Electrochemical Quartz Crystal Nanobalance Study of the Adsorption/Displacement Phenomena of Proteins and Lipids on Pt
Bibliographic record
Abstract
The electrochemical quartz crystal nanobalance (EQCN) was used to measure the adsorption behavior of a series of lipids (stearate, oleate, linoleate, and gamma-linolenate) on a Pt surface from a phosphate buffer pH 7.0 solution at 295 K and to investigate their adsorption/displacement behavior with the proteins, beta-lactoglobulin and alpha-lactalbumin, which are known to cause fouling during milk processing. The EQCN technique and the complementary technique of cyclic voltammetry measured simultaneously provided information on the efficiency of solubilization of the proteins by these lipids. Excellent agreement was obtained for the surface concentration of adsorbed lipid from the surface charge density from cyclic voltammetry measurements and the change in mass from the EQCN frequency measurements. The Gibbs energy of adsorption showed the lipids to have a strong affinity for the platinum surface. Addition of protein to a preadsorbed lipid layer showed alpha-lactalbumin to be able to coadsorb with the lipids, while beta-lactoglobulin was able to desorb some of the unsaturated lipids but appeared to coadsorb with the saturated lipid, stearate. Addition of lipid to a preadsorbed protein layer showed the unsaturated lipids to be able to displace some of the protein. A comparison of the desorption ability of the lipids showed stearate to be very inefficient at removing protein, while the other three lipids were able to remove each of the proteins, with the order of efficiency for protein desorption being oleate > linoleate > gamma-linolenate.
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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 | 0.000 | 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 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".