A highly sensitive double-layer structured nanodevice for moisture induced power generation
Bibliographic record
Abstract
Abstract With the increasing global energy demand, traditional energy sources are gradually failing to meet society’s needs while also having a potential of being harmful to the environment. As such, energy generating technologies capable of converting ubiquitous environmental energy into usable forms, such as electricity, have received increasing attention. In this research, a power generating device composed of a graphene (G) and titanium dioxide nanowire (TiO 2 NWs) double-layer structure is prepared by an electrophoretic deposition method. Since both materials have special nanochannel structures and non-zero zeta potential, they can convert environmental energy into electricity through the diffusion, ionization, and natural evaporation of water. Furthermore, the efficiency of this novel sensor is much higher than their respective single-layer devices. By application of only 6 μ l of water, the open circuit voltage (U OC ) generated on the G-TiO 2 sensor is as high as 1.067 ± (0.008) V. In comparison, TiO 2 NWs single layer can only generate a U OC around 500 mV, and graphene itself can only produce a U OC no more than 250 mV under the same condition. Additionally, the effect of different deposition times of graphene on the surface morphology and thickness of graphene film is explored, and the effects of these changes in microstructure on performance is discussed in depth. Aside from power generation, the high sensitivity of the device to different volumes of water brings its use in the detection of trace amounts of water, and its high efficiency of energy conversion suggests a potential application as a power supply. This research not only provides a satisfactory candidate for inexpensive and efficient evaporative power generation, but also builds a foundation for developing new, intelligent, and self-powered electronic technologies.
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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 | 0.000 |
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".