Nanomaterials and their role in advancing biodiesel feedstock production: A comprehensive review
Bibliographic record
Abstract
Sustainable socio-economic development largely depends on the sustainability of the energy supply from economic, environmental, and public health perspectives. Fossil fuel combustion only meets the first element of this equation and is hence rendered unsustainable. Biofuels are advantageous from a public health perspective, but their environmental and economic sustainability might be questioned considering the conflicts surrounding their feedstocks, including land use change and fuel vs. food conflict. Therefore, it is imperative to put more effort into addressing the downsides of biofuel production using advanced technologies, such as nanotechnology. In light of that, this review strives to scrutinize the latest developments in the application of nanotechnology in producing biodiesel, a promising alternative to fossil diesel with proven environmental and health benefits. The main focus is placed on nanotechnology applications in the feedstock production stage. First, the latest findings concerning the application of nanomaterials as nanofertilizers and nanopesticides to improve the performance of oil crops are presented and critically discussed. Then, the most promising results reported recently on applying nanotechnology to boost biomass and oil production by microalgae and facilitating microalgae harvesting are reviewed and mechanistically explained. Finally, the promises held by nanomaterials to enhance animal fat production in livestock, poultry, and aquaculture systems are elaborated. Despite the favorable features of using nanotechnology in biodiesel feedstock production, the presence of nanoparticles in living systems is also associated with important health and environmental challenges, which are critically covered and discussed in this work.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".