Yuya Kajikawa

Comprehensive perspective on the mechanism of preferred orientation in reactive-sputter-deposited nitrides

Texture control of sputter-deposited nitride films has provoked a great deal of interest due to its technological importance. Despite extensive research, however, the reported results are scattered and discussions about the origin of preferred orientation (PO) are sometimes conflicting, and therefore controversial. The aim of this study is to acquire a clear perspective in order to discuss the origin of PO of sputter-deposited nitrides. Among nitrides, we focus on titanium nitride (TiN), aluminum nitride (AlN), and tantalum nitride (TaN), which are three commonly used nitrides. First, we collected reported experimental results about the relation between operating conditions and PO, because PO is considered to be determined by film formation processes, such as surface diffusion or grain growth, which is affected by operating conditions. We also collected reported results about such PO-determining processes. Then, we categorized the PO-determining processes into an initial stage and a growth stage of film d...

Sustainability science: the changing landscape of sustainability research

Sustainability science is a rapidly expanding field, particularly given the current ecological crises facing many parts of the globe today. To generate a snapshot of the state of sustainability science, we analyzed the current status of sustainability research using citation and text analysis. By reflecting social needs on sustainability science and the increasing number of publications in this field, the landscape is expected to change during the last decade. Our results indicate that previously separated research clusters investigating discipline-focused issues are becoming integrated into those studying coupled systems. We also found the existence of hub clusters bridging different clusters like socio-ecological systems and transition management. We also observed a variety of other emerging research clusters, especially in energy issues, technologies, and systems. Overall, our analysis suggests that sustainability science is a rapidly expanding and diversifying field, which has affected many disparate scientific disciplines and has the potential to feed scientific understanding on socio-ecological systems and to drive society toward transition for sustainability.

Nanobiotechnology as an emerging research domain from nanotechnology: A bibliometric approach

Nanotechnology has been intensively investigated by bibliometric methods due to its technological importance and expected impacts on economic activity. However, there is less focus on nanobiotechnology, which is an emerging research domain in nanotechnology. In this paper, we study the current status of the former, with our primary focus being to reveal the structure and research domains in nanobiotechnology. We also examine country and institutional performance in nanobiotechnology. It emerged that nanostructures, drug delivery and biomedical applications, bio-imaging, and carbon nanotubes and biosensors are the major research domains, while the USA is the leading country, and China has also made substantial contribution. Most institutions having a major impact in the area of nanobiotechnology are located in the USA.

Topological analysis of citation networks to discover the future core articles

In this article, we investigated the factors determining the capability of academic articles to be cited in the future using a topological analysis of citation networks. The basic idea is that articles that will have many citations were in a “similar” position topologically in the past. To validate this hypothesis, we investigated the correlation between future times cited and three measures of centrality: clustering centrality, closeness centrality, and betweenness centrality. We also analyzed the effect of aging as well as of self‐correlation of times cited. Case studies were performed in the two following recent representative innovations: Gallium Nitride and Complex Networks. The results suggest that times cited is the main factor in explaining the near future times cited, and betweenness centrality is correlated with the distant future times cited. The effect of topological position on the capability to be cited is influenced by the migrating phenomenon in which the activated center of research shifts from an existing domain to a new emerging domain.

Link prediction in citation networks

In this article, we build models to predict the existence of citations among papers by formulating link prediction for 5 large-scale datasets of citation networks. The supervised machine-learning model is applied with 11 features. As a result, our learner performs very well, with the F1 values of between 0.74 and 0.82. Three features in particular, link-based Jaccard coefficient, difference in betweenness centrality, and cosine similarity of term frequency-inverse document frequency vectors, largely affect the predictions of citations. The results also indicate that different models are required for different types of research areas--research fields with a single issue or research fields with multiple issues. In the case of research fields with multiple issues, there are barriers among research fields because our results indicate that papers tend to be cited in each research field locally. Therefore, one must consider the typology of targeted research areas when building models for link prediction in citation networks.

Preferred Orientation of Chemical Vapor Deposited Polycrystalline Silicon Carbide Films

We investigated the mechanism that determines the preferred orientation of polycrystalline silicon carbide (SiC) films prepared by CVD from a mixture of dichlorodimethylsilane (DDS) and He. X-ray diffraction (XRD) measurements indicated that the major growth direction is either the (220) or the (111) plane. We developed a numerical model for predicting the preferred orientation, assuming Langmuir-type adsorption and reaction of the growth species. This model suggests that the (111) plane appears under reaction-limited deposition, while the (220) plane appears under adsorption-limited deposition. Our experimental and numerical results show good qualitative agreement with experimental results for films prepared from methyltrichlorosilane (MTS) and H 2 .

Filling the gap between researchers studying different materials and different methods: a proposal for structured keywords

Scientific publications written in natural language still play a central role as our knowledge source. However, due to the flood of publications, obtaining a comprehensive view even on a topic of limited scope, from a stack of publications is becoming an arduous task. Examples are presented from our recent experiences in the materials science field, where information is not shared among researchers studying different materials and different methods. To overcome the limitation, we propose a structured keywords method to reinforce the functionality of a future e-library.

Optics: a bibliometric approach to detect emerging research domains and intellectual bases

Optics is an important research domain both for its scientific interest and industrial applications. In this paper, we constructed a citation network of papers and performed topological clustering method to investigate the structure of research and to detect emerging research domains in optics. We found that optics consists of main five subclusters, optical communication, quantum optics, optical data processing, optical analysis and lasers. Then, we further investigated the detailed subcluster structures in it. By doing so, we detected some emerging research domains such as nonlinearity in photonic crystal fiber, broad band parametric amplifier, and in-vivo imaging techniques. We also discuss the distinction between research front and intellectual base in optics.

Machine learning approach for finding business partners and building reciprocal relationships

Business development is vital for any firms. However, globalization and the rapid development of technologies have made it difficult to find appropriate business partners such as suppliers and customers, and build reciprocal relationships among them, while it simultaneously offers many opportunities. In this contribution, we propose AI-based approach to find plausible candidates of business partners using firm profiles and transactional relationships among them. We employ machine learning techniques to build a prediction model of customer-supplier relationships. We applied our approach to the large amount of actual business data. The results showed that our approach successfully found potential business partners with F-values of about 84% and reciprocity among them with F-values of about 77%. Using our method, we also developed the Web-based system that helps people in actual businesses to find their new business partners. These contribute to developing ones own business in the complicated, specialized and rapidly changing business environments of recent years.

Detecting potential technological fronts by comparing scientific papers and patents

Purpose – This paper seeks to propose a method of discovering uncommercialized research fronts by comparing scientific papers and patents. A comparative study was performed to measure the semantic similarity between academic papers and patents in order to discover research fronts that do not correspond to any patents.Design/methodology/approach – The authors compared structures of citation networks of scientific publications with those of patents by citation analysis and measured the similarity between sets of academic papers and sets of patents by natural language processing. After the documents (papers/patents) in each layer were categorized by a citation‐based method, the authors compared three semantic similarity measurements between a set of academic papers and a set of patents: Jaccard coefficient, cosine similarity of term frequency‐inverse document frequency (tfidf) vector, and cosine similarity of log‐tfidf vector. A case study was performed in solar cells.Findings – As a result, the cosine simil...