Band Engineering of Quantum Dot Substrates to Utilize QD-Generated Photocarrier for SERS Signal Amplification
Bibliographic record
Abstract
Interacting with light, plasmonic materials generate electric fields induced by localized surface plasmon resonance (LSPR) phenomenon. Electric field generated by plasmonic materials can induce optical phenomena such as light scattering, light emissive recombination and surface-enhanced Raman scattering (SERS). These phenomena grant versatile functionalities to plasmonic materials which can be applied to various devices such as photovoltaics, light emitting diode, meta material, SERS and so on. Amplifying electric field of plasmonic materials can improve the optical phenomenon and applications. Injecting external carriers into plasmonic materials can be good solution to improve electric field generation. In this research, we used incident light to generate photo-carrier and designed the band structure of the materials to extract photo-carrier into the plasmonic materials. We fabricated the substrate consisting of lead sulfide quantum dot/zinc oxide/plasmonic structure. The bottom quantum dot (QD) layer absorbs light and transfer photo-carrier to zinc oxide and plasmonic structure. For the photo-active material, we selected lead sulfide quantum dot (QD) because the band gap of the QD is conveniently tunable by controlling the size of QD. We synthesized the QD with the band gap around 1.3eV to optimize the light absorption ranging from 400nm to 900nm. Upper ZnO layer forms p-n junction with QD and selectively transports electron to top plasmonic materials. To verify the enhancement of electric field in photo-carrier injected plasmonic structure (PCIPS) substrate, we applied PCIPS substrate to SERS device. We measured 4 different molecules and the SERS signals enhanced from 3 to 7 times. Kelvin probe force microcopy (KPFM) measurement showed the change in surface potential of the substrate with and without light incidence. Our device showed great potential in SERS application because the device showed meaningful improvement in SERS signal simply using incident laser used to detect molecules. Figure 1
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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) | 0.001 | 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".