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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".