CARBON NANOMATERIALS AND THEIR ELECTROCHEMICAL APPLICATIONS

Recent years have witnessed the continuously growing interest in the area of nanotechnology. Among the innumerable novel compounds and materials, carbon nanomaterials, especially carbon nanotubes (CNTs) and graphene, are undeniably two of the most glorious shining stars due to their unique structures and promising physical, chemical, and electrical properties. Numerous research projects have been focused on the synthesis, characterization, and functionalization of carbon nanomaterials, as well as their enormous possible applications in energy generation/storage, sensors, electronics, reinforcement of composite materials, and drug delivery. Of particular interest in this dissertation are the functionalization of carbon nanomaterials − either by decorating with Pt nanoparticles (NPs) or by doping with nitrogen atoms − and their electrochemical applications for both fuel cell catalysts (and supports) and electrochemical sensors/biosensors. I have successfully synthesized and characterized hybrid structures of Pt NP-CNTs or Pt NP-graphene, and also a novel carbon nanomaterial − nitrogen doped carbon nanotube cups (NCNCs). The electrochemical properties and applications of these nanomaterials were also investigated. Pt NP decorated CNTs or graphene were studied and compared for their electrochemical sensor performance in order to obtain further understanding on the structure-property relationship between 1-dimensional and 2-dimensional nanomaterials as the sensing platform. Both Pt-CNTs and NCNCs were investigated as fuel cell catalysts with the aim of improving the performance and stability, decreasing the amount of expensive Pt, and most importantly, understanding and optimizing NCNCs as non-precious-metal catalysts to ultimately replace expensive Pt-based catalysts. Pt-CNTs demonstrated extraordinary stability with less material used compared to commercial Pt/C catalysts in long term stability testing. NCNCs also exhibited good catalytic activity towards oxygen reduction reaction (ORR) which makes them promising alternatives to Pt-based catalysts. Further look into the ORR mechanism suggested that the presence of both nitrogen and iron from catalyst of NCNCs synthesis process is crucial for the improved ORR catalytic activity. From the materials point of view, a novel simple sonication method was studied to separate stacked NCNCs into individual nanocups structures, with the long-term objective of drug delivery or nano-reactor applications. Both the separation mechanism and the structure-property relationship of the stacked and separated NCNCs were investigated

Risk perception in skincare cosmetics and risk-reduction strategies : an exploratory study of young chinese women

Mestrado em MarketingCom a saturação dos mercados de cosmética nos países desenvolvidos, as empresas internacionais procuram cada vez mais novas oportunidades e crescimento nos mercados emergentes. Como o país mais populoso do mundo, a China é um alvo-chave. Com o aumento do rendimento disponível e a consciencialização do consumidor para a importância dos cuidados da pele, o mercado chinês de produtos de cuidado da pele é extremamente atraente, tanto para os players nacionais como internacionais. O risco percebido é um fator influente nas decisões de compra do consumidor, e os produtos para cuidado da não são exceção. Portanto, é fundamental que as empresas tenham um profundo entendimento das perceções de risco dos consumidores chineses em cosméticos para a pele, a fim de aumentar a presença neste e assumir a liderança. O objetivo deste estudo é entender as perceções de risco de jovens mulheres chinesas em cosméticos para a pele (desempenho, financeiro, físico, social e psicológico) e suas estratégias de redução de risco. Alguns aspetos que afetam a perceção de risco são também investigados. Além disso, este estudo aborda as perceções das mulheres chinesas sobre cosméticos orgânicos e testes de destes produtos em animais. A abordagem selecionada para esta pesquisa é a qualitativa e o método de coleta de dados é a entrevista semiestruturada presencial. A amostra é composta por 12 consumidoras chinesas de produtos para a pele.With the saturation of cosmetic markets in developed countries, international companies increasingly seek new opportunities and growth in emerging markets. As the most populated country in the world, China is a key target. With increasing disposable income and consumer awareness of the importance of skincare, the Chinese skincare cosmetic market is very attractive for both national and international players. Perceived risk is an influential factor in consumer purchase decisions, and skincare in no exception. Thus, it is critical for enterprises to have a deep understanding of Chinese consumers' risk perceptions in skincare cosmetics in order to increase market share and take the lead. The purpose of this study is to understand young Chinese women's perceptions of risk in skincare cosmetics (performance, financial, physical, social, and psychological) and their risk-reductions strategies. Some other issues affecting risk perception are also investigated. Furthermore, this study addresses Chinese female's perceptions of organic cosmetics and testing skincare products on animals. The approach selected for this research is qualitative and the method of data collection is the semi-structured face-to-face interview. The sample consists of 12 Chinese female consumers of skincare products.info:eu-repo/semantics/publishedVersio

