중등 과학교사의 교재연구 및 교재활용의 실태 분석

현명재 연세대학교 교육대학원 2005 국내석사

The good quality teaching, which every teacher is seeking, is a kind of teaching that gives high satisfaction to the teacher as well as improving the achievement of the student by efficient management of all the teaching processes that covers establishment of teaching target, selection and organization of teaching contents, adequate method of teachinglearning, and evaluation. Particularly, in case of selecting and organizing teaching materials, the process of restructuring the teaching contents by using various teaching materials and information as well as teacher''s specialty could be a criterion to determine the quality of teaching.It is concluded that researching and analyzing the study level of teaching materials required for middle and high school science teachers, the types of study, science related training and the status of selection and utilization of teaching materials will help forecast and enhance the possibility of good quality teaching, i.e. the possibility of doing a good teaching.The detailed hypothesis, established to find out middle and high school teacher''s perception on science teaching materials and the status of utilizing such teaching materials, is as follows:Firstly, are the required level of the study of teaching material of middle and high school science teachers different according to sex, school grade, teaching career and education?Secondly, are the types of teaching material study of middle and high school science teachers different according to sex, school grade, teaching career, teaching hours and education?Thirdly, are the status of science related training of middle and high school science teachers different according to sex, school grade, teaching career and education?Fourthly, are the selection and utilization of teaching materials of middle and high school science teachers different according to sex, school grade, teaching career and education?In order to prove the above mentioned hypothesis, detailed survey was performed against 328 science teachers of middle schools and high schools located in Seoul Metropolitan City and Kyung Gi Province. For this purpose SPSS (version 1.5) program was used to analyze the result.As a result, the following conclusions can be deduced.Firstly, it is proved that the teachers think it is very important to perform study by referring to technical books or training, to exchange information and consultation with colleague teachers. Particularly, it is proved that middle school teachers recognize such information exchange and consultation with colleague teachers more important than high school teachers. At present, when organizing and operating education courses, curriculum consultative meetings, consultative meetings and class teacher meetings are usually used and sometimes encouraged. This