Hydrovoltaic Energy Harvesting From Nut Shells
Bibliographic record
Abstract
Water‐induced electric generators (WEGs) exhibit tremendous promise as sustainable energy sources harvesting electricity through the interaction between materials and water utilizing the hydrovoltaic effect, an innovative green energy harvesting method. However, existing water‐induced electric generator devices predominantly rely on inorganic materials with limited research on naturally available, bio‐based materials for hydrovoltaic energy harvesting. This study introduces a novel nutshell‐based hydrovoltaic water‐induced electric generator for the first time. This low‐cost, organic, and efficient renewable energy source can generate a voltage above 600 mV with a power density exceeding 5.96 μW cm−2 utilizing streaming and evaporation potential methodologies, which can be sustained for more than a week. Notably, after further chemical treatments and combining the physical and chemical phenomena, output voltage and maximum current density reach a record high of 1.21 V and 347.2 μA cm−2 respectively, which outperforms most inorganic and organic materials‐based water‐induced electric generators. By connecting two units in series and parallel, this eco‐friendly water‐induced electric generator can power an LCD calculator without the assistance of any rectifier. We believe that this novel nutshell‐based water‐induced electric generator provides a significant advancement in water‐induced electric generator technology by offering a sustainable solution for powering electronic devices utilizing agricultural waste.
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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".