Dark Excitonic State Preparation in a Symmetry‐Protected Open Quantum Battery
Bibliographic record
Abstract
Abstract Previous work (J. Phys. Chem. C 2019 , 123 , 18303) showed that excitation energy can be stored in a symmetry‐protected dark state of an open quantum battery, without losses to the environment. However, how to prepare the battery in such a state remained unresolved. This study presents a charging protocol that uses a continuous‐wave laser to excite the system from its ground state to a bright excited state just above the dark state, followed by relaxation into the target dark state. Turning off the laser after the dark state is populated preserves the stored energy indefinitely. It is also shown that different types of dephasing affect the charging process in distinct ways, with some proving detrimental while others have minimal or no impact. Overall, the results demonstrate a practical method for charging excitonic quantum batteries and show how symmetry and controlled dissipation can be used to enhance energy storage and stability.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".