Impact of the Discovery of Fluorous Biphasic Systems on Chemistry: A

Research Article pubs.acs.org/journal/ascecg

Impact of the Discovery of Fluorous Biphasic Systems on Chemistry: A Statistical and Network Analysis Béla Barabás,*,† Ottilia Fülöp,†,‡ Roland Molontay,§ and Gyula Pályi∥ †

Institute of Mathematics, Department of Stochastics, Budapest University of Technology and Economics, Budapest, Hungary Institute of Mathematics, Department of Differential Equations, Budapest University of Technology and Economics, Budapest, Hungary § Institute of Mathematics, Department of Stochastics, Budapest University of Technology and Economics, Budapest, Hungary ∥ Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy ‡

ABSTRACT: Fluorous chemistry has been in the focus of research interests since the end of the last century. One of the most important contributions in the field was the article Facile Catalyst Separation Without Water: Fluorous Biphase Hydroformylation of Olef ins by István T. Horváth and József Rábai. It has had great impact on academic chemical research. According to Web of Science (downloaded on 31 January 2017), in more than two decades, 2384 researchers have cited this work in 1077 scientific documents. In the present paper, we illustrate and analyze the co-authorship network determined by these citing documents. We studied the statistical features of these publications, as well as the macro- and micro-characteristics of the network using standard social network analysis techniques. We also interpret the results from a chemical point of view. KEYWORDS: Fluorous chemistry, Community detection, Social network analysis, Co-authorship network, Scientometrics

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INTRODUCTION In the fall of 1992, István T. Horváth, Senior Staff Chemist at the Corporate Research Laboratories of the Exxon Research and Engineering Company in Annandale, New Jersey, USA, came up with a proposal that the combination of perfluorinated liquids and nonfluorinated organic solvents exhibit particularly advantageous features as reaction media, primarily for homogeneous or biphasic catalytic reactions. He was proving the viability of the fluorous biphasic system concept in collaboration with József Rábai, who at that time was a visiting scientist.1 This discovery initiated a fast evolution of related research activity, to the extent that 10 years later it was termed as a “parallel chemical universe”2 under the new name of f luorous biphasic chemistry,3 emphasizing the analogy to (usual) aqueous chemistry. The main chemical component of these new systems is a fluorous phase, which may be a perfluoroalkane, a perfluordialkylether, or a perfluortrialkylamine, combined with a suitable organic solvent, for example, a system consisting of 0.43 vol % tetradecafluoromethyl-cyclo-hexane, 0.43 vol % n-hexane, and 0.14 vol % toluene, which forms two phases at room temperature but gives a single phase at 36.5 °C and is a suitable solvent for hydroformylation (with Rh-based catalyst). After the desired reaction was completed by a one-phase state, it needs only a few degrees lower temperature and two phases are formed, which enables the easy separation (and recycling) of the catalyst from the product.1,3 Beyond the ingenious © 2017 American Chemical Society

beauty of this easy technique, it gives a very practical tool for one of the most difficult problems of transition metal-catalyzed organic chemical reactions, the separation (and possibly also recycling) of the catalyst from the reaction product.4−6 The original idea of the fluorous biphasic systems was later extended by utilization of partly or perfluorinated groups in organic chemistry, leading to the development of f luorous chemistry. It is the aim of the present paper to explore the proliferation paths of ideas and the sectors of chemical sciences which became influenced by these ideas, as well as the interactions of the research teams involved in problems of this new field of chemistry by statistical and graph theoretical analysis of the publications citing the original Science paper.1 Research collaboration is a fundamental mechanism that unites dispersed knowledge and expertise into new original ideas and discoveries. Co-authorship in research articles is regarded as one of the most important reflections of research collaboration. The idea of extracting a collaboration pattern from bibliographic data dates back to the 1990s, focusing more on institutional aspects of collaboration.7,8 The rise of network science has brought a new general view into co-authorship studies.9,10 The concept of an Erdõs number may be considered as the earliest emergence of network thinking of research collaboration. Pál Erdõs, a famous Hungarian Received: May 31, 2017 Published: July 20, 2017 8108

DOI: 10.1021/acssuschemeng.7b01722 ACS Sustainable Chem. Eng. 2017, 5, 8108−8118

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Figure 1. Number of collaborating authors per paper.

