“Hearing Loss” in QCM Measurement of Protein Adsorption to Protein Resistant Polymer Brush Layers
Bibliographic record
Abstract
Accurate quantification of nonspecific protein adsorption on biomaterial surfaces is essential for evaluation of their antifouling properties. The quartz crystal microbalance (QCM) is an acoustic sensor widely used for the measurement of protein adsorption. However, although the QCM is highly sensitive, it does have performance limitations when working with surfaces modified with thick viscous layers. In the case of polymer brush surfaces, factors such as the thickness and viscosity of the brush may bring such limitations. In the present work, three types of antifouling molecules were used to explore the applicability of QCM for the evaluation of the protein resistance of hydrophilic polymer brush surfaces. Adsorption was also measured by surface plasmon resonance (SPR) as a reference. It was shown that the detection of adsorbed protein requires that protein be located within a critical distance from the QCM chip surface, determined by the viscosity of polymer brush. For larger proteins like fibrinogen, adsorption is expected to occur mainly "on top" of the polymer brush, and brush thickness determines whether protein is located in the "detectable zone". For smaller proteins like lysozyme, adsorption is expected to occur mainly at the chip surface and within the polymer brush layer and to be detectable by QCM. However, the quantity of adsorbed lysozyme may be underestimated when secondary adsorption also occurred. It is concluded that QCM data suggesting very low protein adsorption on polymer brush surfaces should take account of these considerations and should be treated generally with caution.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".