Abstract
In Parts I and II, the scientific breakthrough of ammonia synthesis was described in terms of a confluence of factors leading to an arena of research mature enough for advancement—it is a dynamic encapsulated in the concept of The Haze.
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In Parts I and II, the scientific breakthrough of ammonia synthesis was described in terms of a confluence of factors leading to an arena of research mature enough for advancement—it is a dynamic encapsulated in the concept of The Haze (Schlögl 2018).Footnote 1 In this particular arena, scientists determined the conditions under which ammonia can be synthesized. The upscaling of the Haber-Bosch process at BASF followed, along with its proliferation and secondary effects, such as the establishment of the high pressure catalytic industry.
Here in Part III, we go into theoretical detail on the structure and dynamic of the Haze. It is a “manual” approach, which can be viewed as complementary to computer-based historical methods and studies that analyze the dynamics of larger or global groups (Fangerau 2010; Graßhoff 1994, 1998, 2003; Graßhoff and May 1995; Renn 2012; Wendt 2016; Wintergrün 2019). In particular, I wish to discuss the nature of the individual interactions leading to knowledge exchange in science and how this activity enables scientific progress by simultaneously exploiting several paradigms. The significance of these events, often without a plan or strategy, may only be recognized by the small number of people involved, if at all. While these interactions do not influence the outcome of scientific research, they are fundamental to the fact that science works at all and their random, stochastic nature succinctly explains the complexity of the Haze. The different approaches to investigating the outcome of many such interactions focus on either large or small groups of actors and share a reliance on case studies. While the reactions to this strategy are varied (Schumpeter 1942, p. 83), (Rudwick 1985, pp. xxii, 15–16), (Basalla 1988, p. 30), (Siggelkow 2007), in many examples, case studies have been shown to have great illustrative power (Fangerau 2010; Fleck 1980; Globe et al. 1973; Holmes 1985; Padgett and Ansell 1993; Padgett and McLean 2006; Padgett and Powell 2012b; Rudwick 1985; Sanderson and Uzumeri 1995; Sgourev 2013, 2015), (Obstfeld 2017, Chapters 4, 5). The case study of ammonia synthesis, too, offers more than only a recounting of events; generalities of science can also be considered. The Haze is a complex phenomenon and the relatively clear-cut nature of the breakthrough of ammonia synthesis suggests a simplified schematic with which we can discuss the revealing of new knowledge in science and its movement into technical and commercial domains.
For practical reasons, continuity, and an attempt at a common lexicon, I have used terminology from social network theory and innovation literature to generalize the dynamics of change in science. Researchers in these fields have had success illustrating the mechanisms of innovation in diverse disciplines but application to purely scientific episodes is rare. In employing their tools, I wish not only to stretch the concepts to cover scientific breakthroughs but also to enable the description of the transfer of knowledge resulting from basic research. There is no attempt, however, to develop these theories further. I also make use of terminology from philosophical perspectives on science and technology from throughout the twentieth century while trying to remain concrete about the mechanisms of progress in the mature, exact natural sciences as they exist today.
The transfer of terminology is not without consequence: the approach uncovers facets of scientific activity that differ from other processes of innovation and in doing so, provides vivid examples of how science progresses toward a discovery. This difference is not the result of the primacy of science or scientific progress over, say, technological progress; technology provides the novel instrumentation and techniques to meet the needs of ever more sophisticated scientific research (Brooks 1994). Science and technology are intimately linked and dependent on one another (and are better off for it) (Basalla 1988, pp. 27–28), (Rogers 1995, pp. 140–141), (Bonvillian 2014). It is rather that the discussion here centers on science because literature on scientific progress remains sparse and because science is my area of expertise. This description is not of the “scientific method,” which is essentially based on honesty of thought, the forthright presentation of results, and the acceptance only of testable ideas which may be disproved. Rather, it is an examination of scientific research as an activity within the framework of existing theories of change that gives tangible descriptions of its driving mechanisms.
Within the terminology of social network and innovation literature, there is widespread use of physical, chemical, biological, etc. (in short: natural scientific) concepts and terms to imply analogy between processes. My initial reaction was to reject such usage. One example that is critical within the context of physical chemistry is the term “catalyst” used in the sense that networks and/or novel combinations of knowledge can have an outsized impact. While this description embodies some of the properties of a catalyst, it should be compared to the definition given by Ostwald in 1902: “A catalyst is any substance that, without having an effect on the end product of a chemical reaction, changes its speed [reaction rates] (Ostwald 1902).”Footnote 2 While a catalyst often has an oversized effect compared to the quantity of material present, it is not required to behave this way; it is only required to change the reaction rates, often suppressing specific reactions while favoring others. The general mathematical term for this kind of behavior is “non-linear” (Quinn 1985).Footnote 3 Such mathematically-based scientific terms are strictly defined for exact application to experimental and theoretical work, and there is no tolerance for creative reinterpretation. As much as possible, there is no subjectivity left in the term. If something is missing, if a new term is needed, it may be derived and characterized. The task of defining terminology in the most unambiguous way possible has long been considered vital to the field, although the meanings of scientific terms can develop over time as their exactitude (hopefully) grows (Popper 1935, p. 11), (Rudwick 1985, pp. 401, 446–448). As I ventured into the theories of change and innovation, however, the power of the analogies became apparent along with the recognition that they had been used with much care (Basalla 1988; Brooks 1994; Langrish 2017; Laubichler and Renn 2015; Murmann and Frenken 2006; Obstfeld 2017; Padgett 2012a; Padgett and McLean 2006; Sgourev 2015; Ziman 2000). I became so convinced of their practicality, in fact, that I have used them myself in describing the Haze. For maximum benefit, the use of descriptive terms from other fields should be accompanied by a concerted effort to understand their origin and original meaning.
It will be no surprise that I have received pushback on my own use of certain terms. One example is the distinction between discovery and innovation. Another is the use of the word “science” as if it were well-understood to mean physics, chemistry, biology, geology or any of the natural or “hard” sciences. It is not clear to everyone, however. Yet continuously attaching a prefix is cumbersome so that after this declaration, I have decided to leave it off in the follow discussion.
Notes
- 1.
The concept of the Haze was developed with significant input from Prof. Dr. Robert Schlögl at the Fritz Haber Institute of the Max Planck Society. He also suggested the term “Haze”.
- 2.
“Ein Katalysator ist jeder Stoff, der, ohne im Endprodukt einer chemischen Reaktion zu erscheinen, ihre Geschwindigkeit verändert.”
- 3.
What is meant is “non-linear” with the exponent greater than 1.
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Johnson, B. (2022). Terminology. In: Making Ammonia. Springer, Cham. https://doi.org/10.1007/978-3-030-85532-1_15
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