Intriguing Two-Dimensional BeO-Based Tribo-Piezoelectric Nanogenerator
Bibliographic record
Abstract
The remarkable piezoelectric characteristics manifested by two-dimensional (2D) materials render them immensely coveted candidates for deployment in nanoscale energy harvesting tools. In this study, we demonstrate the generation of nanoenergy from the tribo-piezoelectric effect in a 2D BeO bilayer system by employing first-principles density functional theory calculations. We find that compression and sliding motions between two BeO layers produce a substantial tribo-piezoelectric effect. Lateral sliding of the upper BeO layer while the bottom BeO layer remains fixed engenders tribological energy from the vertical resistance force. Through tribological energy conversion, we show how BeO bilayers may overcome the interfacial sliding barrier and generate tribo-piezoelectricity. We obtain maximum energy corrugation in the range of 101–301 meV and shear strength in the range of 0.4–2.38 GPa during vertical compression. The highest out-of-plane piezoelectricity is obtained when the bilayers are in the A–A stacking configuration by lateral sliding. A maximum induced voltage of ∼0.22 V is attained through the vertical compressive sliding of the upper layer. These findings offer potential opportunities for harvesting nanoenergy from the tribo-piezoelectric effect of BeO systems, paving the way for advancing the field of self-powered sensors, wireless electronics, and wearable devices.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".