Bibliographic record
Abstract
The preceding chapters developed the basic principles needed to describe selforganization and self-assembly in a variety of systems in either initially prepared unstable and metastable states or far-from-equilibrium states. The underlying mesoscopic description involved order parameter fields whose evolution was given in terms of either free energy functionals or amplitude equations. The latter approach is used for systems for which the free-energy-based description is not applicable. However, in many applications to physical and biological problems there is no clear-cut distinction between these two approaches. Often physical systems operate far from equilibrium, and the dynamics may involve both a free energy functional component and a component that cannot be expressed in this form. In this and the following chapters we describe several applications that illustrate how the methods developed in the body of the book may be used to construct models that capture the important aspects of the dynamics. We begin with a discussion of laser-induced melting in this chapter. In the following chapter we consider reactive physical and biological systems where phase segregation and reaction–diffusion dynamics are combined. The last chapter considers active materials where the constituent elements undergo driven or self-propelled motion. The analysis involves the combination of liquid crystal free energy formulations with order parameter dynamics to account for the active motion. Laser-induced melting When a laser with an appropriate intensity is focused onto a solid semiconductor film it can create a variety of ordered and disordered lamellar patterns of coexisting solid and melt regions (Fig. 29.1). The periodicity of the ordered patterns is commensurate with the wavelength of the incident laser radiation (van Driel et al ., 1982).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".