Adsorption study on the formation of interfacial layers based on birch glucuronoxylans
Bibliographic record
Abstract
Glucuronoxylans (GX), particularly crude fractions obtained by pressurized hot water extraction of birch wood, act as potent emulsifiers and stabilizers against physical separation and lipid oxidation. Herein, we studied the adsorption of GX on hydrophobic interfaces to correlate their multicomponent character towards the formation of interfacial layers in emulsions. Dynamic interfacial tension (DIFT) and quartz crystal microgravimetry with dissipation monitoring (QCM-D) were applied to various GX fractions and the results compared with those from cellulose-based emulsifiers. The roles of residual lignin and polysaccharides are discussed considering the formation of interfacial layers during emulsification. The DIFT of the different GXs reached quasi-equilibrium faster as the lignin concentration increased, implying a correlation between the rate of adsorption and the residual lignin content. The effect of NaCl addition was more pronounced in polysaccharide-rich fractions, indicating that the polysaccharide fraction modulated the effect of ionic strength. QCM-D showed that despite the fast adsorption exhibited by the lignin-rich GX extract in the DIFT curves, the adsorbed materials were lightweight, suggesting that the polysaccharide fraction built the bulk of the interfacial layer. These results provide a foundation towards understanding the role of GX in interfacial stabilization beyond traditional plant-based counterparts.
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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".