Reactive Inkjet Printed Silk Stirrers for Rapid Medical Diagnosis
Bibliographic record
Abstract
Medical diagnostic kits play a vital role in the quick and precise identification of diseases; however, their test times are often limited by the efficiency of molecular interactions between target molecules and binding sites.This research project aims to enhance the performance of medical diagnostic kits by developing surface tension-powered stirring devices using reactive inkjet printing technology which will aim to increase the rate of successful collisions between these target molecules and the binding sites.The ink utilised in this study comprises of silk fibroin, a structural protein derived from the silk cocoon of the Bombyx mori.Silk fibroin possesses versatile biological applications, making it an ideal material for biomedical purposes.The ink is created by subjecting the fibroin fibres to a series of processes, including degumming to remove sericin layers and obtaining Regenerated Silk Fibroin (RSF) through various processes such as Dissolution and Dialysis.Methanol exposure is employed to induce the solidification of the printed structure through the formation of secondary protein structures, specifically Beta-pleated sheets.To facilitate controlled rotation and stirring, Polyethylene Glycol (surfactant) is strategically printed at designated regions, referred to as motor regions.The stirrers are driven by surface tension gradients through the use of a surfactant (Marangoni effect), which elucidates the mechanism behind the induced rotation.Two different stirrer designs were tested, both of which exhibited significant motion.This innovative approach aims to improve reagent and sample mixing within diagnostic kits, thereby enhancing the accuracy and speed of medical diagnosis.The integration of reactive inkjet printed silk micro stirrers holds great promise for advancing the field of rapid medical diagnosis, contributing to more effective disease detection and timely intervention.These stirring devices are also valuable in industries requiring homogenous mixing at small scales (nanoparticle synthesis), immunoassay testing in medical diagnosis kits and lab-on-chip applications.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".