Figure 2. Number of H.I.T.-papers per author.

mathematician published more papers than any other mathematician in history,11 most of them with co-authors. Erdõs has an Erdõs-number of 0. A mathematician who has a joint publication with Erdõs has an Erdõs number of 1; recursively, an author has an Erdõs number of k + 1, where k is the lowest Erdõs number of any of his co-authors. Hence, the Erdõs number measures the graph distance from Erdõs in the co-authorship network of mathematicians. The first extensive study of co-authorship networks was provided by Newman.11,12 A co-authorship network is a network of scientists in which a link between two scientists is formed by their co-authorship in one or more scientific papers. Co-authorship networks since then have been studied extensively in various ways and from various aspects. There are studies that have focused on the evolutionary dynamics of co-authorship networks, and others have analyzed a static snapshot of the network. Some others have investigated the co-authorship network as seen through the papers of important journals or have focused on specific countries; again, others have analyzed the network of the research community that cites a certain important paper (e.g., ref 1 in the present paper).

For a wide-ranging review of the literature in this area, we refer to Kumar.13

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ELEMENTARY STATISTICS OF CITING WORKS

Considering the citation database of Web of Science from 31 January 2017, we analyzed the influence of the paper by I.T. Horváth, J. Rábai, Facile Catalyst Separation Without Water: Fluorous Biphase Hydroformylation of Olef ins1 (published on 07 October 1994 in Science) on academic research. We found 1077 scientific documents which cited this work. We refer to these documents as the H.I.T.-papers. Figures 1 and 2 show the number of collaborating authors per citing works and the histogram providing the number of H.I.T.-papers written by scientists, respectively. As Figure 1 illustrates, the most H.I.T.-papers have three collaborating authors. There is a paper with 16 authors, and this paper emerges as a maximal clique of the network defined in section titled István. T. Horváth’s Network (H.I.T.-network). Figure 2 shows that the maximum number of published H.I.T.-papers per author is 29. This is achieved by two scientists: J. A. Gladysz and C. Cai. 8109


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Figure 3. Journals that published at least 10 citing papers.

Figure 4. Impact of the paper1 of I.T. Horváth and J. Rábay on different fields of chemistry measured with the number of H.I.T-papers in each field. (*The 146 citations in the field of “Inorganic Chemistry” contain 96 references dealing with Fluorine Chemistry).

• The ingenious idea of biphasic catalysis awakened considerable interest also in the sector of physical chemistry. • A striking feature of the above statistics is the surprisingly high number of citations in the “leading” high impact journals. This can be interpreted that the discovery of the biphasic/fluorous catalysis was really a pioneering new result. • The citations from the fields of practical/applied chemistry (polymers, materials, pharma, medical, industrial) indicate that the discovery was also of considerable practical importance. • Citing publications from the field of coordination/ organometallic chemistry indicates intimate contact of the discovery with molecular (mostly transition metal based) catalysis.

Figure 3 gives a broad spectrum of journals publishing at least 10 citing papers. Figure 4 shows the impact of the paper1 on different sectors of chemistry. The previous figures lead us to the following remarks: • The statistics indicate that the discovery of fluorous/ biphasic catalysis has a very broad impact in almost all fields of chemistry. • Biphasic catalysis has the most practical (and mostly realized) importance in the field of organic preparative chemistry. • The relatively high number of citations from the catalysis literature indicates that this discovery importantly enriched the ideas of this sector. 8110


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Figure 5. H.I.T.-network.

Figure 6. Degree distribution of the network.

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• References from the field of green/environmental chemistry indicate contact with one of the hottest topics of present-day chemical research.

Let us continue with the specific network theory vocabulary used in this work as follows: We say that two nodes x and y are adjacent if they are linked by at least one edge xy. A network is said to be complete if any two nodes (of its node set V) are adjacent. Multiple edges are two or more edges that link the same nodes. A loop is an edge that links a node to itself. A path between x and y is a sequence of successive edges xv1, v1v2, ..., viy, where nodes x, v1, v2, ..., vi, y are distinct from one another. Definition 2. A clique of a network is a subset C of its node set V such that every two distinct nodes from C are adjacent. The node set C ⊂ V is a maximum clique of the network if there is no cliques with more nodes. The node set C ⊂ V is a maximal clique if it is not a subset of a larger clique. A maximum clique is therefore always maximal, but the converse does not hold. Nodes x and y are said to be connected (by path) if there exists at least one path between x and y in the network. Otherwise x and y are disconnected. The length of a path is the number of its edges. A network is said to be connected if every two nodes x and y are connected. Otherwise, we call it disconnected. A disconnected network is formed by

ISTVÁ N. T. HORVÁ TH’S NETWORK (H.I.T.-NETWORK) The representation and analysis of social networks showing scientific interactions between researchers has a great potential for scientists in all disciplines. First, we summarize some concepts of the social network analysis (SNA) or graph theoretical definitions (“network” and “graph” are interchangeable here) and fix the notations used throughout this paper. We follow notations and definitions given in a previous work.14 Definition 1. A network or a (finite undirected) graph denoted by G = (V, E) is represented by a set of nodes (or vertices) V and a set of edges (or links) E connecting some pairs of nodes. Social network analysis SNA is the mapping of relationships between people, groups, computers, and other connected entities (nodes). The edges are the relationships between these nodes. 8111


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Figure 7. Largest community found by Walktrap Algorithm (Horváth’s community).