is anticipated because science curriculum has usually a special room called science laboratory.Even though most teachers think teaching material study is important for a better quality teaching, the first reason why they do not study teaching materials is because they do not have enough time due to ''works other than teaching'' and the next reason is that ''they do not know the method of teaching material study''.This result suggests that although enhancing teacher''s specialty is important in improving the quality of teaching, it is more important and urgent to improve the working conditions and environments.Secondly, science teachers think that time to study teaching material is insufficient due to administration works, works to do as a class teacher and other sundry works. They usually allocate time to study teaching materials in the afternoon after work. It is proved that they feel burdened by teaching because of the special character of science which requires teaching preparation focused on laboratory works and a lot of experimentation and evaluation. It is also proved that in some cases even teaching instruments and appliances were not fully utilized due to deterioration and lack of sufficient science laboratories. It is judged that in order to consolidate science basic education, securing a sufficient science laboratory is one of the most urgent tasks which science teaching is now facing.Thirdly, it is proved that many teachers agree that science related training is important and necessary and they responded that they want training focused on new knowledge, textbook related contents and science for practical use. We realize from this suggestion that, instead of training which is focused only on theory, development of more practical and diversified training program, that can be used and applied in the actual teaching, is needed.Fourthly, most teachers responded that proper selection and utilization of teaching materials are needed in preparing the teaching and it is proved that they use reference rooms of education related institutions, science meetings in the school and teaching materials distributed from School Board of city or province. The degree of specialty of teachers themselves was the criterion for selecting teaching materials.It is proved that the many teachers seldom use teaching materials which were prepared and distributed by the School Boards of the city and province, and education related institutions. This is understood because such materials are usually focused on theory which cannot be applied in the actual teaching. There were also quite a few responses that they do not use such materials because they do not know how to use them or they did not use such materials often.This result implies that there is a need to establish and reorganize reference material system to conform with the needs of teacher''s desire for study on the teaching materials and to make a systematic data base for concerned materials. For example, it might be one of the alternatives to provide a training continuously by dispatching a guiding instructor to the school in order to make the most use of TeachingLearning Supporting Center in Seoul... 