A microfluidic method and custom model for continuous, non-intrusive biofilm viscosity measurements under different nutrient conditions
Bibliographic record
Abstract
Straight, low-aspect ratio micro flow cells are used to support biofilm attachment and preferential accumulation at the short side-wall, which progressively reduces the effective channel width. The biofilm shifts downstream at measurable velocities under the imposed force from the constant laminar co-flowing nutrient stream. The dynamic behaviour of the biofilm viscosity is modeled semi-analytically, based on experimental measurements of biofilm dimensions and velocity as inputs. The technique advances the study of biofilm mechanical properties by strongly limiting biases related to non-Newtonian biofilm properties (e.g., shear dependent viscosity) with excellent time resolution. To demonstrate the proof of principle, young Pseudomonas sp. biofilms were analyzed under different nutrient concentrations and constant micro-flow conditions. The striking results show that large initial differences in biofilm viscosities grown under different nutrient concentrations become nearly identical in less than one day, followed by a continuous thickening process. The technique verifies that in 50 h from inoculation to early maturation stages, biofilm viscosity could grow by over 2 orders of magnitude. The approach opens the way for detailed studies of mechanical properties under a wide variety of physiochemical conditions, such as ionic strength, temperature, and shear stress.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".