Lanthanide ions as spectral converters for solar cells
Bibliographic record
Abstract
The use of lanthanide ions to convert photons to different, more useful, wavelengths is well-known from a wide range of applications (e.g. fluorescent tubes, lasers, white light LEDs). Recently, a new potential application has emerged: the use of lanthanide ions for spectral conversion in solar cells. The main energy loss in the conversion of solar energy to electricity is related to the so-called spectral mismatch: low energy photons are not absorbed by a solar cell while high energy photons are not used efficiently. To reduce the spectral mismatch losses both upconversion and downconversion are viable options. In the case of upconversion two low energy infrared photons that cannot be absorbed by the solar cell, are added up to give one high energy photon that can be absorbed. In the case of downconversion one high energy photon is split into two lower energy photons that can both be absorbed by the solar cell. The rich and unique energy level structure arising from the 4f(n) inner shell configuration of the trivalent lanthanide ions gives a variety of options for efficient up- and downconversion. In this perspective an overview will be given of recent work on photon management for solar cells. Three topics can be distinguished: (1) modelling of the potential impact of spectral conversion on the efficiency of solar cells; (2) research on up- and downconversion materials based on lanthanides; and (3) proof-of-principle experiments. Finally, an outlook will be given, including issues that need to be resolved before wide scale application of up- and downconversion materials can be anticipated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".