모든 교사들이 희망하는 질 높은 수업이란 수업목표의 설정, 수업내용의 선정 및 조직, 적절한 교수·학습방법, 평가에 이르는 전(全)과정을 효율적으로 운영함으로써 학생의 성취도 향상은 물론 교사의 수업만족도가 높은 수업이라 고 할 수 있다. 특히 수업내용의 선정·조직 시 본인의 교과전문성 뿐만 아니라 다양한 자료와 정보를 토대로 수업내용을 재구성하는 과정이야말로 좋은 수업의 성패를 가늠하는 척도가 될 수 있다.따라서 중등과학교사들의 교재연구의 요구수준, 교재연구의 실시형태, 각종 과학관련 연수, 교재선정 및 교재활용의 실시형태들을 조사·분석해보는 것은 질 높은 수업 즉 좋은 수업의 성공가능성을 예측하고 높이는데 도움이 될 수 있다고 판단된다. 과학교수·학습 자료에 대한 과학교사의 인식과 그들의 교수·학습자료 활용의 실태를 파악하기 위하여 설정한 구체적인 연구문제는 다음과 같다.첫째, 중등과학교사들의 교재연구와 관련된 요구는 성별, 학교급별, 교직경력, 학력에 따라 차이가 있는가?둘째, 중등과학교사들의 교재연구의 실시형태는 성별, 학교급별, 교직경력, 수업시수, 학력에 따라 차이가 있는가?셋째, 중등과학교사들의 과학관련 연수 이수실태는 성별, 학교급별, 교직경력, 학력에 따라 차이가 있는가?넷째, 중등과학교사들의 교재선정 및 교재활용의 실시형태는 성별, 학교급별, 교직경력, 학력에 따라 차이가 있는가?이상의 연구문제들을 규명하기 위하여 구체적인 연구조사를 서울특별시와 경기도에 소재하는 중·고등학교 과학교사 328명을 연구대상으로 실시하였고, 결과분석을 위한 분석방법으로 SPSS (버전 11.5) 프로그램을 이용 하였다.연구결과를 중심으로 다음과 같은 결론을 내릴 수 있다.첫째, 교재연구와 관련된 교사들의 요구수준은 전문서적이나 연수를 통한 연구노력과, 동료교사와의 정보교환 및 협의 활동이 매우 중요하다고 생각하고 있는 것으로 나타났는데, 특히 중학교 교사들이 고등학교 교사들에 비해 동료교사의 정보교환 및 협의활동을 매우 중요하게 인식하고 있는 것으로 드러났다. 현재 일선학교에서는 교육과정 편성 및 운영 시, 교과협의회, 협의회, 담임협의회 등 여러 대화채널을 활용하고 독려하는 분위기일 뿐만 아니라, 과학교과는 보통 과학부실이라는 특별실에 상주하는 경우가 있기 때문에 이러한 결과는 예상된 것이라 할 수 있다.대부분 교사들은 질 높은 수업을 위해 교재연구가 중요하다고 생각하고 있었으나, ‘수업외의 업무로 인한 시간부족’이 교재연구를 하지 못하는 첫 번째 이유이고, 그 다음으로 ‘교재연구 방법을 알지 못해서’ 라고 응답하였다.이러한 결과는 수업의 질을 높이기 위해서는 교사의 전문성 제고도 중요하지만 교사가 수업에 전념할 수 있도록 근무여건을 개선하는 것이 매우 시급하다는 것을 시사해주고 있다. 그리고 다양한 교재연구 방법과 참고자료를 쉽게 이용할 수 있도록 통합시스템을 갖추어 교사들이 쉽게 자주 이용할 수 있도록 제도적인 방침과 홍보차원의 연수가 필요하다는 것을 느끼게 해주고 있다.둘째, 대체로 과학교사들은 행정업무처리, 담임업무, 기타 잡무 등으로 인해 교재연구 시간의 부족을 느끼고 있었으며, 주로 모든 일과를 마친 오후 시간을 할애하여 교재연구를 하고 있는 것으로 나타났다. 또한 실험중심의 수업준비 및 실험평가를 많이 해야 하는 교과의 특성으로 인해 수업시수에 대해 많은 부담을 느끼고 있었으며, 교과교실(과학실험실)의 부족 및 노후화로 인해 적절한 수업교구와 매체를 충분히 활용하지 못하는 경우도 있는 것으로 드러났다. 아울러 기초과학교육의 내실화를 위해서 충분한 과학시수의 확보도 중요한 당면과제로 판단된다.셋째, 과학관련 연수의 필요성 및 중요성에 관해서는 많은 교사들이 공감하고 있었으며, 새로운 지식과 교과서와 관련된 내용 그리고 생활속에서의 과학 등의 연수를 원하고 있다는 응답이 많이 나왔다. 이는 이론중심의 연수보다는 실제 수업에 활용하고 적용할 수 있는 실질적이고도 다양한 연수프로그램의 개발이 필요함을 알 수 있다.넷째, 대부분의 교사들은 수업준비를 위해 적절한 교재선정 및 교재활용이 필요하다고 응답하였으며 교과와 관련된 유용한 정보들을 구하기 위해 교육관련 기관 자료실, 학교의 교과모임, 시·도교육청 배포자료 등을 활용하는 것으로 나타났다. 또한 교사 자신의 교과전문성 정도를 교재선정의 중요한 선택기준으로 삼고 있었다. 특히 주목할 결과는 많은 교사들이 시·도교육청 및 교육관련 기관에서 제작·보급한 자료들을 수업에 직접 활용하는 정도는 매우 낮은 것으로 나타났는데 이는 이들 자료들이 실제 수업에 적용하기에 적합하지 않은 이론중심의 내용으로 구성되어 있기 때문으로 판단된다. 또한 이러한 자료들을 자주 사용해 보지 않았거나 사용방법을 모르기 때문이라는 응답도 상당 수 있었다.이러한 결과는 교사들의 교재연구 욕구에 부응하고 관련 자료들을 체계적으로 데이터베이스화하는 자료구축 시스템을 구축·재정비할 필요가 있음을 시사해주고 있다. 예를 들어 최근 체계적으로 운영, 홍보되고 있는 서울 교수·학습 지원센터를 적극 활용할 수 있도록 학교현장에 전문 지도강사를 지원하여 지속적인 연수를 실시하는 것도 한 가지 방안이라고 할 수 있다.

