Effects of the Distributions of Energy or Charge Transfer Rates on Spectral Hole Burning in Pigment–Protein Complexes at Low Temperatures
Bibliographic record
Abstract
Effects of the distributions of excitation energy transfer (EET) rates (homogeneous line widths) on the nonphotochemical (resonant) spectral hole burning (SHB) processes in photosynthetic chlorophyll-protein complexes (reaction center [RC] and CP43 antenna of Photosystem II from spinach) are considered. It is demonstrated that inclusion of such a distribution results in somewhat more dispersive hole burning kinetics. More importantly, however, inclusion of the EET rate distributions strongly affects the dependence of the hole width on the fractional hole depth. Different types of line width distributions have been explored, including those resulting from Förster type EET between weakly interacting pigments as well as Gaussian ones, which may be a reasonable approximation for those resulting, for instance, from so-called extended Förster models. For Gaussian line width distributions, it is possible to determine the parameters of both line width and tunneling parameter distributions from SHB data without a priori knowledge of any of them. Concerning more realistic asymmetric distributions, we demonstrate, using the simple example of CP43 antenna, that one can use SHB modeling to estimate electrostatic couplings between pigments and support or exclude assignment of certain pigment(s) to a particular state.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".