Voltage-Doubler RF-to-DC Rectifiers for Ambient RF Energy Harvesting and Wireless Power Transfer Systems
Bibliographic record
Abstract
Wireless Power Transfer (WPT) is promoted as a key enabling technology (KET) for the widespread use of batteryless Internet of Things (IoT) devices and for 5G wireless networks. RF-to-DC rectifiers are essential components for the exploitation of either ambient RF power or wireless transmitted power from a dedicated source. There are several alternative rectifier topologies which can be selected depending on the desired wireless charging scenario and may include one or more diodes. For full rectification, a minimum of two diodes are needed. The current chapter discusses various implementations of voltage-doubler designs, which revolve around the basic topology of two diodes and two capacitors. Schottky diodes are usually used, in combination with lumped capacitors. Off-the-shelf diodes include both separate diodes and integrated voltage-doubler topologies in a single package. Rectifiers are inherently narrowband, non-linear devices, and the RF-to-DC efficiency, which is usually the figure of merit, depends non-linearly on both the termination load and the received RF power. The bandwidth of the rectifier depends on the preceding matching network.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.026 |
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".