Text Augmentation for Named Entity Recognition in Materials Science

SungJu Lee 고려대학교 대학원 2025 국내석사

In applying natural language processing to materials science, named entity recognition (NER) is a key component for constructing structured databases from unstructured research literature. However, NER in materials science faces data scarcity due to two challenges. First, NER data requires word-level labeling which raises annotation costs due to the high labor intensity. Second, it demands annotators with specialized knowledge in materials science, lifting the cost heavier. To address the data scarcity problem in materials science NER, we propose a data augmentation pipeline tailored to materials science. Starting with a previous framework based on the masked language model, we suggest three improvements. First, we employ a materials-specific language model to generate materials science terms. Second, we enhance generation with entity information from a materials-specific knowledge graph. Lastly, we enable entity-level generation to address a limitation of masked language modeling. Experiments on three datasets demonstrate that the suggested augmentation pipeline improves NER performance in materials science, proving the effectiveness of domain-optimized augmentation strategies. 재료 과학 분야에 자연어 처리를 적용할 때, 개체명 인식 (Named Entity Recognition) 은 비정형 텍스트인 연구문헌을 구조화된 데이터베이스로 가공하는 핵심 요소로서 기능한다. 그러나, 재료 과학에서의 NER은 두 가지 이유로 인한 데이터 부족 문제에 직면해 있다. 우선 NER 데이터는 단어 수준의 라벨링이 필요하기에 주석 처리를 위한 노동량이 크기에 생성 비용이 크다. 또한, 이러한 노동을 재료 과학에 대한 고등 교육 을 받은 인력으로 수행해야 하기에 그 비용은 더욱 증가한다. 본 논문에서는 재료 과학 NER에서의 데이터 부족 문제를 해결하기 위해, 재료 과학 도메인에 최적화된 데이터 증강 파이프라인을 제안한다. 기존의 마스킹 언어 모델 (Masked Language Model) 에 기반한 증강 방법에서 출발해, 우리는 세 가지 개선점을 도입해 최적화한다. 첫째, 재료 과학에 특화된 언어 모델을 활용해 개체 어휘를 생성한다. 둘째, 재료 과학 지식 그래프에서 개체 정보를 추출해 어휘 생성을 보조한다. 마지막으로, 토큰이 아닌 개체 단위의 생성을 통해 마스킹 언어 모델의 한계를 완화한다. 우리는 실험적으로 제안된 증강 파이프라인이 재료 과학 분야에서 NER 성능 향상에 기여할 수 있음을 입증하며, 도메인에 최적화된 증강 전략의 효율성을 보인다.

Utilizing Literature to Characterize Materials from Images

Jiang, Weixin Northwestern University ProQuest Dissertations & T 2022 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

The past decade has seen the rapid progress of deep learning, which becomes a game-changing technique in different data-intensive domains, with the availability of large scale data, cost-effective computing hardware and more advanced learning theory and algorithms. Despite of the rapid progress of deep learning methods in daily-life applications, such as face recognition, video enhancement, image classification, there are some challenges that prevent the application of deep learning into more research fields, such as materials science.Materials science have been developed through the empirical correlation of processing and properties for thousands of years. Recently, tons of experimental and simulated data are captured/produced everyday due to the fast image acquisition devices and super computing facilities. The success of deep learning techniques in other fields ( e.g. computer vision) motivates researchers in materials science to develop more advanced algorithms to accelerate the process of discovering and designing new improved materials with desired properties. Unfortunately, applying deep learning techniques in materials science still remains at its early stage and requires more efforts from researchers.In this thesis, I will present my work in understanding the characterization of materials from images. One challenge in developing data-driven algorithms in materials science is the lack of well-labeled datasets (e.g. microscopy images). In fields that dealing with natural image classification or detection tasks, large amount of images are annotated by human annotators (e.g. ImageNet, MS-coco), however, it would be expensive and even not feasible in the field of materials science, due to its requirement of sufficient domain expertise. To this end, we present our work in construction of Materials dataset from scientific literature, in which we developed an effective tool to construct a self-labeled electron microscopy dataset of nanostructure images.In the second part of the thesis, I will present our work on the interpretation of spectrographs. For the purpose of understanding the insights behind these measurements, data points are usually displayed in graphical form within scientific journal articles. However, it is not standard for materials researchers to release raw data along with their publications. As a result, other researchers have to use interactive plot data extraction tools to extract data points from the graph image, which makes it difficult for large scale data acquisition and analysis. Therefore, we propose the Plot2Spectra pipeline, which enables an efficient spectra data extraction from plot images in an fully automatic fashion.As the last part of the thesis, I will present our work in deducing structure information from STEM (Scanning Transmission Electron Microscopy) measurements. Microscopic imaging providing the real-space information of matter in a large range of scale, which plays an important role for understanding the correlations between structure (e.g. morphology, phase, atomic structure, surface facet, interfacial structure) and properties in the field of materials science. Thus, extracting the structural information (e.g. atomic positions ) plays a very important role in exploration of the crystallographic phases, atomic configurations and the insights behind the structure related material-specific properties and performance. However, it is a challenging task to deduce the structure information from STEM measurements. To this end, we present a representation learning framework for HAADF-STEM image retrieval, named STEM2SIM, to deduce the structure information (e.g. crystalline structure) from the given STEM image by efficiently find the similar image (i.e. known structure) from a simulated dataset.

