Water‐Enabled Electricity Generation: A Perspective
Bibliographic record
Abstract
Harvesting energy from the environment offers many opportunities for the generation of clean power from self‐sustained systems and provides great promise for ameliorating the growing threat of the global environmental issues and the energy crisis. Ambient moisture and natural water sources have attracted huge research interest in the field of energy harvesting and conversion due to easy access, good sustainability, and the ubiquity of water on Earth. Taking advantage of the active interaction between water molecules and solid interfaces, various functional materials have been demonstrated to harvest energy and generate useable amounts of electrical power from water. In this review, some perspective on the development of water‐enabled electricity generation is given. The current preferred methods for water‐enabled electricity generation and relevant functional materials are summarized. Also, how the development of new materials and systems has led to significant improvements in the electrical power output reported for these devices is discussed. Then, some recent advances that have resulted in dramatic increases in the electrical output available from water‐enabled electrical generators (WEEGs) is discussed. Finally, some future trends in the development of WEEGs are outlined, and how this may result in practical applications and commercialization of these devices is shown.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".