A Biomimetric Lactate Imprinted Smart Polymers as Capacitive Sweat Sensors
Bibliographic record
Abstract
This paper demonstrates a non-enzymatic biomimetric flexible sweat sensor. Fabricated by multi-layered assembly, the layers comprised a conductive nanoporous carbon nanotube-cellulose nanocrystals (CNC/CNT) porous films entrained with poly (N-isopropylacrylamide) (pNIPAM) based microgel. The pNIPAM microgels based sensor films are effective for detection of ionic strength. The pNIPAM microgels coated with lactate imprinted poly (aniline/phenylboronic acid) (pANI/PBA) moieties switch off their responsivity to NaCl and instead effectively respond to lactate. As such, the printed pNIPAM microgels have been demonstrated as flexible capacitive sensor for multiplex detection of electrolyte (e.g. NaCl) and lactate levels in sweat analysis. The linear concentration range obtained for pNIPAM @CNC/CNT sensor in response to NaCl was 3-80 mM, with a limit of detection (LOD) of 0.23 mM NaCl. The lactate imprinted pANI/PBA- pNIPAM @CNC/CNT sensor resulted in a linear range of 1 mM-25 mM, and a LOD of 0.10 mM, an order of magnitude higher than the non-imprinted form. For both analytes, the sensor precision ranged from 0.5-5.0%, indicative of the overall reliability and validity. The fabricated flexible sensors were demonstrated to be effective in quantitation of electrolytes (principally NaCl) and lactate in a real sweat sample.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".