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Cation Exchange - Springer Nature

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Last Updated: 10 September 2022

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An increase of fine-root biomass in nutrient-poor soils increases soil organic matter but not soil cation exchange capacity

Purpose Fine roots are a common source of soil organic matter, but it is unclear if the cation exchange capacity of leaf-derived and root-derived organic matter is comparable. In surface soils of Cryptomeria japonica, we found that the CEC of both soil types was nearly identical, but we later discovered that in surface soils, low acid buffering capacity, fine-root biomass, and total carbon content were higher than in high-ABC soils. Methods of obtaining Surface soils at eleven C. japonica stands with contrasting ABCs divided into various density fractions: light, middle, and heavy. This ratio showed a strong negative relationship with soil pH in both LF and MF, as well as a positive relationship with fine-root biomass in MF.

Source link: https://doi.org/10.1007/s11104-022-05675-z


The performance of MCDI: the effect of sulfosuccinic acid ratio in the PVA-based cation exchange membrane

The increase of SSA in the cation exchange membrane helps reduce physical attributes such as stress at break, elongation at break, and swelling behavior, as well as cation exchange capacity. The PVA/SSA/GA membrane is also suitable for use in MCDI systems and could be used for ten cycles of desalination process without loss of adsorption capacity, indicating that the PVA/SSA/GA membrane is a reusable component that can be used in the MCDI systems.

Source link: https://doi.org/10.1007/s11581-022-04661-w


Modeling cation exchange capacity in gypsiferous soils using hybrid approach involving the artificial neural networks and ant colony optimization (ANN–ACO)

In gypsiferous soil, a hybrid strategy involving artificial neural networks and ant colony optimization for cation exchange capacity estimation was investigated. With CEC, there was a strong correlation among the soil variables revealed clay content, calcium carbonate equivalent, pH, gypsum, and soil organic matter with CEC; they were selected as input variables. Superior results were obtained in comparison to other models for the ANNu2013ACO model with 2I + 1 optimal neurons numbers in a obscure layer. The estimation function of the ANNu2013ACO model learns quickly in comparison to the conventional ANN models because the weights are optimized by ACO. With determining the relative importance of the soil variables in the CEC estimation, it's possible to transfer knowledge from the model to gypsiferous soil classification and management.

Source link: https://doi.org/10.1007/s40808-021-01344-9

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions