Photo-induced metal-oxide biosensor for analysis of biofluids
Bibliographic record
Abstract
Determining the concentration of biomarkers offers insights into the health condition and performance of the body. The majority of the biosensors applied to measuring biomarkers in biological fluids are electrochemical bases; however, these biosensors suffer from several key drawbacks. These include utilizing complex sensing materials to obtain desirable analytical performance, which prevents their practical application; and operation at a relatively high potential, which leads to inaccurate measurements due to the undesired oxidation of non-target molecules. A novel photo-induced chemiresistive biosensor is introduced here that addresses these challenges. A UV-induced ZnO nanorod (NR) chemiresistive biosensor is developed and applied to monitoring lactate and glucose, as model biomarkers in sweat. The detection mechanism of lactate based on its interaction with ZnO NRs is proposed. Furthermore, the effect of the electrode design and operating parameters, including irradiance, radiation wavelength, and applied potential, are evaluated. The highest response, the shortest response time, and complete recovery are obtained at 5.6 mW/cm2 irradiance of 365 nm and 0.1 V potential. The results indicate that the developed transduction platform utilizing a simple sensing layer is a promising technique with excellent analytical performance for detecting different biomarkers, thereby paving the way toward the emergence of photo-induced chemiresistive biosensors for real-life 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 | 0.001 |
| 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 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".