Examining Förster Energy Transfer for Semiconductor Nanocrystalline Quantum Dot Donors and Acceptors
Bibliographic record
Abstract
Excitation energy transfer involving semiconductor quantum dots (QDs) has received increased attention in recent years because their properties, such as high photostability and size-tunable optical properties, have made QDs attractive as Förster resonant energy transfer (FRET) probes or sensors. An intriguing question in FRET studies involving QDs has been whether the dipole approximation, commonly used to predict the electronic coupling, is sufficiently accurate. Accurate estimates of electronic couplings between two 3.9 nm CdSe QDs and between a QD and a chlorophyll molecule are reported. These calculations are based on transition densities obtained from atomistic semiempirical calculations and time-dependent density functional theory for the QD and the chlorophyll, respectively. In contrast to the case of donor−acceptor molecules, where the dipole approximation breaks down at length scales comparable to the molecular dimensions, we find that the dipole approximation works surprisingly well when donor and/or acceptor is a spherical QD, even at contact donor−acceptor separations. Our conclusions provide support for the use of QDs as FRET probes for accurate distance measurements.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".