SB-CoRLA: Schema-Based Constructivist Robot Learning Architecture

This dissertation explores schema-based robot learning. I developed SB-CoRLA (Schema- Based, Constructivist Robot Learning Architecture) to address the issue of constructivist robot learning in a schema-based robot system. The SB-CoRLA architecture extends the previously developed ASyMTRe (Automated Synthesis of Multi-team member Task solutions through software Reconfiguration) architecture to enable constructivist learning for multi-robot team tasks. The schema-based ASyMTRe architecture has successfully solved the problem of automatically synthesizing task solutions based on robot capabilities. However, it does not include a learning ability. Nothing is learned from past experience; therefore, each time a new task needs to be assigned to a new team of robots, the search process for a solution starts anew. Furthermore, it is not possible for the robot to develop a new behavior. The complete SB-CoRLA architecture includes off-line learning and online learning processes. For my dissertation, I implemented a schema chunking process within the framework of SB-CoRLA that involves off-line evolutionary learning of partial solutions (also called “chunks”), and online solution search using learned chunks. The chunks are higher level building blocks than the original schemas. They have similar interfaces to the original schemas, and can be used in an extended version of the ASyMTRe online solution searching process. SB-CoRLA can include other learning processes such as an online learning process that uses a combination of exploration and a goal-directed feedback evaluation process to develop new behaviors by modifying and extending existing schemas. The online learning process is planned for future work. The significance of this work is the development of an architecture that enables continuous, constructivist learning by incorporating learning capabilities in a schema-based robot system, thus allowing robot teams to re-use previous task solutions for both existing and new tasks, to build up more abstract schema chunks, as well as to develop new schemas. The schema chunking process can generate solutions in certain situations when the centralized ASyMTRe cannot find solutions in a timely manner. The chunks can be re-used for different applications, hence improving the search efficiency

Modeling redistribution of α-HCH in Chinese soil induced by environment factors

This study explores long-term environmental fate of alpha-HCH in China from 1952 to 2007 using ChnGPERM (Chinese Gridded Pesticide Emission and Residue Model). The model captures well the temporal and spatial variations of alpha-HCH concentration in Chinese soils by comparing with a number of measured data across China in different periods. The results demonstrate alpha-HCH grasshopping effect in Eastern China and reveal several important features of the chemical in Northeast and Southeast China. It is found that Northeast China is a prominent sink region of alpha-HCH emitted from Chinese sources and alpha-HCH contamination in Southwest China is largely attributed to foreign sources. Southeast China is shown to be a major source contributing to alpha-HCH contamination in Northeast China, incurred by several environmental factors including temperature, soil organic carbon content, wind field and precipitation. (C) 2011 Elsevier Ltd. All rights reserved.This study explores long-term environmental fate of alpha-HCH in China from 1952 to 2007 using ChnGPERM (Chinese Gridded Pesticide Emission and Residue Model). The model captures well the temporal and spatial variations of alpha-HCH concentration in Chinese soils by comparing with a number of measured data across China in different periods. The results demonstrate alpha-HCH grasshopping effect in Eastern China and reveal several important features of the chemical in Northeast and Southeast China. It is found that Northeast China is a prominent sink region of alpha-HCH emitted from Chinese sources and alpha-HCH contamination in Southwest China is largely attributed to foreign sources. Southeast China is shown to be a major source contributing to alpha-HCH contamination in Northeast China, incurred by several environmental factors including temperature, soil organic carbon content, wind field and precipitation. (C) 2011 Elsevier Ltd. All rights reserved

Multi-agent deep reinforcement learning for solving large-scale air traffic flow management problem: a time-step sequential decision approach

In this paper, we focus on the demand-capacity balancing (DCB) problem in air traffic flow management, which is considered as a fully cooperative multi-agent learning task. First, a rule-based time-step environment is designed to mimic the DCB process. In this environment, each agent ‘flight’ decides its action at valid time steps. Three different rules are defined, based on the remaining capacity and the number of cooperative flights in each sector, to ease the learning process. Second, a multi-agent reinforcement learning framework, built on the proximal policy optimization (MAPPO), is proposed by using the parameter sharing mechanism and the mean-field approximation method, where an inherent feature of all other agents is extracted to address the credit assignment problem. Moreover, a supervisor integrated MAPPO framework is proposed, where a supervisor is designed to generate supervised actions, in such a way to further improve the learning performance. In the experiments, two performance indices, Search Capability and Generalization Capability, are considered. Both indices are assessed with the evaluation of two toy cases and a real-world case study. Results suggest that, the supervisor integrated MAPPO with supervised actions achieves the best performance across the different cases; other proposed methods also show some promising Search Capability, but only prove an acceptable Generalization Capability in simpler cases than the training cases