Effect of <scp>hexyl‐branched</scp> backbone size on the size distribution and coalescence of free volume around an amphiphilic molecule
Bibliographic record
Abstract
Abstract We used molecular dynamics simulation to study the size distribution and coalescence of free volume around an amphiphilic molecule (nonyl ethoxylate (NE)) at a concentration of 0.5 wt% in blends of linear and branched polyethylene that contained a small four‐arm alkane (7,12 hexyl octadecane). The branched polyethylene chains had 10 and 82 hexyl branches/1000 backbone carbons. The coalescence dynamics (fluctuation of free volume in time) was quantified by the number of Fourier frequencies and the corresponding power (amplitude). In our previous work, we hypothesized that cavitation, the first step of environmental stress cracking observed experimentally, starts from the free volume coalescence at the interface between NE and polyethylene molecules and showed that hexyl branches in the branched polyethylene molecules suppress such coalescence, suggesting that cavitation, a much longer time scale process, could also be slowed down. The current work showed that the behavior of hexyl branches in branched polyethylene in a blend with linear polyethylene was similar to that in pure branched polyethylene, but that the hexyl branches in the 7,12 hexyl octadecane exhibited the opposite behavior. In particular, they intensified free volume coalescence, especially around the hydrophilic ethylene oxide segments of NE. The addition of 7,12 hexyl octadecane to branched polyethylene alone or blended with linear polyethylene does not seem to slow down free volume coalescence (cavitation), leading us to conclude that the effect of hexyl branches on the free volume coalescence around NE depends on the size of the backbone to which the branches are attached. Highlights Hexyl branches reduce free volume coalescence activities. The longer the backbone is, the stronger the hexyl branch effect. More coalescence activities occur on the hydrophilic segment of NE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".