Digital microfluidic platform assembled into a home-made studio for sample preparation and colorimetric sensing of S-nitrosocysteine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digital microfluidics (DMF) is a versatile lab-on-a-chip platform that allows integration with several types of sensors and detection techniques, including colorimetric sensors . Here, we propose, for the first time, the integration of DMF chips into a mini studio containing a 3D-printed holder with previously fixed UV-LEDs to promote sample degradation on the chip surface before a complete analytical procedure involving reagent mixture, colorimetric reaction, and detection through a webcam integrated on the equipment. As a proof-of-concept, the feasibility of the integrated system was successfully through the indirect analysis of S-nitrosocysteine (CySNO) in biological samples. For this purpose, UV-LEDs were explored to perform the photolytic cleavage of CySNO, thus generating nitrite and subproducts directly on DMF chip. Nitrite was then colorimetrically detected based on a modified Griess reaction , in which reagents were prepared through a programable movement of droplets on DMF devices . The assembling and the experimental parameters were optimized, and the proposed integration exhibited a satisfactory correlation with the results acquired using a desktop scanner. Under the optimal experimental conditions, the obtained CySNO degradation to nitrite was 96%. Considering the analytical parameters, the proposed approach revealed linear behavior in the CySNO concentration range between 12.5 and 400 μmol L −1 and a limit of detection equal to 2.8 μmol L −1 . Synthetic serum and human plasma samples were successfully analyzed, and the achieved results did not statistically differ from the data recorded by spectrophotometry at the confidence level of 95%, thus indicating the huge potential of the integration between DMF and mini studio to promote complete analysis of lowmolecular weight compounds.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it