A comparatively experimental study on the temperature-dependent performance of thermophotovoltaic cells
Bibliographic record
Abstract
The power output and the efficiency of a thermophotovoltaic (TPV) system are determined by the radiation from a heat source, TPV cells, system parasitic losses, cell cooling subsystems, etc. The cells are core devices of a system, and the performance of the cells can be characterized by a number of parameters, including open circuit voltage (Voc), short circuit current density (Isc), and maximum output power density (Pm). The cell temperature has a great effect on these parameters. Although several papers have reported the dependence of these parameters on the cell temperature, few studies experimentally examined the temperature-dependent performance of the TPV cells or comparatively analyzed them. In this study, we investigated how the fundamental parameters of GaSb, Ge, and InGaAsSb cells varied with their operating temperatures using a home-built TPV prototype. The measured data indicate that the cell temperature significantly affects the performance of the TPV cells. Variations of these parameters with the cell temperature are different for various TPV cells. The Voc and Pm of the GaSb, Ge, and InGaAsSb cells decreased linearly with increasing cell temperature, while the Isc increased slightly. The normalized value of the temperature coefficient for the Pm of the GaSb was lower than those for the Ge and InGaAsSb cells, which indicated that the GaSb cells were relatively less sensitive to cell temperature. The results provided in this study are useful to improve cell performance and design TPV systems.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".