Carbon nanofertilizers in agriculture: A critical review of soil ecosystem impacts and phytotoxicity
Bibliographic record
Abstract
The rapid growth in global population has placed immense pressure on food production systems. As a result, the overuse of chemical fertilizers and pesticides in modern farming has severely reduced soil fertility and disrupted critical ecosystem functions and equilibria. There have been significant leaching and runoff losses of key mineral elements in the environment due to synthetic fertilizer application, resulting in a substantial reduction in nutrient use efficiency (NUE) in different cropping systems. Nanofertilizers (NFs) have been considered more effective and economical than synthetic fertilizers because their nanostructure regulates the delivery of nutrients and enhances plant absorption due to a high surface area to volume ratio. Different types of carbon-based nanoparticles (CNPs), such as carbon nanotubes (CNTs), carbon nanodots (CDs), and carbon nanofibers (CNFs), have been synthesized from various biological sources acting as carbon nanofertilizers (CNFs). These CNPs stimulate soil microbial and enzymatic activities, promote organic matter decomposition, enhance carbon sequestration, and improve water retention, mainly through enhanced nutrient accessibility, aggregation with soil organic matter, and a high-surface-area matrix for microbial habitat and proliferation. Although CNFs exhibit considerable potential, several critical challenges remain, including high production costs, limited knowledge of their long-term environmental behavior, and the requirement for well-defined concentration thresholds tailored to specific soil–crop systems. This review outlines the benefits, potential drawbacks, and key challenges of CNFs for plants and soil health that must be addressed to harness their full potential as plant growth stimulants and soil quality boosters. • A critical review of carbon nanofertilizers' soil ecosystem impacts. • CNFs enhance soil health, microbial activity, and carbon sequestration. • Synthesis methods and structure-activity relationships are thoroughly discussed. • Dose-dependent phytotoxicity and soil microbial risks are key concerns. • Identifies knowledge gaps and future research priorities for safe use.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".