Sequential Reagent Release from a Layered Tablet for Multistep Diagnostic Assays
Bibliographic record
Abstract
Diagnostic assays are commonly performed in multiple steps, where reagents are added at specific times and concentrations into a reaction chamber. The reagents require storage, preparation, and addition in the correct sequence and amount. These steps rely on trained technicians and instrumentation to perform each task. The reliance on such resources hinders the use of these diagnostic assays by lay users. We developed a tablet that can sequentially introduce prequantified lyophilized diagnostic reagents at specific time points for a multistep assay. We designed the tablet to have multiple layers using cellulose-grade polymers, such as microcrystalline cellulose and hydroxypropyl cellulose. Our formulation allows each layer to dissolve at a controlled rate to introduce reagents into the solution sequentially. The release rate is controlled by modulating the compression force or chemical formulation of the layer. Controlling the reagent release time is important because different assays have specific times when reagents need to be added. As proof of concept, we demonstrated two different assays with our tablet system. Our tablet detected nucleic acid target ( tpp47 gene from Treponema pallidum ) and nitrite ions in an aqueous sample without user intervention. Our multilayer tablets can simplify multistep assay processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".