Authors:W. Sato, K. Sueki, K. Kikuchi, K. Kobayashi, S. Suzuki, Y. Achiba, H. Nakahara, Y. Ohkubo, F. Ambe, and K. Asai
Time-differential perturbed angular correlation method was applied to Ce@C82 in order to investigate the electronic properties of the encaged Ce atom. The nuclear quadrupole frequency of the nuclear
spin of the Ce was successfully determined as ωQ = 6.5(3)·107 rad/s, which is much greater than what has already been estimated for Ce4+ and La3+ in other compounds. By comparing the present value with the values for the Ce4+ and La3+, it was inferred that the valence state of the encaged Ce atom is trivalent.
Authors:Yue Cheng, Kaiming Liu, Gengmei Xing, Hui Yuan, Long Jing, and Yuliang Zhao
The water-solubilization of metallofullerenes is important for their potential applications, but their formation processes
are still not clear, and the formation yield is uncontrollable. In this paper, we quantitatively studied the water-solubilizing
process of Gd@C82 with hydroxylation reaction using ICP-MASS techniques. For the first time, it was found that the formation yield of the multihydroxylated
Gd@C82 is declined quickly with the break up of carbon cage of Gd@C82 in the hydroxylated processes. The observation revealed a way to control the hydroxylation processes and increase the formation
Authors:Jun Tang, Gengmei Xing, Hui Yuan, Xingfa Gao, Long Jing, Shukuan Wang, Yue Cheng, and Yuliang Zhao
The electronic properties of the metal atoms encaged in a fullerence cage were investigated using synchrotron X-ray photoelectron
spectroscopy. Systematic variations in photoemission of valence band of Gd@C82, Gd@C82(OH)12, and Gd@C82(OH)22 were observed in Gd 5p levels. The results suggest that the electronic properties of the inner metal atom can be efficiently
modulated by surface chemistry of the fullerene cage.
Authors:Li Ying Yang, Ting Yue, Jie Lan Ding, and Tao Han
Using a collection of papers gathered from the Web of Science, and defining disciplines by the JCR classification, this paper compares the disciplinary structure of the G7 countries (representing high S&T level countries) and the BRIC countries (representing fast breaking countries in S&T) by using bibliometric methods. It discusses the similarity and the balance of their disciplinary structure. We found that: (1) High S&T level countries have a similar national disciplinary structure; (2) In recent years the disciplinary structure of the BRIC countries has become more and more similar to that of the G7 countries; (3) The disciplinary structure of the G7 countries is more balanced than that of the BRIC countries (4) In the G7 countries more emphasis goes to the life sciences, while BRIC countries focus on physics, chemistry, mathematics and engineering.
This paper examines the drivers and the size of the shadow economies of the Czech Republic, Hungary and Poland. It also investigates the tax losses associated with these shadow economic activities in all three countries. The Multiple Indicators and Multiple Causes (MIMIC) model is applied and uses time series data covering the period 1990–2019. The key findings show that the sizes of the shadow economies of the Czech Republic, Hungary and Poland are 10.44, 11.18 and 20.47% respectively. The results also show that the average size of the shadow economies between 1990–2019 was 14.92% in the Czech Republic, 18.72% in Hungary and 22.85% in Poland. The Czech Republic loses 3.13% of tax revenue from goods and services and 2.83% from incomes and profits as a result of the shadow economy, while Hungary loses 5.05% of tax revenue from goods and services and 1.68% from incomes and profits. Poland loses 5.25% of tax revenue from goods and services and 4.34% from incomes and profits.
Authors:Sungchul Choi, Janghyeok Yoon, Kwangsoo Kim, Jae Yeol Lee, and Cheol-Han Kim
This paper suggests a method for Subject–Action–Object (SAO) network analysis of patents for technology trends identification by using the concept of function. The proposed method solves the shortcoming of the keyword-based approach to identification of technology trends, i.e., that it cannot represent how technologies are used or for what purpose. The concept of function provides information on how a technology is used and how it interacts with other technologies; the keyword-based approach does not provide such information. The proposed method uses an SAO model and represents “key concept” instead of “key word”. We present a procedure that formulates an SAO network by using SAO models extracted from patent documents, and a method that applies actor network theory to analyze technology implications of the SAO network. To demonstrate the effectiveness of the SAO network this paper presents a case study of patents related to Polymer Electrolyte Membrane technology in Proton Exchange Membrane Fuel Cells.
