Size-Controllable and pH-Sensitive Whey Protein Microgels as High-Performance Aqueous Biolubricants
Bibliographic record
Abstract
Developing efficient aqueous biolubricants has become a significant focus of research due to their prevalence in biotribological contacts and enormous potential in soft matter applications. In this study, size-controllable, pH-sensitive whey protein microgels were prepared using a water-in-water emulsion template method from protein-polysaccharide phase separation. The granular hydrogel from the protein microgels exhibited superior lubricity, obtaining 2.7-fold and 1.7-fold reductions in coefficient of friction (μ) compared to native protein and human saliva (μ = 0.30 compared to 0.81 and 0.52, respectively). The microgels also exhibited outstanding load-bearing capabilities, sustaining lubrication under normal forces up to 5 N. Microgels with a smaller size (1 μm) demonstrated better lubricating performance than 6 and 20 μm microgels. The exceptional lubricity was from a synergistic effect of the ball-bearing mechanism and the hydration state of the microgels. Particularly at pH 7.4, the hydration layer surrounding highly negative charges contributed to the electrostatic repulsion among the swollen microgels, leading to an improved buffer ability to separate contact surfaces and effective rolling behavior. Such pH-dependent repulsion was evidenced using a surface forces apparatus that the adhesion between the whey protein-coated surfaces and protein-mica surfaces decreased from 4.49 to 0.97 mN/m and from 7.89 to 0.36 mN/m, respectively, with pH increasing from the isoelectronic point to 7.4. Our findings fundamentally elucidated the tribo-rheological properties and lubrication mechanisms of the whey protein microgels with excellent biocompatibility and environmental responsiveness, offering novel insights for their food and biomedical applications requiring aqueous biolubrication.
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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) | 0.000 | 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".