A Simple High Power, Fast Response Streaming Potential/Current-Based Electric Nanogenerator Using a Layer of Al<sub>2</sub>O<sub>3</sub> Nanoparticles
Bibliographic record
Abstract
Harvesting energy from ambient moisture and natural water sources is currently of great interest due to the need for standalone self-powered nano/micro-systems. In this work, we report on the development of a cost-effective nanogenerator based on a carbon paper-Al 2 O 3 nanoparticle layer-carbon paper (CAC) sandwich structure, where the 3D Al 2 O 3 layer is deposited via vacuum filtration. This type of device can produce an open-circuit voltage ( U OC ) of up to 4 V and a short-circuit current ( I SC ) of ∼18 μA with only an 8 μL water droplet applied. To our knowledge, this is the highest voltage yet reported from a single moisture/water-induced electricity nanogenerator using solid oxides and carbon-based materials. A remarkable output power of 14.8 μW can be reached with an optimized resistive load. An LED with a working voltage of 3–3.2 V can operate for a short time with the power from a single CAC device exposed to one 8 μL water droplet. Furthermore, a CAC generator adsorbing as little as 2 μL water droplets every 3 min can also give a U OC of 3.63 V. We show that CAC devices provide a robust electrical output over more than 200 wet–dry cycles without any deterioration in performance. These units demonstrate much promise as cost-effective electricity generators for harvesting energy from natural sources like rainwater, tap water, snow runoff, and dew. The response time of CAC devices can be as fast as 10–100 ms, making them ideal for applications as self-powered water detectors. The generation of power in this device arises from the streaming current. To assist in the optimization of these devices, we have analyzed how their response is related to such factors as layer thickness, time interval between application of water droplets, and the volume of each water droplet.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".