An integrated RF energy–harvesting system with broad input voltage range and high power conversion efficiency
Bibliographic record
Abstract
By 2050, countries around the world are expected to be gradually phasing out fossil fuels and implementing greener energy technologies. In this work, we present a system employing Energy harvesting, a self-powered technology that can recycle energy from the surrounding environment. A high-efficiency radio frequency (RF) energy-harvesting chip was designed and fabricated. With an off-chip antenna and rectifier, the system scavenges ambient RF energy and converts it into usable energy, which is then stored in energy storage elements (such as a supercapacitor or a rechargeable battery). The system can further be implemented as an energy source for charging smart devices. The system-on-chip design consists of a cold start block, a boost converter with maximum power point tracking functionalities, and a charging block. The chip was fabricated using AMS 350 nm technology. Although the system was optimized for harvesting RF energy, it can be easily adapted to harvest other energy sources (i.e., mechanical and thermal energy sources). Using an optimized cold start architecture, the circuit has a cold start voltage of 380 mV. With an improved control strategy of power conversion, the system is capable of continuously charging up to 4.5 V with a broad input voltage range of 100 mV to 10 V and has a peak charging efficiency of 82%.
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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 | 0.001 |
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".