Elucidating the Origin of Heterogeneous Stresses in Granular and Microstructured Materials Using Data Science Methods

Shrivastava, Ankit Carnegie Mellon University ProQuest Dissertations 2021 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

The heterogeneity in microstructured and granular materials due to micro-scale structures causes high stresses when an external load is applied. The high stresses in microstructured and granular materials are seen in the form of peak-stress clusters and force chains, respectively. These high stresses play a significant role in governing materials’ strength; hence it is crucial to know their relationship with the micro-scale properties to design high-strength materials.We use data-science-based methods to understand the relationship between microscale properties and high stresses in these materials. Since both materials are different, the data science approach for finding the relationship between the micro-scale properties and high stresses is also different. A learned feature-based approach is utilized for microstructured materials, and for granular materials, a hand-crafted feature-based approach is used.In the learned feature-based approach, the microstructures’ grain features that are causing peak-stress clusters are detected using a deep-learning-based Convolutional Encoder-Decoder (CED) method. The CED method is first trained to predict linear elastic calculations of von Mises stress fields in synthetically-generated microstructures. The accuracy analysis showed that the proposed method is well-suited to predict the characteristics of the peak-stress clusters. Later, saliency maps are obtained to realize the microstructures’ regions that were causing peak-stresses. The proposed CED method is computationally much faster than existing numerical schemes. With high accuracy, fast predictions, and the ability to detect long-range effects, the CED method can be used for the on-site design of high-strength materials.In granular materials, the effects of particle size and cohesion on the force chains are examined in bi-disperse (with two types of particles) systems using a hand-crafted feature-based approach. The effect of particle size is studied in terms of various disorders induced in the granular materials. Here, the disorders induced due to the difference in the size of the two types of particles are denoted as size-disorders. In the hand-crafted feature-based approach, the features describing the disorders and cohesion are then examined against the graph-based descriptors of force chains using data visualization. The particles in the granular materials with stresses above a threshold and forming long continuous chains are characterized as force chains. It was observed that no force chains are present in ordered materials. Moreover, with the increase in size-disorder, the force chains are observed even in the materials without positional disorders. It was also observed that in the granular materials, irrespective of positional disorder, the force chains do not form with an increase in the cohesion of the particles. This study shows that the size and cohesion of the particles play an important role in causing force chains irrespective of positional disorders.In conclusion, the work in this thesis shows that data science-based methods can be effectively used to understand the relationship between the micro-scale properties and high stresses of microstructured and granular materials.

Stochastic multiscale modeling of polycrystalline materials

Wen, Bin Cornell University 2013 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Mechanical properties of engineering materials are sensitive to the underlying random microstructure. Quantification of mechanical property variability induced by microstructure variation is essential for the prediction of extreme properties and microstructure-sensitive design of materials. Recent advances in high throughput characterization of polycrystalline microstructures have resulted in huge data sets of microstructural descriptors and image snapshots. To utilize these large scale experimental data for computing the resulting variability of macroscopic properties, appropriate mathematical representation of microstructures is needed. By exploring the space containing all admissible microstructures that are statistically similar to the available data, one can estimate the distribution/envelope of possible properties by employing efficient stochastic simulation methodologies along with robust physics-based deterministic simulators. The focus of this thesis is on the construction of low-dimensional representations of random microstructures and the development of efficient physics-based simulators for polycrystalline materials. By adopting appropriate stochastic methods, such as Monte Carlo and Adaptive Sparse Grid Collocation methods, the variability of microstructure-sensitive properties of polycrystalline materials is investigated. The primary outcomes of this thesis include: (1) Development of data-driven reduced-order representations of microstructure variations to construct the admissible space of random polycrystalline microstructures. (2) Development of accurate and efficient physics-based simulators for the estimation of material properties based on mesoscale microstructures. (3) Investigating property variability of polycrystalline materials using efficient stochastic simulation methods in combination with the above two developments. The uncertainty quantification framework developed in this work integrates information science and materials science, and provides a new outlook to multi-scale materials modeling accounting for microstructure and process uncertainties. Predictive materials modeling will accelerate the development of new materials and processes for critical applications in industry.

