Graded Recombination Layers for Multijunction Photovoltaics
Bibliographic record
Abstract
Multijunction devices consist of a stack of semiconductor junctions having bandgaps tuned across a broad spectrum. In solar cells this concept is used to increase the efficiency of photovoltaic harvesting, while light emitters and detectors use it to achieve multicolor and spectrally tunable behavior. In series-connected current-matched multijunction devices, the recombination layers must allow the hole current from one cell to recombine, with high efficiency and low voltage loss, with the electron current from the next cell. We recently reported a tandem solar cell in which the recombination layer was implemented using a progression of n-type oxides whose doping densities and work functions serve to connect, with negligible resistive loss at solar current densities, the constituent cells. Here we present the generalized conditions for design of efficient graded recombination layer solar devices. We report the number of interlayers and the requirements on work function and doping of each interlayer, to bridge an work function difference as high as 1.6 eV. We also find solutions that minimize the doping required of the interlayers in order to minimize optical absorption due to free carriers in the graded recombination layer (GRL). We demonstrate a family of new GRL designs experimentally and highlight the benefits of the progression of dopings and work functions in the interlayers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".