Figure 8. Second largest community found by Walktrap Algorithm (Pozzi’s community).

connected components that are maximal (i.e., largest possible) subgraphs in which all nodes are connected to each other by paths. A bridge is an edge whose deletion increases the number of connected components. A node x is called an articulation point if its deletion increases the number of connected components. The degree of a node x (denoted by deg(x)) is the number of edges incident to x, where loops are counted twice. Definition 3. The average degree of a network with n nodes and e edges provides information about the number of edges compared to the number of nodes. Each edge is incident to two nodes and counts in the degree of both nodes; thus, the average degree of an undirected network is 2e/n. Definition 4. The degree distribution P(k) is the probability that the degree of a randomly chosen vertex is equal to k. Definition 5. A path is geodesic if its endpoints cannot be connected by a shorter path. The geodesic distance between

two nodes of a network is the length of the geodesic connecting the nodes. The eccentricity of a node (denoted by ϵ(x)) is the longest geodesic distance between x and any other node. The diameter d(G) of a network G = (V, E) is the maximum of all eccentricities, i.e., d = maxx∈Vϵ(x) . Definition 6. The betweenness centrality (or overall centrality or simply centrality) of a node x is CB(x) =

∑ s≠x≠t∈V

gst (x) gst

where gst is the total number geodesics between the nodes s and t and gst(x) is the number of geodesics connecting s and t that contain x. This notion was introduced by Linton C. Freeman in 1978.15 The terms of this sum are the probabilities that node x falls on a randomly selected geodesic connecting s and t for all different s, t ∈ V \ {x}. Nodes with high centrality have a large influence 8112


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Figure 9. Third largest community by Walktrap Algorithm (Curran’s community).

Figure 10. Fourth largest community by Walktrap Algorithm (Wang’s community).

Figure 11. Largest community by Fastgreedy Algorithm (Curran’s community). 8113


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ACS Sustainable Chemistry & Engineering on the transfer of information through the network, under the assumption that information transfer follows the shortest paths. Definition 7. The István T. Horváth’s network, simply H.I.T.-network, is a social network with a node set formed by all scientists who have at least one H.I.T.-paper. In this network, two scientists (nodes) are adjacent (i.e., linked by a single edge) if they have at least one common H.I.T.-paper. Edge xy does not mean that the corresponding common H.I.T.-paper or H.I.T.-papers have only two authors (x and y); in case of an edge xy, the possibility of other co-authors is not excluded. The István T. Horváth’s network is a kind of collaboration network of scientists influenced by the above-mentioned paper;1 thus, in this analysis, we considered only documents with co-authors. This means that 93 documents were left out (Figure 1). The remaining 984 documents have 2384 authors. There are no multiple edges in the graph. So, in the case of two co-authors, we cannot see the number of common works; they are linked only by a single edge. The network is simple (i.e., it does not contain loops or multiple edges), has 2384 nodes and 6146 edges, and is awe-inspiring at first sight (Figure 5). This network with an average degree of 5.156 was made by the “igraph” package, which is a library of the R programming language for network analysis.16 The degree distribution of the network illustrated in Figure 6 shows that most frequently three scientists work together and publish together their results. The “igraph” software calculated the highest betweenness centrality score (see Definition 6) of the researchers in the H.I.T.-network. The results are as follows: Pozzi, G. 71394; Curran, D.P. 63737, and Gladysz, J.A. 61233. In the past two decades, I.T. Horváth and his co-workers continued to take a leading part in exploitation of the potentialities of fluorous biphasic catalysis; for examples, see refs 3

and 17−20 and other papers, such as refs 21−26. One can identify Horváth’s growing interest toward the most actual problems of green chemistry.17,20

Table 1. Four Largest Betweenness Centrality Scores of the Largest Community Given by Fastgreedy Algorithm

Table 2. Four Largest Betweenness Centrality Scores of the Second Largest Community Given by Fastgreedy Algorithm

Centrality Degree

Curran, D. P.

Pozzi, G.

Kainz, S.

Leitner, W.

8142 41

6515 37

3464 11

3464 11

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MAIN COMMUNITIES OF THE NETWORK USING WALKTRAP ALGORITHM We used mathematical algorithms to derive communities in the above-mentioned social network. A network is said to have a community structure if the nodes of the network can be grouped into sets of nodes such that each set of nodes is internally densely connected. Overlapping communities are allowed. The more general definition is based on the principle that pairs of nodes are more likely to be connected if they are both members of the same community and less likely to be connected if they do not share communities.27 Articulation points make connection between communities. Two different mathematical algorithms were used in this work. One of them is called the “Walktrap Algorithm”.28 This is a stochastic algorithm based on random walks. The idea is that short random walks tend to stay in the same community. The Walktrap Algorithm found 317 communities in the network. The largest communitylet us call it Horváth’s communitycontains 159 authors, and it is shown in Figure 7. To reveal the closest relations between co-workers, one can search cliques in the graph. In this community, the maximum clique contains 11 scientists as follows: Molchanova, G., Artyushin, O., Sharova, E., Roschenthaler, G.V., Odinets, R., Mastryukova, T., Lyssenko, K., Kollar, L., Keglevich, G., Kegl, T., Goryunov, E. The above-mentioned 11 scientists are also coauthors of a paper. The maximum clique of this community is not the maximum clique of the H.I.T.-network (i.e., whole network). There is only one citing paper, which is published by 16 coauthors,29

Centrality Degree

Gladysz, J.A.

Rabai, J.

Buhlmann, P.

Soos, T.

5467 48

3308 42

2929 19

1624 19

Figure 12. Second largest community by Fastgreedy Algorithm (Gladysz’s community). 8114


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Figure 13. Third largest community by Fastgreedy Algorithm (Zhang’s community).

Figure 10 contains the fourth largest community by Walktrap Algorithm that contains 59 nodes. Wang, Y. H. (736), Jin, Z. L. (475), and Liu, C. (458) have the three highest betweenness centrality scores of this community, and here also the size of the maximum clique is 10. We are talking about authors Xiang, K. S., Chai, L. Y., Zhang, C., Yang, S., Yang, B. T., Xie, X. F., Min, X. B., Liu, Z. L., Liu, H., Liu, C.

Table 3. Four Largest Betweenness Centrality Scores of the Third Largest Community Given by Fastgreedy Algorithm Centrality Degree

Zhang, W.

Cai, C.

Geib, S.

Weber, S. G.

1172 17

1098 17

846 5

621 13

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that implies the existence of a clique with at least 16 members in the H.I.T.-network. Let us mention two maximal cliques in the Horvath’s community containing 10 scientists due to joint papers (the difference between maximal and maximum cliques is given by Definition 2: Meier, R., Gladysz, J.A., Soos, T., Rademacher, P., Nolan, S.P., LeStang, S., Kowski, K., Jiao, H.J., Jafarpour, L., Hamard, J.B. and Odinets, I., Artyushin, O., Roschenthaler, G.V., Mastryukova, T., Lyssenko, K., Kollar, L., Keglevich, G., Kegl, T., Goryunov, E., Fedyanin, I. Scientists with highest betweenness centrality are the most important since they assert more control over the network. Using the Walktrap Algorithm, the three highest betweenness centrality scores of the largest community are as follows: Horváth, I.T. (7033), Gladysz, J.A. (4660), Rábai, J. (3649). This means that the center of this community is Horváth, I.T. himself, indicating that Horváth’s work influences an unusually high number of scientists. Here, we must mention that Rábai, J. is the co-author of the analyzed paper.1 He is also a leading scientist of fluourous chemistry. The second largest community by Walktrap Algorithm contains 90 scientists and is shown in Figure 8. The maximum clique of this community contains 10 authors: Paige, D. R., Bhattacharyya, P., Wood, D. R. W., Stuart, A. M., Russell, D. R., Kemmitt, R. D. W., Hope, E. G., Gudmunsen, D., Fawcett, J., Croxtall, B. The three highest betweenness centrality scores of the second largest community are as follows: Pozzi, G. (1718), Hope, E. G. (1346), and Sinou, D. (1180). Figure 9 shows the third largest community by Walktrap Algorithm that contains 69 nodes. Curran, D. P. (1625), Matsubara, H. (800), and Arai, M. (627) have the three highest betweenness centrality scores of this community, and the size of the maximum clique is 10. We are talking about authors Wang, Q., Arai, M., Zhao, F. Y., Yu, Y. C., Xi, C. Y., Wu, C. Y., Ming, J., Liu, R. X., Cheng, H. Y., Cai, S. X.

