Assessing brushite-polyphosphate nanocomposites as nanofertilizers for improved phosphorus use efficiency in maize
Bibliographic record
Abstract
Phosphorus (P) inefficiency in conventional fertilizers poses a critical challenge to sustainable agriculture and global food security. This study introduces brushite-polyphosphate (DCPD-PolyP) nanofertilizers as a novel, high-efficiency alternative to enhance phosphorus availability, micronutrient uptake, and maize productivity. Advanced characterization using X-ray diffraction and scanning electron microscopy confirmed the amorphous nanostructure of brushite-polyphosphate (50–100 nm), while inductively coupled plasma optical emission spectroscopy revealed high phosphorus (22.72%) and reduced calcium (18.66%) content in the composite. In greenhouse trials, maize treated with DCPD-PolyP at full concentration (C1) exhibited 170% higher root biomass (12.783 g), 240% greater shoot biomass (29.090 g), and 42% increased plant height (136.83 cm) compared to controls, outperforming traditional triple superphosphate (TSP) and unmodified DCPD. Notably, P uptake surged by 280% (5.434 g at C1), with phosphorus use efficiency peaking at 56.93 under reduced doses. Soil Olsen-P levels reached 61.930 mg kg -1 (C2), significantly exceeding TSP and control treatments. Remarkably, DCPD-PolyP enhanced bioavailability of key micronutrients (Ca, Cu, Mg, Mn, Fe, Zn) in both plants and soil, demonstrating dual benefits for crop nutrition and soil health. These results highlight DCPD-PolyP’s potential to revolutionize nutrient management in agriculture, bridging the gap between advanced material science and sustainable food production by optimizing resource efficiency.
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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 | 0.000 | 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 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".