On the Optimization of a Photo-Electrode: Interplay of Photoactive and Conductive Materials in a Lithium-Ion Photo-Battery
Bibliographic record
Abstract
Green and sustainable devices that can harvest and store the solar energy along with delivering electricity during dark periods have garnered much attention. This is owing to solar energy being the best renewable source of energy for producing clean electricity that can potentially meet demand on a global scale. Although silica solar panels can harvest sunlight, they must be coupled with conventional batteries such as lithium ion batteries (LIBs) to store the collected energy. Organic dyes offer many advantages to silica panels. They are lighter, cheaper, more versatile, as well as a more sustainable harvesting solution. We therefore took advantages of the benefits of LIBs and an organic dye to lay the groundwork required for developing an all-in-one device that potentially can harvest sunlight and store the energy directly in the LIB. An organic dye was judiciously selected based on its known properties that meet the requirements for light harvesting and charge transfer with the LIB active components. We have established structure/property relationships that have confirmed the dye can be reduced by the battery’s electroactive components upon light absorption. This will be complemented by steady-state and time-resolved solid-state emission quenching studies. It will be shown that Raman spectroscopy can provide further insight in the effect of the photobattery architecture and the conductive surfaces on the charge transfer processes. This will be complemented with electrochemical studies, including galvanostatic cycling with the dye with various photo-cathode architectures to further understand the role of the microstructure in the electronic transfer. Systematically replacing various components of the LIB with their organic counterparts will also be presented to elucidate the photocharge mechanisms and lay the groundwork for an all-in-one photobattery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".