High-Efficiency Wide Input Power Range Three-Phase Radio Frequency Energy Harvester for IoT Applications
Bibliographic record
Abstract
The need for efficient and high-performance energy harvesting systems is rising to power modern wearable smart devices. Several energy sources can be harvested such as thermal, vibrational, and ambient radio frequency (RF). RF energy harvesters (RFEHs) are widely adopted as they wirelessly deliver power. This article proposes a new RFEH design based on the three-phase rectifier topology. The rectifier is integrated with a custom-designed phase shifter that can split received power equally and deliver three signals with a 120° phase shift at the same moment. Due to their low forward drop voltage and high sensitivity, the rectifier diodes are chosen to be Schottky diodes SMS7621-005LF from Skyworks. A prototype is fabricated on an RT/Duroid 5880 Laminates substrate with 0.005 in thickness to reduce the dielectric losses. The RF energy harvester shows promising results in the ISM band at a 435.6 MHz frequency. At 8 dBm available RF power, the prototype demonstrates a high efficiency of 56% end to end at 6 k$\Omega $load and 5.2 V output voltage. In addition, the system maintains an efficiency higher than 20% over a wide available input power range (IPR) of 28 dBm. The RFEH reports a 1 V sensitivity at −10 dBm. This system is ideal for supplying ambient sensor nodes and systems-on-chip (SoCs) in urban areas where RF electromagnetic waves are widely available.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".