성공지능 관점에서 과학영재교육 교재에 등장하는 발문 분석

진미나 전북대학교 교육대학원 2017 국내석사

The purpose of this study was to analysis on the questions of the teaching materials for science gifted education using successful intelligence frame work. 143 questions of teaching materials for CPAS(centers for the provincial education office affiliated science-gifted education) from 2015, and 134 questions of teaching materials for CUAS(a center for the university affiliated science-gifted education) from 2015 are used as the subjects of analysis. The questions were analyzed using the successful intelligence frame work and semantic network analysis method. Findings are listed below. First, identifying problem questions of analytical ability, generating ideas questions of creative ability and prompting for practical thinking questions of practical ability were used many times in the study of teaching materials for CPAS. Secondly, although representing and organizing information questions of analytical ability and generating ideas questions of creative ability were used many time in the study of teaching materials for CUAS, there are only a few questions of practical ability. Third, various kind of questions that are related to the successful intelligence were used more in the teaching materials for CPAS than the teaching materials for CUAS. Additionally, the successful intelligence component were more connected in the questions of teaching materials for CPAS than the questions of teaching materials for CUAS. Based on the result of this study, this paper suggests the following: Firstly, non-cognitive factors of creative ability is needed in the teaching materials for CPAS. Secondly, more questions of practical ability is also necessary in the teaching materials for CUAS using subject convergence and thinking convergence.

Development of Ethno-Pedagogy Oriented Science Teaching Materials for Primary Pre-Service Teachers Competencies

수산티 한국교원대학교 대학원 2020 국내석사

Optimization of local wisdom by the ethno-pedagogy approach to provide the pre-service teachers teaching effectiveness is needed. Indonesia has, moreover, 250 cultures that can use as an education tool. This research aimed to describe the ability of primary pre-service teachers in developing ethno-pedagogy oriented science teaching materials. This research used a quantitative and content analysis technique, data obtained based on the pre- and post-survey survey, science teaching materials analysis, and interview results. The participants of this study are pre-service teachers as many as 62 persons at Institut Agama Islam Negeri (IAIN) and Institute Sunan Giri Ponorogo (INSURI) East Java, Indonesia. The content analysis of the science teaching materials shows that pre-service teachers have mind construction and alteration to accept new knowledge and strategy in teaching science by adjusting to their own culture. The survey results indicated that the ethno-pedagogy gave preliminary experience with the pre-survey has a higher score rather than the post-survey. Based on the interview analysis, pre-service teachers feel indecisive because of excited gaining new approaches and overwhelming pour their ideas into science teaching. The research also revealed that the pre-service teachers did not have an ethno-pedagogy course in their first course, and there was a limitation of adequate resources and other academic facilities. Overall, the results suggest that ethno-pedagogy plays an essential role in the development of pre-service teachers’ competency.

