Water‐Enabled Electricity Generation: A Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it