Degenerately Hydrogen Doped Molybdenum Oxide Nanodisks for Ultrasensitive Plasmonic Biosensing
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Plasmonic biosensors based on noble metals generally suffer from low sensitivities if the perturbation of refractive‐index in the ambient is not significant. By contrast, the features of degenerately doped semiconductors offer new dimensions for plasmonic biosensing, by allowing charge‐based detection. Here, this concept is demonstrated in plasmonic hydrogen doped molybdenum oxides (H x MoO 3 ), with the morphology of 2D nanodisks, using a representative enzymatic glucose sensing model. Based on the ultrahigh capacity of the molybdenum oxide nanodisks for accommodating H + , the plasmon resonance wavelengths of H x MoO 3 are shifted into visible‐near‐infrared wavelengths. These plasmonic features alter significantly as a function of the intercalated H + concentration. The facile H + deintercalation out of H x MoO 3 provides an exceptional sensitivity and fast kinetics to charge perturbations during enzymatic oxidation. The optimum sensing response is found at H 1.55 MoO 3 , achieving a detection limit of 2 × 10 −9 m at 410 nm, even when the biosensing platform is adapted into a light‐emitting diode‐photodetector setup. The performance is superior in comparison to all previously reported plasmonic enzymatic glucose sensors, providing a great opportunity in developing high performance biosensors.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 it