Sorption and diffusion of gold and silver nanoparticles in solution through nitrile rubber membrane
Bibliographic record
Abstract
ABSTRACT Mechanical and physical properties of the rubber material may be affected by swelling when brought into contact with solutions of engineered nanoparticles (ENP). As the rubber swells in the liquid carrier of the ENP, the polymeric chains of the network expand and the ENP can penetrate the structure being carried by the diffusion of the liquid. The aim of this work is to assess the influence of ENP and evaluate the effect of additives present in the solutions on the diffusion process through a rubbery structure. Swelling of membrane material specimens was evaluated by measuring mass gain and liquid diffusion was then deduced. The present study focuses on the contact of nitrile rubber membranes with commercial gold ENP (5 and 50 nm in diameter) and silver ENP (50 nm) in MilliQ water. Swelling tests were also conducted with MilliQ water and filtrates (the solutions from which the ENP were extracted). Results show that the diffusion coefficients of all the solutions of ENP are slightly different and are around 1.2 × 10−10 cm2 s−1. However, it should be noted that these coefficients are notably higher for the filtrates and reach 2.4 × 10−10 cm2 s−1 for the filtrate of the silver ENP. This result underscores the effect of the ENP on the liquid penetration process. We also found that the ENP has a noticeable effect on the Fickian diffusion mechanism of the penetrant; it was noticed that the presence of these nanoparticles lowers the diffusion mechanism index. Moreover, the size of the nanoparticles was found to have an impact on the diffusion coefficient of the solutions as well as their solubility. These findings help to better understand the diffusion phenomenon of the ENP through nitrile membrane materials. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45350.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".