Synthesis and Characterization of Machine Learning Predicted Thermoelectric Materials

Graser, Jake The University of Utah ProQuest Dissertations & Th 2020 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Thermoelectric materials have the capacity to revolutionize multiple fields of engineering. From focused cooling such as computer’s central processing unit (CPU) to energy harvesting for wearable devices. However, these materials have historically been hindered by low performance and expensive materials. The ideal thermoelectric material has a high electric conductivity and low thermal conductivity, a so called ”phonon-glass electron crystal.” The discovery of a low cost, nontoxic thermoelectric material could help increase efficiency as well as reduce the size of known materials. The difficulty of material discovery is not restricted to the field of thermoelectric materials. Many fields such as batteries and high strength materials require new materials to overcome bottlenecks within their fields. A new approach is the use of computational recommendations utilizing machine learning. By taking a large dataset, models can be trained to recommend materials with predicted properties. These algorithms use multiple statistical methods to verify accuracy such as k-fold cross validation as well as precision and recall. Yet, experimental verification is king. In this paper, we will explore the physical properties of a thermoelectric materials as well as some choice papers using machine learning to predict new thermoelectric materials of interest. We will then discuss the methods of synthesis and characterization of thermoelectric materials in this study. We will then conclude with three papers detailing the investigation of these materials and their measurements.

Three-dimensional helical carbon materials: Microcoiled carbon fibers, carbon nanocoils, carbon nanotubes: Synthesis, properties and applications

Xie, Jining The Pennsylvania State University 2003 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Materials with a 3D-helical/spiral-structure in micron size have recently aroused a great deal of interests because of their helical morphology and unique properties. However, materials with a 3D helical structure are not commonly observed among industrially available materials. Researchers have been trying to synthesize various micro- and nano-sized 3D helical materials and are exploring the mechanisms, nature, and properties of these materials. Yet a systematic study on 3D helical carbon materials in micro- and nano-size has been missing. This research work is intended as a first step to fill this gap. Among various 3D helical materials, carbon element has stimulated great interests. Micro coiled carbon fibers, carbon nanocoils, and carbon nanotubes are major types of 3D helical carbon materials ranging from micron to nano size. Synthesis of these 3D helical carbon materials by a catalytic chemical vapor deposition method is presented in this thesis. It involves a pyrolysis of hydrocarbon gas (e.g. acetylene) over transition metals, such as Ni, Fe, and Co, at high reaction temperature (500–1000°C). Besides the conventional thermal filament chemical vapor deposition method, a novel microwave chemical vapor deposition (MWCVD) method has been developed to synthesize micro- and nano-sized 3D helical carbon materials economically. The faster heating and cooling processes associated with microwave CVD have potential for large-scale production in the near future. Compared with previously reported microwave plasma enhanced chemical vapor deposition (MWPECVD) method, this method does not require high vacuum and much higher deposition rate is another major advantage. It has been found in this work that microwave plays an important role on coil morphology formation for micro coiled carbon fibers and carbon nanocoils. The large temperature gradient around the catalytic particles could be the reason. Different reaction factors have been checked to optimize the deposition. Due to their extraordinary properties, carbon nanotubes have been expected to have wide applications. Efforts have been made on the synthesis of high quality carbon nanotubes economically in this work. A novel catalyst/catalyst support pair, iron/magnesium carbonate, has been developed for synthesis of multi-walled carbon nanotubes with high purity. The coil morphology is induced by insertion of pentagon-heptagon pairs into hexagonal network of nanotube wall periodically. Thorough purification of carbon nanotubes is always a concern before investigating their properties and potential applications. Impurities in raw carbon nanotube material have to be removed by chemical treatment. A couple of purification methods are presented in this work. Various techniques have been used to characterize these micro- and nano-3D materials, such as scanning electron microscopy (SEM), energy dispersive spectrum (EDS), transmission electron microscopy (TEM), X-ray diffraction (XRD), Brunauer Emmett-Teller (BET), thermal gravimetric analysis (TGA), etc. Growth mechanisms are proposed based on the experimental and characterization results. It is verified that the nonuniform carbon deposition rate on catalyst particles leads to the bending of the carbon fiber/tubule, and hence results in the coil morphology. To conclude, the research work reported here is a systematic study on synthesis, characterizations, and applications of micro- and nano-3D helical carbon materials, such as micro coiled carbon fibers, carbon nanocoils and carbon nanotubes. A few suggestions for future research directions are also listed.

