Bio‐based materials for solar cells
Bibliographic record
Abstract
Abstract Plant‐based materials are emerging as an alternative to conventional components in advanced energy applications. Among these, energy harvesting from sunlight is highly attractive and, in fact, represents the fastest growing energy technology. This review addresses the broad field of solar cell science since plant‐based components can be utilized in almost all solar technologies, and in certain photovoltaic technologies, they can fulfill most of the roles in photovoltaic devices. There is strengthened recent interest in developing sustainable materials options as well as new functionalities being developed for bio‐based materials. This contribution describes the different options for plant‐derived materials in photovoltaics and discusses their deployment feasibility. We focus on performance, lifetime, and embedded energy, all of which are critical to achieve—economically and sustainably–competitive photovoltaic devices. We address the tendency in the current literature for greenwashing, given that not all plant‐based solutions are environmentally‐sound at the device level. On the other hand, plant‐based materials can offer functionalities that cannot be reached with currently used materials. This article is categorized under: Sustainable Energy > Solar Energy Emerging Technologies > Materials Sustainable Energy > Bioenergy
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".