The adsorption-diffusion model and biomimetic simulation reveal the switchable roles of silicon in regulating toxic metal uptake in rice roots.

Soil contamination by heavy metals has become a serious threat to global food security. The application of silicon (Si)-based materials is a simple and economical method for producing safe crops in contaminated soil. However, the impact of silicon on the heavy-metal concentration in plant roots, which are the first line in the chain of heavy-metal entering plants and causing stress and the main site of heavy-metal deposition in plants, remains puzzling. We proposed a process-based model (adsorption-diffusion model) to explain the results of a collection of 28 experiments on alleviating toxic metal stress in plants by Si. Then we evaluated the applicability of the model in Si-mitigated trivalent chromium (Cr[III]) stress in rice, taking into account variations in experimental conditions such as Cr(III) concentration, stress duration, and Si concentration. It was found that the adsorption-diffusion model fitted the experimental data well (R2 > 0.9). We also verified the binding interaction between Si and Cr in the cell wall using SEM-EDS and XPS. In addition, we designed a simplified biomimetic device that simulated the Si in cell wall to analyze the dual-action switch of Si from increasing Cr(III) adsorption to blocking Cr(III) diffusion. We found that the adsorption of Cr(III) by Si decreased from 58% to 7% as the total amount of Cr(III) increased, and finally the diffusion blocking effect of Si dominated. This study deepens our understanding of the role of Si in mitigating toxic metal stress in plants and is instructive for the research and use of Si-based materials to improve food security.

Spatial variation in stability of wheat (Triticum aestivum L.) straw phytolith-occluded carbon in China.

Global climate warming, driven by human activities emitting greenhouse gases like CO2, results in adverse effects, posing significant challenges to human health and food security. In response to this challenge, it is imperative to enhance long-term carbon sequestration, including phytolith-occluded carbon (PhytOC). Currently, there is a dearth of research on the assessment and distribution of the stability of PhytOC. Additionally, the intricate relationships and effects between the stability and environmental factors such as climate and soil remain insufficiently elucidated. Our study provided a composite assessment index for PhytOC stability based on a rapid solubility assay and principal component analysis. The machine learning models that we developed in this study, utilize experimentally and publicly accessible environmental data on large spatial scales, facilitating the prediction and spatial distribution mapping of the PhytOC stability using simple kriging interpolation in wheat ecosystems across China. We compared and evaluated 10 common classification machine learning models at 10-fold cross-validation. Based on the overall performance, the Stochastic Gradient Boosting model (GBM) was selected as predictive model. The stability is influenced by dynamic and complex environments with climate having a more significant impact. It was evident that light and temperature had a significant positive direct relationship with the stability, while the other factors showed indirect effects on the stability. PhytOC stability exhibited obvious zonal difference and spatial heterogeneity, with the distribution trend gradually decreasing from the southeast to the northwest in China. Overall, our research contributed to reducing greenhouse gas emissions and achieving global climate targets, working towards a more sustainable and climate-resilient future.

The spatial distribution of phytoliths and phytolith-occluded carbon in wheat (Triticum aestivum L.) ecosystem in China.

Phytolith is a form of SiO2 in plants. Carbon can be sequestrated as phytolith-occluded carbon (PhytOC) during the formation of phytoliths. PhytOC is characterized by its high resistance to temperature, oxidation and decomposition under protection of phytoliths and can be stored in the soil for thousands of years. Soil also is a huge PhytOC sink; however, most studies focus on PhytOC storage in straw and other residues. Wheat is a major staple food crop accumulating high content of Si and distributed widely, while its potential for PhytOC is not clear. At present, PhytOC storage only considers on the average value, but not on the relationship between ecological factors and the spatial distribution of PhytOC sequestration. Climatic factors and soil physiochemical properties together affect the formation process and stability of phytoliths. In our study, we collected wheat straw and soil samples from 95 sites among five provinces to extract phytolith and PhytOC. We constructed XGBoost model to predict the spatial distribution of phytolith and PhytOC across the country using the national soil testing and formula fertilization nutrient dataset and climate data. As a result, soil physiochemical factors such as available silicon (Siavail), total carbon (Ctot) and total nitrogen (Ntot) and climate factors related to temperature and precipitation have a great positive impact on the production of phytoliths and PhytOC. Meanwhile, PhytOC storage in wheat ecosystems was estimated to be 7.59 × 106 t, which is equivalent to 27.83 Tg of CO2. In China, the distribution characteristics of phytoliths and PhytOC in wheat straw and soil display a trend of decrease from south to north. He'nan Province is the largest wheat production area, producing approximately 1.59 × 106 t PhytOC per year. Therefore, PhytOC is a stable CO2 sink pathway in the agricultural ecosystems, which is of great importance for mitigating climate warming.