Photo-induced chemiresistive sensor for lactate and glucose monitoring
Bibliographic record
Abstract
• A novel photo-induced chemiresistive biosensor was developed for lactate detection. • The biosensor has a simple sensing layer, making it stable and easy to produce. • The biosensor shows superb selectivity, reproducibility, and long-term stability. • The biosensor has the practical application of monitoring various biomarkers. Biosensors provide essential information on the health condition and performance of the body by identifying the concentration of specific biomarkers. Photoelectrochemical biosensors operate based on the principle of separation of excitation and detection sources, leading to advantages such as a very low detection limit. Here, a novel photo-induced chemiresistive biosensor is introduced that addresses the key challenges of existing biosensors and provides favorable analytical performance. A highly sensitive and selective ultraviolet (UV)-induced zinc-oxide (ZnO) nanorod/lactate oxidase chemiresistive biosensor for lactate monitoring in sweat was designed. The sensor exhibited excellent lactate monitoring capability. Further, the biosensor showed superb selectivity with responses of 1% or less when exposed to interfering molecules, indicating its potential for analyzing the target biomarker in biological samples. Furthermore, the biosensor exhibited high reproducibility with a relative standard deviation (RSD) of less than 2% and long-term storage stability, highlighting its potential for practical purposes. To further demonstrate the application of this platform for the detection of other biomarkers, a UV-induced ZnO nanorod/glucose oxidase chemiresistive biosensor was developed for glucose monitoring. The overall results indicated the potential of this transduction platform as a promising technique with excellent analytical performance for detecting different biomarkers in biological fluids for practical applications.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".