Noninvasive lactate monitoring in human sweat using fluorescent carbon quantum dots and molecularly imprinted polymer sensing
Bibliographic record
Abstract
Over the years, there has been a growing demand for biosensors that enable non-invasive monitoring of biomarkers, such as lactate, which is crucial for assessing athletic performance and detecting metabolic disorders. Traditional lactate sensing relies on enzyme-based biosensors, but these are costly, unstable, and lose activity over time. A promising alternative is the use of molecularly imprinted polymers (MIPs) which are synthesized with target molecules to create selective binding sites. MIPs offer greater stability and selectivity than enzymatic biosensors and can also be combined with carbon quantum dots (CQDs) to generate a fluorescent, image-based biosensor for lactate detection. The general goal of this research was to develop portable CQD-MIP biosensors that detect lactate by combining MIP selectivity with CQD sensitivity. Up to this point, the CQD-MIP sensors were in a liquid state, which made its integration into a point-of-care device challenging. However, for the first time in this research branch, we successfully synthesized CQD-MIP detectors on paper by polymerizing the monomer 3-aminopropyltriethoxysilane (APTES) with lactic acid, in the presence of tetraethyl orthosilicate (TEOS) as crosslinker and cetyltrimethylammonium bromide (CTAB) as surfactant. With this solid biosensor, we obtained a significant relationship between the concentration of lactate and color values measured by a smartphone camera using image processing techniques. The final goal is to create a non-invasive health monitoring tool that measures lactate in sweat via image-based sensing, eliminating the need for complex processing units. By integrating this biosensor with smartphone technology, we aim to make health monitoring more accessible and convenient.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".