Enhanced near-infrared plasmonic sensing chips with ultra-thin optical absorption nanolayer fabricated by cross-beam pulsed laser deposition (CB-PLD)
Bibliographic record
Abstract
Plasmonic biosensing is an optical technique that based on refractive index change when the target molecules interact with the sensing surface. Main plasmonic material used in this type of biosensors is gold. Our work is dedicated to design a novel sensing SPR chip with vanadium dioxide (VO2) nanolayer, known for its unique insulator-to-metal phase transition in the near-infrared region. VO2 thin film is deposited using Cross-Beam Pulsed Laser Deposition (CB-PLD) method and gold layer deposition is performed by sputtering. By employing the VO2 nanolayer, we create a highly responsive biosensing interface (with a much-improved sensitivity and also a wide dynamic measurement range). The VO2 layer's ability to modulate the refractive index enables precise control of the excited plasmon resonance. This interaction results in enhancing sensitivity and the capability to detect low-concentration analytes with high accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".