In the competitive business environment, early identification of technological opportunities is crucial for technology strategy formulation and research and development planning. There exist previous studies that identify technological directions or areas from a broad view for technological opportunities, while few studies have researched a way to detect distinctive patents that can act as new technological opportunities at the individual patent level. This paper proposes a method of detecting new technological opportunities by using subject–action–object (SAO)-based semantic patent analysis and outlier detection. SAO structures are syntactically ordered sentences that can be automatically extracted by natural language processing of patent text; they explicitly show the structural relationships among technological components in a patent, and thus encode key findings of inventions and the expertise of inventors. Therefore, the proposed method allows quantification of structural dissimilarities among patents. We use outlier detection to identify unusual or distinctive patents in a given technology area; some of these outlier patents may represent new technological opportunities. The proposed method is illustrated using patents related to organic photovoltaic cells. We expect that this method can be incorporated into the research and development process for early identification of technological opportunities.
Authors:Janghyeok Yoon, Sungchul Choi, and Kwangsoo Kim
Technology analysis is a process which uses textual analysis to detect trends in technological innovation. Co-word analysis (CWA), a popular method for technology analysis, encompasses (1) defining a set of keyword or key phrase patterns which are represented in technology-dependent terms, (2) generating a network that codifies the relations between occurrences of keywords or key phrases, and (3) identifying specific trends from the network. However, defining the set of keyword or key phrase patterns heavily relies on effort of experts, who may be expensive or unavailable. Furthermore defining keyword or key phrase patterns of new or emerging technology areas may be a difficult task even for experts. To solve the limitation in CWA, this research adopts a property-function based approach. The property is a specific characteristic of a product, and is usually described using adjectives; the function is a useful action of a product, and is usually described using verbs. Properties and functions represent the innovation concepts of a system, so they show innovation directions in a given technology. The proposed methodology automatically extracts properties and functions from patents using natural language processing. Using properties and functions as nodes, and co-occurrences as links, an invention property-function network (IPFN) can be generated. Using social network analysis, the methodology analyzes technological implications of indicators in the IPFN. Therefore, without predefining keyword or key phrase patterns, the methodology assists experts to more concentrate on their knowledge services that identify trends in technological innovation from patents. The methodology is illustrated using a case study of patents related to silicon-based thin film solar cells.
Patents constitute an up-to-date source of competitive intelligence in technological development; thus, patent analysis has been a vital tool for identifying technological trends. Patent citation analysis is easy to use, but fundamentally has two main limitations: (1) new patents tend to be less cited than old ones and may miss citations to contemporary patents; (2) citation-based analysis cannot be used for patents in databases which do not require citations. Naturally, citation-based analysis tends to underestimate the importance of new patents and may not work in rapidly-evolving industries in which technology life-cycles are shortening and new inventions are increasingly patented world-wide. As a remedy, this paper proposes a patent network based on semantic patent analysis using subject-action-object (SAO) structures. SAO structures represent the explicit relationships among components used in a patent, and are considered to represent key concepts of the patent or the expertise of the inventor. Based on the internal similarities between patents, the patent network provides the up-to-date status of a given technology. Furthermore, this paper suggests new indices to identify the technological importance of patents, the characteristics of patent clusters, and the technological capabilities of competitors. The proposed method is illustrated using patents related to synthesis of carbon nanotubes. We expect that the proposed procedure and analysis will be incorporated into technology planning processes to assist experts such as researchers and R&D policy makers in rapidly-evolving industries.
Authors:Jun Zhang, Kaimin Liu, Gengmei Xing, Tongxiang Ren, and Shukuan Wang
Gd@C82(OH)40 has been developed as a new generation of MRI contrast agent. But recently, it was found that Gd@C82(OH)x with a larger number of OH (x>36) would lead to cage break and hence, release of highly toxic Gd ions. We synthesized the more stable Gd@C82(OH)x with less OH-number, Gd@C82(OH)16, and studied its proton relaxivity and MRI images. The results indicate that Gd@C82(OH)16 also gives high proton relaxivity, even higher than that of (NMG)2-Gd-DTPA. The bio-distribution indicated that Gd@C82(OH)16 tends to be entrapped in the liver and kidney and remained in tissue for about 2 hours. The results suggest that the more
stable metallofullerene derivative Gd@C82(OH)16 can be the potential candidate of the new MRI contrast agent.