Probing the Interaction between Nanoparticles and Lipid Membranes by Quartz Crystal Microbalance with Dissipation Monitoring
Bibliographic record
Abstract
There is increasing interest in using quartz crystal microbalance with dissipation monitoring (QCM-D) to investigate the interaction of nanoparticles (NPs) with model surfaces. The high sensitivity, ease of use and the ability to monitor interactions in real-time has made it a popular technique for colloid chemists, biologists, bioengineers, and biophysicists. QCM-D has been recently used to probe the interaction of NPs with supported lipid bilayers (SLBs) as model cell membranes. The interaction of NPs with SLBs is highly influenced by the quality of the lipid bilayers. Unlike many surface sensitive techniques, by using QCM-D, the quality of SLBs can be assessed in real-time, hence QCM-D studies on SLB-NP interactions are less prone to the artifacts arising from bilayers that are not well formed. The ease of use and commercial availability of a wide range of sensor surfaces also have made QCM-D a versatile tool for studying NP interactions with lipid bilayers. In this review, we summarize the state-of-the-art on QCM-D based techniques for probing the interactions of NPs with lipid bilayers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".