Surface Tensions: Challenges to Philosophy of Science from Nanoscience

January 31, 2013

Julia Bursten

Abstract: A traditional view of the structure of scientific theories, on which philosophers of science have based their accounts of explanation, modeling, and inter-theory relations, holds that scientific theories are composed of universal natural laws coupled with initial and boundary conditions. In this picture, universal laws play the most significant role in scientific reasoning. Initial and boundary conditions are rarely differentiated and their role in reasoning is largely overlooked. In this talk, I use the problem of modeling surfaces in nanoscience to show why this dismissal is deeply problematic both for philosophers of science and for scientists themselves.

In macroscopic-scale modeling, surfaces are treated as boundaries in the mathematical sense-that is, as infinitesimally thin borders of a system that confine its interior. As such, surface structure and behavior is usually modeled in an idealized manner that ignores most of the physics and chemistry occurring there. At the nanoscale, however, the structure and behavior of these surfaces significantly constrains the structure and behavior of the interior in more complex ways. Three important conclusions emerge:

1. The very concept surface changes as a function of scale, and other central concepts in nanoscience also behave in this scale-dependent manner.
2. The traditional view of theory described above does not adequately capture the nature of nanomaterials modeling, which requires attention to multiple models constructed at different characteristic scales. These component models do not comport well with a single set of universal laws, as the standard view suggests. Instead, boundary behaviors become crucial and models are designed to capture these behaviors.
3. The projects of nanomaterials modeling and synthesis dictate that divisions between boundaries and interiors must be continually adjusted. Overlooking this problem has led to failures of experimental design and interpretation of data.


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