Valorization and repurposing of seafood waste to next-generation carbon nanofertilizers
Bibliographic record
Abstract
The surge in population growth, urbanization, and shifts in food consumption patterns have resulted in a rise in the global production of organic waste. This waste material must be repurposed and effectively managed to minimize environmental footprints. The generation of abundant biowaste, especially from marine sources, may have detrimental impacts on the environment and human health if left untreated. In recent years, substantial efforts have been made to valorize seafood waste, contributing significantly to the sustainability of the blue economy through the repurposing of marine discards. Seafood waste can be transformed into different by-products which can be applied as soil amendment to enhance soil quality and health, demonstrating a holistic approach to repurposing and waste utilization. The extraction of bioactive metabolites from these waste materials has opened avenues for developing nanofertilizers. This intersection of waste valorization and nanotechnology is pertinent in the context of sustainable agriculture. While conventional fertilizers improve soil fertility with significant leaching and gaseous losses, the advent of nanofertilizers introduces a paradigm shift with their targeted and controlled delivery mechanisms, rendering them significantly more efficient in resource utilization and mitigation of environmental crises. This review delves into the global issue of seafood waste accumulation, offering an overview of various methods for repurposing. The primary aim of this review is to bring into limelight the recent efforts in developing a portfolio of carbon-based nanofertilizers derived from organic waste, replacing previous valorization methods due to their sustainability, efficiency, and eco-friendliness. There are immense opportunities for future work in this direction by exploring innovative nanoengineering approach owing to the potential of carbon nanofertilizers in enhancing the production of value-added products and reduction of environmental pollution.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".