Self‐Powered Smart Vibration Absorber for In Situ Sensing and Energy Harvesting
Bibliographic record
Abstract
Vibration signals are essential data for the health monitoring of structures and the cyber–physical system. However, commercial vibration sensors are generally installed on struts/surfaces to gather raw data, which affects the result or causes the detachment problem. This study uses a facile 3D printing method to fabricate a smart vibration absorber with an interior multifunctional multimaterial elastic lattice (MMEL). These lattices, which work as vibration absorbers, have been demonstrated to possess the functions of self‐powered sensing and energy harvesting via the triboelectric effect. The triangular geometry parameter, materials type, etc. have been investigated to illustrate their basic mechanical and triboelectric properties and their coupled influence. Further, the results of the shock test show that MMEL can decrease the peak force from approximately 625 to 90 N and convey the Voc impulse signal simultaneously. The vibration signal has been collected through the MMEL to detect the vibration frequency and charge a watch simultaneously, demonstrating the feasibility and practical potential of the MMEL. The research provides a new method for constructing a multifunctional vibration absorber for applications.
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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".