis of tributyl citrate using SO42-/Zr-MCM-41 as catalyst

Zirconium-containing mesoporous molecular sieve SO42-/Zr-MCM-41 was synthesized for catalyst in synthesis of tributyl citrate. The structure was characterized by XRD, N2 Ad/De isotherms and FT-IR. The results indicated that the solid acids show good catalytic performance and are reusable. Under optimum conditions and using SO42-/Zr-MCM-41 as catalyst, the conversion of citric acid was 95%. After easy separation of the products from the solid acid catalyst, it could be reused three times and gave a conversion of citric acid not less than 92%. The structure of tributyl citrate was characterized by FT-IR and 1H-NMR.KEY WORDS: Mesoporous molecular sieve, Tributyl citrate, Synthesis Bull. Chem. Soc. Ethiop. 2011, 25(1), 147-150

Exploration of the impact of demographic changes on life insurance consumption: empirical analysis based on Shanghai Cooperation Organization

Based on the panel data of eight member states of Shanghai Cooperation Organization (SCO) from 1996 to 2019, this study explores the impact of demographic changes on life insurance consumption in SCO member countries under the framework of static panel model and dynamic panel model. And the study analyzes the heterogeneity of religious division and different aging degrees. The empirical results show that both old-age dependency ratio and teenager dependency ratio have positive impacts on life insurance consumption in the SCO countries. Besides, the current consumption of ordinary life insurance significantly stimulates the future consumption of ordinary life insurance. Furthermore, demographic changes have heterogeneous impacts on life insurance consumption in terms of different religions and different degrees of aging. Our findings provide managerial implications for insurance companies that carry out life insurance business in SCO member states. Insurance companies should consider the policyholders’ life insurance consumption in accordance with demographic changes of both old-age dependency ratio and teenager dependency ratio, and also take differentiated life insurance sales strategies according to different degrees of aging and whether the residents believe in Islam