Photoactive nanomaterials enabled integrated photo‐rechargeable batteries
Bibliographic record
Abstract
batteries and capacitors) has been increasing over the last years. The rising need for electricity storage and overcoming the intermittent nature of renewable energy sources have been potent drivers of this increase. Solar energy is the most abundant renewable energy source. Thus, the combination of photovoltaic devices with energy storing systems has been pursued as a novel approach in applications such as electric vehicles and smart grids. Among all the possible configurations, the "direct" incorporation of photoactive materials in the storing devices is most attractive because it will enhance efficiency and reduce volume/weight compared to conventional systems comprised two individual devices. By generating and storing electricity in a singular device, integrated photo-rechargeable batteries offer a promising solution by directly storing electricity generated by sunlight during the day and reversibly releasing it at night time. They hold a sizable potential for future commercialization. This review highlights cutting-edge photoactive nanomaterials serving as photoelectrodes in integrated photobatteries. The importance and influence of their structure and morphology and relevant photocatalytic mechanisms will be focal points, being strong influencers of device performance. Different architecture designs and working principles are also included. Finally, challenges and limitations are discussed with the aim of providing an outlook for further improving the performance of integrated devices. We hope this up-to-date, in-depth review will act as a guide and attract more researchers to this new, challenging field, which has a bright application prospect.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".