RF Power Harvester With Varactor-Enabled Wide-Power-Range Capability for Wireless Power Transfer Applications
Bibliographic record
Abstract
This article proposes an approach of designing a wide-power-range RF power harvester integrated with a dc – dc boost converter, in order to facilitate wireless charging driven by wireless power transfer (WPT). The approach addresses the operational constraints posed by a dc – dc boost converter with maximum power control functionality, achieving high power conversion efficiencies (PCEs) across a wide input power range. It involves the integration of a varactor to dynamically compensate for fluctuations in the input impedance of the rectifier. The direct biasing of the varactor is strategically achieved through a double-voltage rectifying structure, without any external biasing methods. The proposed rectifier demonstrates high RF- dc PCEs exceeding 50% within an input power range of 2.4–20.9 dBm in the experiment, under a constant voltage load of 3.3 V, which emulates the dc – dc boost converter effects. Our design surpasses the conventional capacitor-based approach by more than 10 dB in the operating power range without relying on external bias. Finally, a self-powered wireless sensor node is designed based on the proposed rectifier, demonstrating the stability of rectifier’s performance within a system design. It also validates the potential use of the proposed rectifier for efficiently powering wireless sensor nodes in practical scenarios.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".