Excitonics: A universal set of binary gates for molecular exciton processing and signaling
Bibliographic record
Abstract
The ability to regulate energy transfer pathways through materials is an important goal of nanotechnology, as a greater degree of control is crucial for developing sensing, solar energy, and bioimaging applications. Such control necessitates a toolbox of actuation methods that can direct energy transfer based on user input. Here we propose a novel molecular exciton gate, analogous to a traditional transistor, for controlling exciton migration in chromophoric systems. The gate may be activated with an input of light or an input flow of excitons. Unlike previous gates and switches that control exciton transfer, our proposal does not require isomerization or molecular rearrangement, instead relying on excitation migration via the second singlet (S2) state of the gate molecule--hence the system is named an "S2 exciton gate." After presenting a set of system properties required for proper function of the S2 exciton gate, we show how one would overcome the two possible challenges: short-lived excited states and suppression of false positives. Precision and error rates are studied computationally in a model system with respect to excited-state decay rates and variations in molecular orientation. Finally, we demonstrate that the S2 exciton gate gate can be used to produce binary logical AND, OR, and NOT operations, providing a universal excitonic computation platform with a range of potential applications, including e.g. in signal processing for microscopy.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".