Photoelectrochemical Bioanalyte Sensor Based on Engineered One-Dimensional Nanostructured Oxide
Bibliographic record
Abstract
Photoelectrochemical (PEC) non-enzymatic sensing systems are beneficial for the detection of low concentrations of biological molecules. Among different potential nanostructured materials, one dimensional TiO2 and ZnO nanorods are promising semi-conductor which can be successfully applied in many photoelectrochemical applications, mainly due to their photoactive properties, and long-term stability. In spite of their advantages, the wide band gap of 1D TiO2 and ZnO along with their high recombination rate confine their applications dramatically. These properties, prevent 1D TiO2 and ZnO from absorbing visible light; therefore, the semiconductor is only photoactive in the UV range. Here, we describe novel non-enzymatic sunlight-driven PEC sensors based on a modified 1D TiO2 nanorod array with cocatalyst and 1D ZnO core-shell structure for the aim of improved PEC non-enzymatic detection of glucose and H2O2, respectively. The 1D TiO2 and ZnO nanorods array were prepared through facile hydrothermal at and electrodeposition methods, respectively. The modified electrodes were characterized through FESEM, HRTEM, XRD, and UV-vis spectroscopy. The novel 1D TiO2 and 1D ZnO@TiO2 core shell electrodes exhibited enhanced PEC sensing properties with a low limit of detection and a high sensitivity, over a linear range of 0.1 – 1000 nM of glucose and 1 pM-100 mM of H2O2 in PBS. Additionally, the sensor displayed high selectivity, high stability, and high reproducibility. Overall, our engineered PEC sensor demonstrated ultrasensitive detection of glucose and H2O2 and a capable platform for continued development into the field of PEC non-enzymatic sensors.
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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 | 0.000 |
| Open science | 0.001 | 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".