A non-enzymatic photoelectrochemical sensor based on Co-Pi modified one-dimensional titanium oxide embedded microscale reactor
Bibliographic record
Abstract
Photoelectrochemical (PEC) sensing systems are promising candidates for detecting low concentrations of biological molecules; they are particularly encouraging when offered in a non-enzymatic format. A pitfall of non-enzymatic PEC sensors is their specificity. This, however, is often resolved by utilizing inorganic nanocatalyst. Here, we describe a novel non-enzymatic sunlight-driven PEC sensor based on cobalt phosphate (Co-Pi) deposition on a one-dimensional titanium dioxide (1D-TiO2) nanorod array for the ultra-low detection of glucose. The 1D-TiO2 nanorod array was prepared through a simple hydrothermal method and modified with Co-Pi using photo-assisted electrodeposition. The result was a microscale fluidic reactor. The modified electrodes with various Co-Pi thicknesses photocatalyst were characterized through a variety of techniques, including HRTEM, XRD, UV–vis spectroscopy, electrochemical impedance spectroscopy, and chronoamperometry. The characterization methods served to study and confirm the optimal electrode structure. The novel 1D-TiO2/Co-Pi electrode exhibited enhanced absorbance in the visual range of the nanorods with increased photoactivity and no drastic modification of the array’s surface. The PEC sensor microscale reactor exhibited a low limit of detection of 0.031 nM and a high sensitivity of 900 μA mM−1 cm−2 over a linear range of 0.1–10000 nM, demonstrating ultrasensitive detection of glucose. Overall, the surface modification of TiO2 by Co-Pi improved the sensor properties resulting in high selectivity, high stability, and high reproducibility.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".