Toward High‐Performance Triboelectric Nanogenerators by Engineering Interfaces at the Nanoscale: Looking into the Future Research Roadmap
Bibliographic record
Abstract
Abstract To meet the future need for clean and sustainable energies, there has been considerable interest in the development of triboelectric nanogenerators (TENGs) that scavenge waste mechanical energies. The performance of a TENG at the macroscale is determined by the multifaceted role of surface and interface properties at the nanoscale, whose understanding is critical for the future development of TENGs. Therefore, various protocols from the atomic to the macrolevel for fabrication and tuning of surfaces and interfaces are required to obtain the desired TENG performance. These protocols branch out into three categories: chemical engineering, physical engineering, and structural engineering. Chemical engineering is an affordable and optimal strategy for introducing more surface polarities and higher work functions for the improvement of charge transfer. Physical engineering includes the utilization of surface morphology control, and interlayer interactions, which can enhance the active interfacial area and electron transfer capacity. Structural engineering at the macroscale, which includes device and electrode design/modifications has a considerable effect on the performance of TENGs. Future challenges and promising research directions related to the construction of next‐generation TENG devices, taking into consideration “interfaces” are also presented.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".