The effect of hydroxyl functional groups and molar mass on the viscosity of non-crystalline organic and organic–water particles
Bibliographic record
Abstract
Abstract. The viscosities of three polyols and three saccharides, all in the non-crystalline state, have been studied. Two of the polyols (2-methyl-1,4-butanediol and 1,2,3-butanetriol) were studied under dry conditions, the third (1,2,3,4-butanetetrol) was studied as a function of relative humidity (RH), including under dry conditions, and the saccharides (glucose, raffinose, and maltohexaose) were studied as a function of RH. The mean viscosities of the polyols under dry conditions range from 1.5 × 10−1 to 3.7 × 101 Pa s, with the highest viscosity being that of the tetrol. Using a combination of data determined experimentally here and literature data for alkanes, alcohols, and polyols with a C3 to C6 carbon backbone, we show (1) there is a near-linear relationship between log10 (viscosity) and the number of hydroxyl groups in the molecule, (2) that on average the addition of one OH group increases the viscosity by a factor of approximately 22 to 45, (3) the sensitivity of viscosity to the addition of one OH group is not a strong function of the number of OH functional groups already present in the molecule up to three OH groups, and (4) higher sensitivities are observed when the molecule has more than three OH groups. Viscosities reported here for 1,2,3,4-butanetetrol particles are lower than previously reported measurements using aerosol optical tweezers, and additional studies are required to resolve these discrepancies. For saccharide particles at 30 % RH, viscosity increases by approximately 2–5 orders of magnitude as molar mass increases from 180 to 342 g mol−1, and at 80 % RH, viscosity increases by approximately 4–5 orders of magnitude as molar mass increases from 180 to 991 g mol−1. These results suggest oligomerization of highly oxidized compounds in atmospheric secondary organic aerosol (SOA) could lead to large increases in viscosity, and may be at least partially responsible for the high viscosities observed in some SOA. Finally, two quantitative structure–property relationship models (Sastri and Rao, 1992; Marrero-Morejón and Pardillo-Fontdevila, 2000) were used to predict the viscosity of alkanes, alcohols, and polyols with a C3–C6 carbon backbone. Both models show reasonably good agreement with measured viscosities for the alkanes, alcohols, and polyols studied here except for the case of a hexol, the viscosity of which is underpredicted by 1–3 orders of magnitude by each of the models.
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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 | 0.000 |
| 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".