Photoactive Coatings for Potentially Enabling a Battery to be Charged with Light
Bibliographic record
Abstract
Harvesting solar energy is among the most critical challenges of our society. This is of importance because sunlight has the potential to satisfy global energy demands. Moreover, it is non-polluting and it is sustainable. Converting sunlight to electricity is further advantageous for improving the affordability of electricity and providing electricity to those that are not connected to an energy grid. However, the variable intensity and intermittent sunlight are current hurdles because electricity generation is disrupted during these periods of low light. As such, there is a need for innovative approaches to store harvested sunlight and its conversion. Solar cells and batteries can be connected to charge batteries from sunlight. While the connection of these two conventional technologies is a viable solution for harvesting and storing sunlight, there are some limitations. Their connectivity is often bulky and inefficient. A promising alternative for storing solar energy is a photo-rechargeable battery. This system captures sunlight, converts it into electricity, and stores the electricity in a battery all at the molecular level. We will present the rationally designed coating that can be used towards charging the battery at the molecular level. The synthesis of a molecular component of the coating that can both harvest light and transfer it to energy within a conventional battery will be presented. Transforming the light harvesting material to coating to encapsulate the active components of the battery will be presented. The photo-physical and electrochemical properties of the combined opto- and electroactive coating will be presented.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".