Bibliographic record
Abstract
Optical wireless power transmission (OWPT) can be used for applications that cannot access traditional power using metal wires. Photovoltaic power-converting III-V semiconductor devices are the core components required for achieving such remote and galvanically isolated power deployments. The development of high-efficiency power converters has already propelled several sensors and probe applications. This growing applied physics field is leveraging the use of ubiquitous laser diode products, now commonly available at various wavelengths. Novel multijunction designs, based on the vertical epitaxial heterostructure architecture devices, have recently allowed fiber-based and free-space applications to quickly progress to higher electrical powers and to benefit from other laser wavelengths. Here, we discuss the perspectives of such multijunction power converters from the viewpoint of realizing additional OWPT deployments and for enabling more probe, sensor, or electronic subsystem power capabilities. The Perspective hence provides a roadmap for devices achieving not only higher conversion efficiency, but also elaborates on the practical aspects necessary to concurrently push the power converters to higher output powers. The photovoltaic multijunction power-converting device is particularly a game-changer for smartly increasing the output voltage and therefore maintaining practical optimal external loads at high laser input powers. Examples of conversion efficiencies above 60% for output powers up to 17.5 W are demonstrated at ∼808 nm in this study, and up to 22 W of output power is obtained with an efficiency of 48.9% at ∼980 nm.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".