Bibliographic record
Abstract
Growth of order from disorder is a natural phenomenon which is seen in a variety of systems. An important class of such phenomena involves the kinetics of phase ordering and phase separation. The examples of such growth processes that were described in Chapter 2 had common characteristics. Now, we discuss the experimental results that point to common features of the kinetics of phase separation processes. A combination of techniques from nonequilibrium statistical mechanics and nonlinear dynamics is used to study the formation and evolution of spatial structures. Substantial progress in our understanding of the kinetics of domain growth during a first-order phase transition has been made over the past few decades. The knowledge gained in these studies forms the underpinning of the descriptions of many such processes which create order from disorder. Kinetics of phase ordering and phase separation Phase separation is usually initiated by a rapid change or quench in a thermodynamic variable (often temperature and sometimes pressure), which places a disordered system in a post-quench initial nonequilibrium state. The system then evolves towards an inhomogeneous ordered state of coexisting phases, which is its final equilibrium state. Depending on the nature of the quench, the post-quench state may be either thermodynamically unstable or metastable (see Fig. 2.1). In the former case, the onset of separation is spontaneous, and the kinetics that follows is known as spinodal decomposition. For the metastable case, nonlinear fluctuations are needed to initiate the separation process. The system is said to undergo phase separation through homogeneous nucleation if the system is pure. Phase separation occurs by heterogeneous nucleation if the system has impurities or surfaces which initiate nucleation events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".