Interaction of Pseudomonas aeruginosa with surface-modified silica studied by ultra-high frequency acoustic wave biosensor
Bibliographic record
Abstract
Aim: This study aimed to examine the amount of surface non-specific adsorption, or fouling, observed by Pseudomonas aeruginosa (P. aeruginosa) on a quartz crystal based acoustic wave biosensor under different flow conditions with and without an anti-fouling layer. Methods: An electromagnetic piezoelectric acoustic sensor (EMPAS) based on electrode free quartz crystals was used to perform the analysis. Phosphate buffered saline (PBS) was flowed over the crystal surface at various flow rates from 50 μL/min to 200 μL/min, with measurements being taken at the 43rd harmonic (~864 MHz). The crystal was either unmodified, or modified with a monoethylene glycol [2-(3-silylpropyloxy)-hydroxy-ethyl (MEG-OH)] anti-fouling layer. Overnight culture of P. aeruginosa PAO1 (PAO1) in lysogeny broth (LB) was injected into the system, and flow maintained for 30 min. Results: The frequency change of the EMPAS crystal after injection of bacteria into the system was found to change based on the flow rate of buffer, suggesting the flow rate has a strong effect on the level of non-specific adsorption. The MEG-OH layer drastically reduced the level of fouling observed under all flow conditions, as well as reduced the amount of variation between experiments. Flow rates of 150 μL/min or higher were found to best reduce the level of fouling observed as well as experimental variation. Conclusions: The MEG-OH anti-fouling layer is important for accurate and reproducible biosensing measurements due to the reduced fouling and variation during experiments. Additionally, a flow rate of 150 μL/min may prove better for measurement compared to the current standard of 50 μL/min for this type of instrument.
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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 | 0.001 |
| 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".