Tribo‐Tunneling DC Generator with Carbon Aerogel/Silicon Multi‐Nanocontacts
Bibliographic record
Abstract
Abstract Although tip‐enhanced tribo‐tunneling in metal/semiconductor point nanocontact is capable of producing DC with high current density, scaling up the process for power harvesting for practical applications is challenging due to the complexity of tip array fabrication and insufficient voltage output. Here, it is demonstrated that mechanical contact between a carbon aerogel and silicon (SiO2/Si) interface naturally forms multiple nanocontacts for tribo‐tunneling current generation with an open‐circuit voltage output (VOC) reaching 2 V, and short‐circuit DC current output (ISC) of ≈15 µA. It has a theoretical current density ( J*) on the order of 100 A m−2. Molecular dynamics simulation and atomistic field theory show that a strong localized electronic excitation can be induced at a dynamic carbon/SiO2 sliding interface, which is in good agreement with the experimental results. The DC power output is enhanced by the intense local pressure at the presence of nanocontacts, as well as the increased sliding velocity v. To demonstrate the method for practical applications, light‐emitting diodes (LEDs) with different colors are successfully lighted by a single‐carbon aerogel monolith/SiO2 sliding unit, and the DC electricity is stored in a capacitor without an additional rectification circuit.
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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".