Understanding the role of Reynolds number on self-assembly formation of nanoparticles
Bibliographic record
Abstract
Abstract Self-assembly formation of nanoparticles (NPs) is a central theme in nanotechnology that has garnered significant attention in both academia and industry. The size of self-assembled NPs plays crucial role in their physicochemical properties. It has long been known that when macromolecules in a solvent being exposed to an anti-solvent, they can spontaneously form NPs of varying sizes through static diffusion (mixing rate = 0) and dynamic convection (mixing rate > 0). However, the impact of solvent mixing rate on the size of self-assembled NPs remains a mystery. Here, for the first time, we mathematically and experimentally prove that Reynolds number (Re), which quantifies fluid turbulence, is decisive on the self-assembly formation of NPs, by exponentially influencing the way kinetic energy of precursor molecules converting into surface energy of subsequent NPs. We surprisingly find that various self-assembly systems share a very similar critical value of Re about 1,000 ~ 1,200, which predetermines whether self-assembly occurs following a low or high energy dissipation pathway. This new framework further enables us to quantitatively determine energy conversion efficiency of a self-assembly process, measure surface tension of NP in a complex system, and predict NP size at arbitrary Re (including 0), which cannot be achieved in the past due to the limits of technology.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".