Collaboratively Harvesting Ambient Radiofrequency and Thermal Energy
Bibliographic record
Abstract
In this paper, an ambient power harvester with a single-diode device is proposed for simultaneously scavenging both radiofrequency (RF) and thermal energy in a mixed and cooperative manner. This cooperative harvesting process is theoretically examined through a proposed model of the diode and then validated by simulation and measurement. In the proposed cooperative power harvester, the harvested dc voltage from a thermal source is used to bias the diode for improving the diode's RF-to-dc power conversion efficiency (PCE). An accurate analytical model of the Schottky diode is developed for specifying the constraining parameters of RF-to-dc PCE and accurately predicting diode's performances in a low RF power range (≤ -25 dBm), respectively. The calculated results are found to be in a good agreement with the simulated ones obtained by the harmonic balance simulator in the advanced design system. For demonstration and validation, the proposed mixed cooperative power harvester is designed and prototyped on the basis of diode SMS7630. A total measured output dc power around 0.8 μW is obtained with an RF-to-dc PCE around 33.4%, when the two injecting power sources at the diode are both -30 dBm. In addition, rectennas with and without a matching network are both fabricated and tested. By eliminating the L matching network, the rectenna is found to offer a higher dc output power. The proposed mixed cooperative power harvester is hoped to find potential real-world applications in an ambient atmosphere with RF coverage and temperature gradient. It not only helps to produce a higher power but also provides a reliable way of improving the resilience of dc power production.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".