Solar Panel Recycling from Circular Economy Viewpoint: A Review
Bibliographic record
Abstract
Abstract Solar energy has emerged as a prominent contender in this arena, attracting significant attention across the globe. Governments worldwide have undertaken extensive efforts to encourage the adoption of renewable energy, increasing the usage of solar panels. Despite its benefits, the deployment of photovoltaic (PV) modules generates significant waste, thereby posing a major environmental challenge. This study explores several recycling techniques, including physical, thermal, and chemical methods, that could be employed to manage solar panel waste. An in-depth analysis of separation techniques presently employed and underdevelopment was studied and compared to determine the physical treatment necessary for the separation of glass and aluminium. Extraction of rare earth metals cadmium, copper and tellurium requires chemical treatments using organic and inorganic solvents along with thermal treatment at 500–600°C to remove the EVA polymer. Recovery of silicon wafers and rare metals through various metal extraction processes is further examined. Europe was concluded as a frontrunner in solar waste management policies after analysis of the governmental policies of developed and developing nations of the world. The circular economy model developed portrayed a systematic approach for the removal of different components of a solar panel and reintegration into the manufacturing process. The implementation of a robust circular economy for renewable energy systems is conditional upon the optimization of resource recovery while minimizing energy consumption and this serves as the governing framework of this review.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".