Exciton Generation in Semiconductor Nanocrystals via the Near-Field Plasmon Energy Transfer
Bibliographic record
Abstract
The plasmon resonance of small-diameter metal nanoparticles has a unique decay character, as it produces almost no far field scattering. Instead, most of the absorbed light is emitted in the near field (NF), a phenomenon also known as strong field localization. Such nonradiative emission is short-lived and decays primarily via the production of heat, which prevents efficient harvesting of the NF energy. Here, we demonstrate a general strategy for coupling the NF radiation of surface plasmons to long-lived optical excitations in semiconductor nanocrystals (NCs). This concept was manifested through the observation of an enhanced exciton generation in CdSe NCs coupled to 5 nm Au nanoparticles. To distinguish the plasmon antenna effect from photoinduced charge transfer processes, both Au and CdSe nanoparticles were encapsulated into insulating CdS or ZnS matrices. A unique signature of the plasmon to exciton energy transfer was observed in photoexcitation measurements that unambiguously correlate the increase in the CdSe exciton population with the excitation of plasmon modes in Au domains. The demonstrated scheme for harvesting the NF radiation of metal nanoparticles presents an excellent opportunity for extracting the evanescent emission of surface plasmons that can find practical applications in photovoltaic or photocatalytic technologies.
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 | 0.000 | 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".