Recent progress in triboelectric nano-generators: Powering the future of smart agriculture
Bibliographic record
Abstract
The emergence of smart agriculture, driven by advancements in big data, Internet of Things (IoT), and Artificial Intelligence (AI), necessitates efficient, sustainable and autonomous energy solutions to power the sensors and equipment critical for smart farming. Conventional energy sources, such as batteries and wired power systems, have several challenges, including limited lifespan, environmental concerns, and complex installation procedures, especially in remote agricultural environments. Triboelectric Nanogenerators (TENGs) offer a promising alternative solution for generating electrical energy by capturing low-frequency mechanical energy from available natural sources such as rain, wind and water flow. TENG operates based on the combined effect of contact electrification (CE) and electrostatic induction (EI). However, there is limited exploration of TENG in powering smart agriculture technologies. This comprehensive review discusses the working mechanism, operational modes, and material engineering strategies employed to develop high-performance TENGs, with special focus on their significance in smart agricultural technologies. Different approaches used by TENGs to harness freely available energy from agricultural environments are summarized, and related mechanisms are discussed in detail. A brief overview of the recent development of the TENG application for nitrogen fixation, crop growth promotion and agricultural environmental monitoring is discussed, and the benefits of TENG technology for smart agriculture are discussed. Finally, the conclusions and strategic recommendations for future research directions for advancing TENG-assisted smart agriculture technologies are proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".