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Minerals
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Clays in Soil Science and Soil Chemistry
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Clays in Soil Science and Soil Chemistry

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Clays and Engineered Mineral Materials".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1918

Special Issue Editors

Dr. Edgar Galicia-Andrés

E-Mail Website
Guest Editor
Institute of Molecular Modeling and Simulation, Department of Material Sciences and Process Engineering, University of Natural Resources and Life Sciences, Vienna (BOKU), Muthgasse 18, 1190 Vienna, Austria
Interests: the behavior of complex fluids with interfacial and biological activity with potential applications in material science; soil chemistry; geochemistry and environmental chemistry fields using computer simulation techniques; atomistic and coarse-grained models
Prof. Dr. Daniel Tunega

E-Mail Website
Guest Editor
Institute for Soil Research, BOKU University, 1180 Vienna, Austria
Interests: application of molecular modeling methods (from force-field-based to quantum chemical methods) in the field of material chemistry; environmental chemistry; soil chemistry and geochemistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Clays and clay minerals belong to a rich family of hydrous aluminum phyllosilicates, formed in nature by the weathering transformation of primary silicate minerals. They are variable in chemical composition, and their properties highly dependent on their origin material and location. Clays are not only found naturally but can also be prepared synthetically. Due to their unique structures and properties, they have many applications, such as smart materials in material chemistry and the cosmetic, pharmaceutical, and construction industries. Their high abundance worldwide makes them an essential component of soils, where they play an important role in numerous soil processes. This makes clays of particular interest in fields such as soil science and soil chemistry.

This Special Issue will study the impacts of clays and clay minerals in soils by considering the behavior and interactions of clays with components of organic and inorganic nature, such as soil organic matter, heavy metals and pesticides, as contaminants. This Special Issue welcomes different perspectives from computer simulations methods such as quantum chemical, classical molecular dynamics, Monte Carlo and Machine Learning models, as well as experimental approaches, such as microcalorimetry, nano-SIMS, AFM, and XANES measurements, and a combination of both approaches.

Dr. Edgar Galicia-Andrés
Prof. Dr. Daniel Tunega
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • clay minerals
  • soil organic matter
  • organic-mineral interactions
  • soil pollutants
  • molecular modeling

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Published Papers (2 papers)

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16 pages, 934 KB  
Article
Data-Fusion MCR-ALS of IHSS Humic Substances: Quantitative Integration of 13C NMR, Elemental, and Acidic Characteristics into Endmember Compositional Motifs for Molecular Modeling
by Mikhail Borisover and Marcos Lado
Minerals 2026, 16(3), 228; https://doi.org/10.3390/min16030228 - 25 Feb 2026
Viewed by 514
Abstract
Realistic atomistic modeling of mineral and soil systems requires chemically meaningful representations of organic matter (OM). Bulk 13C nuclear magnetic resonance (NMR) data have been proposed as compositional inputs for stochastic generation of OM structures, and prior studies using nonnegative multivariate curve [...] Read more.
Realistic atomistic modeling of mineral and soil systems requires chemically meaningful representations of organic matter (OM). Bulk 13C nuclear magnetic resonance (NMR) data have been proposed as compositional inputs for stochastic generation of OM structures, and prior studies using nonnegative multivariate curve resolution (MCR) suggested that bulk 13C NMR spectra of OM may be represented as mixtures of only a few components. However, these studies typically relied on single-block decompositions and did not explicitly assess decomposition uniqueness. The objective of this work was to examine whether a quantitative and chemically interpretable nonnegative MCR decomposition of OM can be obtained while explicitly evaluating (1) residual rotational ambiguity controlling the uniqueness of components, and (2) the variance captured by the decomposition. Using a dataset of International Humic Substances Society (IHSS) humic acids, fulvic acids, and aquatic OM, we applied single- and multi-block nonnegative MCR–alternating least squares (ALS) analyses integrating 13C NMR spectra, elemental composition (C, H, O, N, S), and titratable carboxylic and phenolic group contents. The multi-block approach effectively narrowed the feasible solution space and enriched the chemical characterization of the resulting MCR components. Across all analytical blocks, two chemically distinct components, an aromatic-rich and an aliphatic-rich motifs, consistently emerged, together explaining ~97–98% of the total variance and exhibiting near-zero residual rotational ambiguity. These findings support that diverse OM types can be represented quantitatively as mixtures of a small set of unique recurring compositional motifs. These motifs serve as ensemble-level averages whose underlying molecular diversity may vary substantially across materials. They provide quantitative, chemically justified inputs for molecular modeling of mineral–OM systems, which could contribute to chemical interpretability of modeling and provide better mechanistic insights into OM variation across diverse sample series. Full article
(This article belongs to the Special Issue Clays in Soil Science and Soil Chemistry)
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11 pages, 470 KB  
Article
Machine Learning-Based Prediction of Boron Desorption in Acidic Tea-Growing Soils
by Fatih Gökmen
Minerals 2026, 16(2), 219; https://doi.org/10.3390/min16020219 - 22 Feb 2026
Viewed by 648
Abstract
In acidic tea soil, boron (B) adsorption and desorption processes are dominated by the complex relationship between soil acidity, mineralogy, and organic matter. This study investigated B adsorption–desorption behavior in five acidic tea soils (pH 3.8–5.6) collected from the Eastern Black Sea region [...] Read more.
In acidic tea soil, boron (B) adsorption and desorption processes are dominated by the complex relationship between soil acidity, mineralogy, and organic matter. This study investigated B adsorption–desorption behavior in five acidic tea soils (pH 3.8–5.6) collected from the Eastern Black Sea region of Türkiye and evaluated the potential of machine learning (ML) algorithms to predict B desorption. Laboratory batch experiments were conducted using five initial B concentrations, and adsorption data were interpreted using the Langmuir isotherm model. Adsorption experiments indicated that B interacted with Fe/Al-oxide-containing clay minerals, which had low but favorable binding affinity, as indicated by Langmuir maximum adsorption capacities (Qmax) ranging from 46.5 to 181.8 mg kg−1. Desorption experiments revealed a high degree of reversibility, particularly in soils with lower adsorption capacities, ensuring potential B leaching. To capture the governing B desorption, six machine learning (ML) algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Regression (SVR), Gaussian Process Regression (GP), Elastic Net Regression (EN), and Multivariate Adaptive Regression Splines (MARS)—were trained on 75 data points. Among the tested models, Elastic Net showed the highest predictive accuracy (R2 = 0.735). This model does not replace adsorption experiments. It offers a within-assay determination of desorption given measured adsorption, which may reduce the requirement for separate desorption equilibration and analyses. Permutation importance analysis identified B_ads as the dominant predictor of B desorption, with smaller contributions from pH_ads and EC_ads. The results demonstrate that integrating laboratory experiments with machine learning provides an effective framework for predicting B mobility in acidic tea soils, offering a parameterized experimental framework for describing boron desorption behavior in acidic tea soils. Full article
(This article belongs to the Special Issue Clays in Soil Science and Soil Chemistry)
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