MAIN COMMUNITIES OF THE NETWORK USING FASTGREEDY ALGORITHM We also used another algorithm what is called ”Fastgreedy”. This is a deterministic algorithm that follows the heuristic problem-solving process of making the locally optimal choice at each stage with the hope of finding a global optimum.30 Fastgreedy Algorithm has found 327 communities; the largest here contains 168 scientists and is illustrated in Figure 11. Table 1 highlights the four highest betweenness centrality scores (from this community) in the first row together with the corresponding node degrees in the second row. The Fastgreedy Algorithm provides that the maximum clique in the largest community has 10 members: Arai, M., Cai, S. X., Zhao, F. Y., Yu, Y. C., Xi, C. Y., Wu, C. Y., Wang, Q., Ming, J., Liu, R. X., Cheng, H. Y. The second one consists of nine members: Choplin, A., Busch, S., Sinou, D., Quignard, F., Pozzi, G., Leitner, W., Koch, D., Kling, R. and Kainz, S. The second largest community by Fastgreedy Algorithm contains 135 researchers and is shown in Figure 12. Table 2 contains the four highest betweenness centrality scores of the community (first row) together with the corresponding node degrees (second row). The Fastgreedy Algorithm provides that the maximum clique in the second largest community has 10 members: Jafarpour, L., Gladysz, J. A., Soos, T., Rademacher, P., Nolan, S. P., Meier, R., LeStang, S., Kowski, K., Jiao, H. J., Hamard, J. B. As was strikingly visible also from the statistical analysis, the invention of the biphasic fluorous systems induced many new ideas in different sectors of chemical sciences. We demonstrate this tendency by citing a few characteristic references from three leading scientists in the field, who showed also high betweenness centrality scores in Table 1 (Dennis P. Curran2,31−40 and Gianluca Pozzi41−50) and in Table 2 (John A. Gladysz51−60). 8115


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Figure 14. Fourth largest community by Fastgreedy Algorithm (Horváth’s community).

of the analyzed paper1 (István T. Horváth and József Rábai) obtained an ingenious tool for resolving a technical problem in transition metal catalysis (catalyst separation from product). They had, however, enough expertise to recognize that their finding was not an “ad hoc” technical tool but that it had a much more general significance for preparative chemistry. They started immediately to explore the possibilities, which were “beyond” the original goal. The beauty of the original idea and the richness of the consequences awakened very soon the attention also of other groups, and this led to a respectable number of citations on one side, together with the fireworks of new results in these citing papers.

One can identify the main purposes of these outstanding scientists from these references, as the following: • Finding new catalytically active systems under fluorous conditions • Synthesizing and characterizing new compounds which could be useful catalysts or auxiliaries in fluorous chemistry • Finding new (catalytic) reaction principles which are enabled only under fluorous conditions Interactions between groups are marked also by joint communications.2,61 Figure 13 shows the third largest community by Fastgreedy Algorithm which contains 66 nodes. Table 3 shows the main betweenness centralities and their their corresponding node degrees. The Fastgreedy Algorithm provides that the maximum clique in the third largest community has seven members: Cai, C., Farran, A., Xu, Y. M., Sandoval, M., Liu, J., Linhardt, R. J., Hernaiz, M. J. The fourth largest community by Fastgreedy Algorithm contains 65 researchers and is shown in Figure 14. Table 4 contains the main betweenness centralities with their corresponding node degrees.

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*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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DEDICATION This paper is dedicated to Prof. Dr. István T. Horváth on the occasion of his 65th birthday. We introduce him as one of the pioneers of green chemistry and as a good friend of one of the authors (G.P.) for several years.

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Table 4. Four Largest Betweenness Centrality Scores of the Fourth Largest Community Given by Fastgreedy Algorithm Centrality Degree

Horváth, I.T.

Mika, L.T.

Kollar, L.

Hughes, R.P.

1322 29

957 17

735 16

502 15

AUTHOR INFORMATION

Corresponding Author

ACKNOWLEDGMENTS The research of R. Molontay was supported by MTA-BME Stochastics Research Group.

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The Fastgreedy Algorithm provides that the maximum clique in the fourth largest community has 11 members: Kegl, T., Artyushin, O., Sharova, E., Roschenthaler, G. V., Odinets, R., Molchanova, G., Mastryukova, T., Lyssenko, K., Kollar, L., Keglevich, G., Goryunov, E.

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CONCLUDING REMARKS The history of fluorous biphasic chemistry is a typical example of that feature of scientific research which makes impossible the “planning” of real discoveries. In the present case, the authors 8116


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