All-Inclusive Sensing Tablet with Integrated Passive Mixer for Ultraviscous Solutions
Bibliographic record
Abstract
Developing low-cost and easy-to-use point-of-care devices is necessary for timely disease diagnosis and health monitoring. Here, we introduce all-inclusive, tablet-based chemo/biosensors with rapid automixing features, capable of mixing in highly viscous solutions with viscosities up to 1700 mPa·s. These tablets are created using a simple powder compression method and contain all necessary reagents to perform assays in a "drop-and-detect" manner, without the need for vigorous shaking or vortex mixing. As proof of concept, we demonstrated the applicability of our Speedy tablets for detecting nitrite in human saliva, a challenging medium due to its viscosity. The strong mixing capability of the proposed tablets ensured consistent and reliable results across range of viscosities, from low to high, while delivering an excellent detection range of 0.03-1.50 mg/dL, covering nitrite levels in human saliva. Additionally, we developed a straightforward method to encapsulate enzymes in trehalose, making them bulkier and more stable using only a mist sprayer, nonstick tray, and spatula, eliminating the need for expensive equipment. This approach allowed us to incorporate small amounts of enzymes into tablet formulations and fabricate the first automixing tablet biosensor. These biosensors were used for the bienzymatic detection of glucose in real human urine within the biologically relevant range of 0.3-2.5 mM, indicating the compatibility of automixing tablets with bioreagents. Each tablet costs less than $0.30 to produce and remains stable for at least one month at room temperature. The affordability and convenience of our tablets make them a valuable addition to the array of diagnostic tools.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".