Ethanol obtained from the conversion process of different types of biomass is a renewable source of fuel and since 2010 it has been classified as an “advanced fuel” by the EPA, due to its contribution to the reduction of the impacts of GHG emissions. Recent literature stresses the importance of the use of second-generation fuels to reduce the impacts of the direct and indirect use of land, mostly on agricultural prices. Although these demands constitute a clear clue to R&D activities, there are an impressive number of alternatives, regarding different kinds of biomass, processes and byproducts, a complex matrix of technological opportunities and the demands that generates a clear incentive for collaboration. This paper uses both the Bibliometry and Scientometry approach and the Innovation System (IS) literature under the perspective of Social Networks Analysis (SNA) to build Collaborative Networks (CNs) to the second-generation ethanol (lignocellulosic) using ISI Web of Science database. The adopted procedure emerges once authors, countries and institutions related to bioenergy have incentives to share information in the process of creating a new role in partnership—a network point-of-view. The results show that the United States is in a better position than other countries, improving the role of the university in their IS while China proves to be a great ally of the United States regarding the production of technology to produce lignocellulosic ethanol. Brazil however, does not appear well placed in the network, despite being the second largest producer of first-generation ethanol in the world.
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In more simple terms, the process for obtaining second-generation ethanol consists of "breaking" the lignocellulosic plant material (which may be done physically or through chemical or enzymatic reactions) to obtain the cellulose. In this case sucrose is obtained, and one of the destinations is the production of ethanol. To convert lignocellulosic materials into other products the following steps must be performed: (1) pretreatment of lignocellulosic material in order to increase the exposure of the pulp fibers, facilitating the action of acids or enzymatic hydrolytic agents; (2) use of enzymes from microorganisms such as fungi and bacteria, obtaining sugars by the enzymatic hydrolysis process; and (3) fermentation process of the sugar mixture. See for more details, Brown and Brown (2012), Lee (1997), Sun and Cheng (2002) and Rabelo (2010).
Consists of obtaining ethanol through a fermentation and distillation process from disposable and significant sugars that are in the plants. The main commercial crops are sugarcane, maize, sugar beet, potato and wheat.
Using the same queries for both approaches—bibliometrics and scientometrics—with the content analysis of scientific papers.
The panel sessions were held in a meeting coordinated by BIOEN-Research team during the early 2012 with researchers from bioenergy research centers from the University of São Paulo, State Paulista University (UNESP), State University of Campinas and CTBE.
The VantagePoint version 7. http://www.thevantagepoint.com/.
For this procedure, three different programs were chosen: Microsoft Excel 2013—Data tabulation of The VantagePoint version 7 and exported to UCINET version 6 (import the data, graph building, analysis of indicators for networks and nodes and visualization of relationships among key stakeholders and Gephi version 0.82 beta—This program allows artistic visualization of networks. For the display the Fruchterman-Reingold algorithm was chosen as it best represented the data because of the enormous quantity of relationships. This algorithm represents a force-directed layout because it considers a force between any two nodes. In this algorithm, the nodes are represented by steel rings and the edges have springs between them. The attractive force is analogous to the spring force and the repulsive force is analogous to the electrical force. The basic idea is to minimize the energy of the system by moving the nodes and changing the forces between them (Fruchterman and Reingold 1991). Because of the enormous quantity of data there was a superposition of nodes, even using the algorithm, so the second-step was avoid the superposition of most relevant nodes of the network, setting to the nodes be placed at the bound of the sphere.
KeyWord Plus is a kind of automatic indexing used in the citation databases produced by ISI.
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de Souza, L.G.A., de Moraes, M.A.F.D., Dal Poz, M.E.S. et al. Collaborative Networks as a measure of the Innovation Systems in second-generation ethanol. Scientometrics 103, 355–372 (2015). https://doi.org/10.1007/s11192-015-1553-2
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