Application of Metamaterial in Renewable Energy: A Review
Bibliographic record
Abstract
Metamaterial advancements hold promise for compact renewable energy harvesting, capturing acoustic, electromagnetic, mechanical, and solar energy on a modest scale across global industries. Engineered structures surpass natural material limitations, offering capabilities unattainable in traditional counterparts. This investigation explores metamaterials' manipulation of acoustic, electromagnetic, mechanical, and solar energy. Mechanical metamaterials convert strain into electrical energy, applicable from interstellar travel to terrestrial infrastructure. Precision-configured acoustic metamaterials efficiently harness dispersed acoustic energy, improving renewable energy methodologies. Integration into photovoltaic cells showcases metamaterials' solar potential, with innovative designs enhancing solar energy conversion efficiency. Electromagnetic metamaterials efficiently absorb and convert frequencies into usable energy from communications and monitoring systems, in the agricultural and environmental sectors. Comparative analyses highlight noteworthy efficiency advancements, underscoring metamaterials' transformative influence on renewable energy. As they redefine the sector, implications extend to both small-scale devices and large-scale applications, positioning them as pivotal contributors. The paper critically evaluates metamaterial effectiveness in harnessing diverse energy sources, guiding future research. Metamaterial adaptability to different sizes and integration into technology reveals possibilities for compact energy sources. Ongoing research addresses scientific and economic challenges, paving the way for scaling metamaterial applications to commercial operations and emphasizing their importance in incorporating renewable energy into our technological milieu.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".