Modeling of noise-assisted quantum transfer between donor and acceptor with finite bandwidths
Bibliographic record
Abstract
Abstract We present an analytical and numerical study of noise-assisted quantum exciton (electron) transfer (ET) in a dimer consisting of donor and acceptor, modeled by interacting continuous electron bands of finite widths. We start with discrete energy levels for both bands. The interaction with the environment is modeled by a stationary stochastic process (noise) acting on all the donor and acceptor energy levels. Then, by using a continuous limit for the electron spectra, we derive integro-differential equations for the ET dynamics between the two bands. Finally, we derive from these equations rate-type differential equations for the ET dynamics. We formulate the conditions of validity of the rate-type equations. We consider different regions of parameters characterizing the widths of the donor and acceptor bands and the strength of the dimer-noise interaction. For a simplified model with a single energy level donor and a continuous acceptor band, we derive a generalized simple analytic expression and provide numerical simulations for the ET rate. They are consistent with Wigner–Weisskopf, Förster-type, and Marcus-type expressions, in their corresponding regime of parameters. For a weak dimer-noise interaction, our approach leads to the Wigner–Weisskopf ET rate, or to the Förster-type ET rate, depending on other parameters. In the limit of strong dimer-noise interaction, our approach is non-perturbative in the dimer-noise interaction constant, and it recovers the Marcus-type ET rate. Our analytic results are confirmed by numerical simulations. We demonstrate how our theoretical results are modified when both the donor and the acceptor are described by finite bands. We also show that, for a relatively wide acceptor band, the efficiency of the ET from donor to acceptor can be close to 100% for a broad range of noise amplitudes, for both ‘downhill’ and ‘uphill’ ET, for sharp and flat redox potentials, and for reasonably short times. We discuss possible applications of our approach.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".