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<p>Nutrient polymerization slow-release fertilizers (NPFs) are attracting increasing interest because they are environmentally friendly and can increase crop yield. However, there are many challenges in practical agricultural applications because of the different synthesis processes used and the lack of studies on their molecular characteristics. In this study, response surface methodology (RSM), multiple linear regression (MLR) and artificial neural networks (ANN) were employed to optimize the performance of NPFs, identify the best preparation route and predict the relationships between molecular characteristics and nutrients release rate. The results demonstrated that RSM design was an effective way to prepare NPFs with different contents of nutrient functional groups, such as amino, hydroxyl, amide and phosphate. Further, the ANN method has superior nitrogen (N) prediction ability (<i>R</i> = 0.970; RMSE = 0.774), and the MLR method has superior phosphorus (P) prediction ability (<i>R</i> = 0.941; RMSE = 0.588). There were different contributions to the N and P release rates for amino, hydroxyl, amide and phosphate. After three months of incubation in water, these NPFs with different molecular characteristics achieved nutrient release percentages of 36.72%—76.84% and 39.37%—67.24% for N and P. Meanwhile, the actual differences in the performance and effects of NPFs designed through different processes could be shown by FTIR, <sup>1</sup>H NMR analysis and pot experiments. Thus, the establishment of these optimization and prediction methods for NPFs will foster application confidence in eco-friendly and sustainable agriculture.</p> Graphical abstract <p></p>

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Nutrient polymerization slow-release fertilizers (NPFs) Preparation by optimizing the synthesis process to regulate the nutrient release characteristics for crop specific fertilizer production

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Junyin Li,
  • Shugang Zhang,
  • Yuanyuan Yao,
  • Bin Gao,
  • Yaohua Zhang,
  • Dongdong Cheng,
  • Yuechao Yang

摘要

Nutrient polymerization slow-release fertilizers (NPFs) are attracting increasing interest because they are environmentally friendly and can increase crop yield. However, there are many challenges in practical agricultural applications because of the different synthesis processes used and the lack of studies on their molecular characteristics. In this study, response surface methodology (RSM), multiple linear regression (MLR) and artificial neural networks (ANN) were employed to optimize the performance of NPFs, identify the best preparation route and predict the relationships between molecular characteristics and nutrients release rate. The results demonstrated that RSM design was an effective way to prepare NPFs with different contents of nutrient functional groups, such as amino, hydroxyl, amide and phosphate. Further, the ANN method has superior nitrogen (N) prediction ability (R = 0.970; RMSE = 0.774), and the MLR method has superior phosphorus (P) prediction ability (R = 0.941; RMSE = 0.588). There were different contributions to the N and P release rates for amino, hydroxyl, amide and phosphate. After three months of incubation in water, these NPFs with different molecular characteristics achieved nutrient release percentages of 36.72%—76.84% and 39.37%—67.24% for N and P. Meanwhile, the actual differences in the performance and effects of NPFs designed through different processes could be shown by FTIR, 1H NMR analysis and pot experiments. Thus, the establishment of these optimization and prediction methods for NPFs will foster application confidence in eco-friendly and sustainable agriculture.

Graphical abstract