A microfluidic platform with pH imaging for chemical and hydrodynamic stimulation of intact oral biofilms
Bibliographic record
Abstract
A microfluidic platform with a fluorescent nanoparticle-based sensor is demonstrated for real-time, ratiometric pH imaging of biofilms. Sensing is accomplished by a thin patterned layer of covalently bonded Ag@SiO2+FiTC nanoparticles on an embedded planar glass substrate. The system is designed to be sensitive, responsive and give sufficient spatial resolution to enable new micro-scale studies of the dynamic response of oral biofilms to well-controlled chemical and hydrodynamic stimulation. Performance under challenging operational conditions is demonstrated, which include long-duration exposure to sheer stresses, photoexcitation and pH sensor biofouling. After comprehensive validation, the device was used to monitor pH changes at the attachment surface of a biofilm of the oral bacteria, Streptococcus salivarius. By controlling flow and chemical concentration conditions in the microchannel, biochemical and mass transport contributions to the Stephan curve could be probed individually. This opens the way for the analysis of separate contributions to dental caries due to localized acidification directly at the biofilm tooth interface.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".