Photoacoustic and photothermal and the photovoltaic efficiency of solar cells: A tutorial
Bibliographic record
Abstract
Optical losses are the major drawback to overcome in the solar energy industry and development. Conversion of solar radiation into heat accounts for over 80% of the incident solar energy, which is driven by several aspects like the chosen design and encapsulation of the devices, built-in materials, and the mismatch between the solar spectrum and the active cell's bandgap energy. Photoacoustic (PA) and photothermal (PT) methods are characterization techniques based on the heat generation after the illumination of a material and respective detection. Since the beginning of the solar panel industrial development, these methods have been successfully applied to evaluate the photovoltaic efficiency of devices and the thermophysical parameters of related materials. Here, we present the state-of-art of application of these PA and PT methods to characterize solar cell devices and their built-in materials. Historical aspects, conceptual mechanisms of the basic phenomena, and perspectives on their application for energy-conversion measurements in the new frontier of solar cell research will be discussed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".