Cost-Effective Measurement Techniques for Accurate Characterization of Triboelectric Nanogenerators
Bibliographic record
Abstract
Sustainable energy harvesting technologies have gained significant attention for their potential to power modern electronic applications like the Internet of Things (IoT), wearable devices, and self-powered systems. Among these technologies, triboelectric nanogenerators (TENGs) stand out for their ability to convert mechanical energy into electricity. Their lightweight structure, scalability, and flexibility to harvest energy from various sources make them particularly well-suited for these applications. However, accurately characterizing their performance remains challenging due to their high internal impedance and dynamic operating conditions, leading to measurement inaccuracies that hinder research and practical adoption. This paper presents a practical and cost-effective voltage divider method for measuring triboelectric nanogenerator (TENG) output, addressing the impedance mismatch issue with standard instruments. The approach is simple, reproducible, and validated through simulations and experiments, demonstrating high accuracy (within$85-97 \%$of a professional electrometer). The proposed approach was validated through simulations in LTspice and experimental tests with a wind energy harvester, demonstrating improved measurement accuracy across different TENG setups. These results represent a significant advancement in overcoming TENG measurement challenges and pave the way for broader integration of TENGs into emerging technologies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".