Interfacial Dynamics in the Fabrication of Various Concave Hydrogel Discs for Enhanced Biosensing
Bibliographic record
Abstract
Hydrogel-based biosensors are commonly used in diagnostic applications. However, their performance remains constrained by slow analyte diffusion within polymer matrices, particularly when larger biomolecules are involved. Concave hydrogel geometries present a promising solution to enhance diffusion rates through increased surface area. However, the interfacial dynamics governing their formation must be studied. In this research, we investigated the interfacial dynamics that influence the formation of concave hydrogel discs fabricated by a simple pipetting method. We characterized the fluid interactions occurring during droplet deposition of alginate and CaCl2 solutions. A three-phase flow model incorporating confocal microscopy validation was employed to simulate time-dependent interfacial behaviors. Concave hydrogel discs fabricated with alginate-first deposition exhibited 83% larger surface area compared to hemispherical counterparts at a CaCl2: alginate volume ratio of one. Increasing the volume ratio further enhanced both surface area and diameter, though this highlighted limitations for microscopy-based detection. According to our results, reaction speed in alginate concave hydrogel discs can be controlled by varying the volume of CaCl2 solution while keeping the volume of alginate solution constant, which changes the surface area while maintaining constant hydrogel volume.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".