Facile Semiconductor p–n Homojunction Nanowires with Strategic p-Type Doping Engineering Combined with Surface Reconstruction for Biosensing Applications
Bibliographic record
Abstract
Abstract Photosensors with versatile functionalities have emerged as a cornerstone for breakthroughs in the future optoelectronic systems across a wide range of applications. In particular, emerging photoelectrochemical (PEC)-type devices have recently attracted extensive interest in liquid-based biosensing applications due to their natural electrolyte-assisted operating characteristics. Herein, a PEC-type photosensor was carefully designed and constructed by employing gallium nitride (GaN) p–n homojunction semiconductor nanowires on silicon, with the p-GaN segment strategically doped and then decorated with cobalt–nickel oxide (CoNiOx). Essentially, the p–n homojunction configuration with facile p-doping engineering improves carrier separation efficiency and facilitates carrier transfer to the nanowire surface, while CoNiOx decoration further boosts PEC reaction activity and carrier dynamics at the nanowire/electrolyte interface. Consequently, the constructed photosensor achieves a high responsivity of 247.8 mA W−1 while simultaneously exhibiting excellent operating stability. Strikingly, based on the remarkable stability and high responsivity of the device, a glucose sensing system was established with a demonstration of glucose level determination in real human serum. This work offers a feasible and universal approach in the pursuit of high-performance bio-related sensing applications via a rational design of PEC devices in the form of nanostructured architecture with strategic doping engineering.
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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 | 0.000 | 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 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".