Arabidopsis seedlings respond differentially to nutrient efficacy of three rock meals by regulating root architecture and endogenous auxin homeostasis.

BACKGROUND: Plants show developmental plasticity with variations in environmental nutrients. Considering low-cost rock dust has been identified as a potential alternative to artificial fertilizers for more sustainable agriculture, the growth responses of Arabidopsis seedlings on three rock meals (basalt, granite, and marlstone) were examined for the different foraging behavior, biomass accumulation, and root architecture. RESULTS: Compared to ½ MS medium, basalt and granite meal increased primary root length by 13% and 38%, respectively, but marlstone caused a 66% decrease, and they all drastically reduced initiation and elongation of lateral roots but lengthened root hairs. Simultaneous supply of organic nutrients and trace elements increased fresh weight due to the increased length of primary roots and root hairs. When nitrogen (N), phosphorus (P), and potassium (K) were supplied individually, N proved most effective in improving fresh weight of seedlings growing on basalt and granite, whereas K, followed by P, was most effective for those growing on marlstone. Unexpectedly, the addition of N to marlstone negatively affected seedling growth, which was associated with repressed auxin biosynthesis in roots. CONCLUSIONS: Our data indicate that plants can recognize and adapt to complex mineral deficiency by adjusting hormonal homeostasis to achieve environmental sensitivity and developmental plasticity, which provide a basis for ecologically sound and sustainable strategies to maximize the use of natural resources and reduce the production of artificial fertilizers.

A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone.

The index mechanical properties, strength, and stiffness parameters of rock materials (i.e., uniaxial compressive strength, c, ϕ, E, and G) are critical factors in the proper geotechnical design of rock structures. Direct procedures such as field surveys, sampling, and testing are used to estimate these properties, and are time-consuming and costly. Indirect methods have gained popularity in recent years due to their time-saving and highly accurate results, which are comparable to those obtained through direct approaches. This study presents a procedure for establishing a deep learning-based predictive model (DNN) for obtaining the geomechanical characteristics of marlstone samples that have been recovered from the South Pars region of southwest Iran. The model was implemented on a dataset resulting from the execution of numerous geotechnical tests and the evaluation of the geotechnical parameters of a total of 120 samples. The applied model was verified by using benchmark learning classifiers (e.g., Support Vector Machine, Logistic Regression, Gaussian Naïve Bayes, Multilayer Perceptron, Bernoulli Naïve Bayes, and Decision Tree), Loss Function, MAE, MSE, RMSE, and R-square. According to the results, the proposed DNN-based model led to the highest accuracy (0.95), precision (0.97), and the lowest error rate (MAE = 0.13, MSE = 0.11, and RMSE = 0.17). Moreover, in terms of R2, the model was able to accurately predict the geotechnical indices (0.933 for UCS, 0.925 for E, 0.941 for G, 0.954 for c, and 0.921 for φ).

Characterization of Modified Natural Minerals and Rocks for Possible Adsorption and Catalytic Use.

This study focused on natural materials such as clinoptilolite (CLI), metakaolin (MK), marlstone (MRL) and phonolite (PH). Clinoptilolite is one of the most known and common natural minerals (zeolites) with a unique porous structure, metakaolin is calcined kaolin clay, marlstone is a sedimentary rock and phonolite is an igneous rock composed of alkali feldspar and other minerals. These natural materials are mainly used in the building industry (additions for concrete mixtures, production of paving, gravels) or for water purification, but the modification of their chemical, textural and mechanical properties makes these materials potentially usable in other industries, especially in the chemical industry. The modification of these natural materials and rocks was carried out by leaching using 0.1 M HCl (D1 samples) and then using 3 M HCl (D2 samples). This treatment could be an effective tool to modify the structure and composition of these materials. Properties of modified materials were determined by N2 physisorption, Hg porosimetry, temperature programmed desorption of ammonia (NH3-TPD), X-ray fluorescence (XRF), X-ray powder diffraction (XRD), diffuse reflectance infrared Fourier transform (DRIFT) and CO2 adsorption using thermogravimetric analysis (TGA). The results of N2 physisorption measurements showed that that the largest increase of specific surface area was for clinoptilolite leached using 3M HCl. There was also a significant increase of the micropore volume in the D2 samples. The only exception was marlstone, where the volume of micropores was zero even in the leached sample. Clinoptilolite had the highest acidity and sorption capacity of CO2. TGA showed that the amount of CO2 adsorbed was not significantly related to the increase in specific surface area and the opening of micropores. Hg porosimetry showed that acid leaching using 0.1 M HCl and 3 M HCl resulted in a significant increase in the macropore volume in phonolite, and during leaching using 3M HCl there was an increase of the mesopore volume. From the better properties, cost-efficient and environmental points of view, the use of these materials could be an interesting solution for catalytic and sorption applications.