High-efficiency commercial grade 1cm<sup>2</sup>AlGaInP/GaAs/Ge solar cells with embedded InAs quantum dots for concentrator demonstration system
Bibliographic record
Abstract
Triple-junction AlGaInP/InGaAs/Ge solar cells with embedded InAs quantum dots are presented, where typical samples obtain efficiencies of > 40% under AM1.5D illumination, over a range of concentrations of 2- to 800-suns (2 kW/m2 to 800 kW/m2). Quantum efficiency measurements show that the embedded quantum dots improve the absorption of the middle subcell in the wavelength range of 900-940 nm, which in turn increases the overall operating current of the solar cell. These results are obtained with 1 cm2 solar cells, and they demonstrate the solar cells' low series resistance, which and makes them ideal for the current generation in commercial concentrator systems. The thermal management and reliability of the solar cell and carrier is demonstrated by testing the experimental samples under flash (up to 1000-suns) solar simulator and continuous (up to 800-suns) solar simulator. Under continuous solar illumination, the solar cell temperature varies between ~Δ3°C at 260-suns linearly to ~Δ33°C at 784-suns when the solar cell is mounted with thermal paste, and ~Δ27°C at 264-suns linearly to ~Δ91°C at 785-suns when no thermal paste is used. The solar cells experience the expected shift in open circuit voltage and efficiency due to temperature, but otherwise operate normally for extended periods of time.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".