A New Configuration Diplexer for RF Harvesting Applications
Bibliographic record
Abstract
This paper presents an advanced microstrip diplexer specifically engineered for radio frequency (RF) energy harvesting. As key elements in multi-band harvesting setups, diplexers allow for the concurrent capture and conversion of ambient RF power across several frequency bands, leading to a substantial increase in the total harvested energy. The proposed design incorporates two band-pass filters, tuned to 5.8 GHz and 2.26 GHz, and constructed on a 1.6 mm thick FR-4 substrate (with a dielectric constant of 4.4 and a loss tangent of 0.025). A thorough evaluation of the filters' architecture and performance confirms their capability to convert ambient RF energy effectively. The diplexer efficiently segregates these frequency bands, enabling independent processing and rectification of the captured energy at each frequency. Such separation is essential for achieving maximum energy harvesting efficiency by reducing interference between bands and allowing the implementation of optimized rectifier circuits tailored to each band. Simulated S-parameters corroborate crucial performance indicators, including excellent impedance matching (low S11), minimal insertion loss (high S21 and S31), and significant isolation between ports (low S23 and S32), all of which are paramount for successful RF energy harvesting.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".