Multiscale Modeling of Soft Materials and Related Biological Responses

Li, Ying Northwestern University 2015 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Liquids, polymers, gels, foams and a number of biological materials are soft materials, which can be easily deformed by thermal stress or thermal fluctuations. Predominant physical behaviors of these soft materials occur at energy scale comparable with room temperature thermal energy. These behaviors cannot be, or are not easily, directly predicted from its atomic or molecular constituents. This is because the soft materials are always self-assemble into mesoscopic structures, which are much larger than the microscopic scale (the scale of atoms and molecules), and yet much smaller than the macroscopic (overall) scale of these materials. Especially, the mechanical and physical properties of soft materials originate from the interplay of phenomena at different spatial and temporal scales. Simultaneously considering these behaviors at different scales is a forbidden challenge, even with the state-of-the-art supercomputer. As such, it is necessary to adopt multiscale techniques when dealing with soft materials in order to account for all important mechanisms. We start with studying the structure and dynamics of polymeric materials. Using the Iterative Boltzmann Inversion method, both the static structures and dynamic behavior of all-atomistic models of polymers (such as polyisoprene and polyethylene) can be reproduced by a simple coarse-grained model, which bridges the scale from micro to meso. On this coarse-grained level, the entangled network of polymer chains is described via a primitive path analysis, which allows us to extraction of the tube diameter and primitive chain length, quantities required to bridge the scale from meso to macro. Furthermore, by making the affine-deformation assumption, a continuum constitutive law for polymeric materials has been developed from the tube model of primitive paths, which can be applied to study mechanical behaviors of polymeric materials. In this way, the different scales are crossed by using different bridging laws, which enable us to directly predict the viscoelastic properties of polymeric materials using a bottom-up approach. Our predicted dynamic moduli, zero-rate shear viscosities, and relaxation moduli of polyisoprene and polyethylene polymers are found to be in excellent agreement with experimental results. We then investigated the structure and dynamics of polymer nanocomposites through multiscale modeling, by considering the different volume fractions of fillers. When highly entangled polymer chains are confined between fillers, their conformation and entanglement network are dramatically changed, in contrast to their unentangled counterparts. The entangled polymer chains are found to be significantly disentangled and flattened during increment of the volume fractions of spherical nonattractive fillers. A critical volume fraction is found to control the crossover from polymer chain entanglements to "nanoparticle entanglements", below which the polymer chain relaxation accelerates upon filling. These results provide a microscopic understanding of the dynamics of entangled polymer chains inside their composites, and offer an explanation for the unusual rheological properties of polymer composites. The endocytosis of polymer grafted (PEGylated) nanoparticles is studied by using the large scale dissipative particle dynamics simulations and self-consistent field theory. The free energy change of grafted polyethylene glycol (PEG) polymers, before and after endocytosis, is identified to have an effect which is comparable to, or even larger than, the bending energy of the membrane during endocytosis. By incorporating the free energy change of PEG, the critical ligand-receptor binding strength for PEGylated NPs to be internalized can be correctly predicted by a simple analytical equation. Without considering this free energy change, it turns out impossible to predict whether the PEGylated NPs will be delivered into the diseased cells. These simulation results and theoretical analysis not only provide new insights into the endocytosis process of PEGylated NPs, but also shed light on the underlying physical mechanisms, which can be utilized for designing efficient PEGylated NP-based therapeutic carriers with improved cellular targeting and uptake. The contributions of this dissertation are threefold: (1) establishing a multiscale modeling framework to predict macroscopic behaviors of polymers with molecular information, (2) utilizing the multiscale modeling technique to provide mechanisms underpinning the design of polymer nanocomposites, (3) rapid computational prototyping and testing of polymer grafted drug carriers for targeted drug delivery.