Mikhail Borisover

Modeling soil organic carbon (OC) contents from mid‐infrared (MIR) spectra remains challenging, and conventional multivariate spectroscopic models often lack chemical interpretability. The objective of this study was to examine whether an additive model based on non‐negative multivariate curve resolution—alternating least squares (MCR‐ALS) could reproduce variations in OC contents of whole soils and size‐separated fractions while providing a chemically interpretable description of that variation. The approach was implemented on MIR spectra of forest and woodland soils in Israel, including fine (< 50 μm) and coarse (> 50 μm) fractions representing mineral‐associated organic matter (MAOM) and particulate organic matter (POM), respectively. The OC content was represented as a sum of contributions from nonnegative MCR‐ALS spectral components, and the effects of soil fractionation and spectral range selection (4000–400 cm ⁻¹ versus optimized subranges) were evaluated. Using the full MIR spectral range, the additive model reproduced OC contents more effectively for the POM‐associated fraction than for the whole soils, explaining 71.7% and 38.2% of the observed variance, respectively, whereas no improvement was obtained for the MAOM‐associated fraction (16.1%). Optimization of the spectral range increased the explained variance to 69.1% for the whole soils and 56.1% for the MAOM‐associated fraction. Leave‐one‐out cross‐validation indicated the predictive capability of the optimized models ( Q ² = 0.625, 0.526 and 0.512 for the POM‐associated fraction, whole soils and the MAOM‐associated fraction, respectively), while bootstrap analysis supported the stability of several optimized parameter estimates. These results demonstrate the potential of spectral range optimization for effective OC modeling using nonnegative MCR‐ALS. Analysis of model performance versus the number of MCR components revealed transition‐like behavior, supporting the identification of chemically meaningful components. Overall, this study establishes a physically grounded and interpretable framework for MCR‐ALS‐based modeling of soil OC from MIR spectra, highlighting the roles of soil fractionation, optimization of continuous spectral windows, and interpretability of spectral components.

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.

Molecular modeling and molecular dynamics (MD) simulations are capable of improving our molecular-level understanding of natural organic matter (NOM) by providing new alternatives such as virtual experiments that may be difficult (or even impossible) to perform in real tests. The fine control of molecular structure required in molecular simulations is highly valuable and significant due to the fact that neither the structure nor (often) detailed composition of real NOM is known. The control of molecular structure and its educated variation guided by experimental data on 13C NMR-derived composition may be performed using Vienna Soil Organic Matter Modeller (VSOMM) [1], which allows accounting for the simultaneous presence of multiple NOM molecules of different structures. This work exploring the VSOMM is focused on examining how and whether the humic substances (HS) models representing Leonardite humic acid (LHA) can maintain stable associates in water. In this approach, the stability of HS aggregates was elucidated in the 100 ns MD simulations by varying amounts of water in a broad range, from representing "water solution in NOM" to aqueous dissolved NOM, and modifying molecular size and extent of ionization of HS models, and the type of counter-ions (Na+ vs Ca2+). Multiple properties characterizing HS-water systems have been calculated, e.g., cumulative coordination numbers, numbers of HS-HS and HS-water contacts and H-bonds at short-range distances, number and size of formed clusters as well as energies of Coulomb and Lennard-Jones interactions of HS with ions (Na+ or Ca2+), HS and water. One outcome of this modeling work is that it shows how HS dilution leads to the decomposition of HS aggregates which occurs, in particular in the presence of the Na+ counter ion, gradually. The results of this work are placed into the context of experimental data and discussion on whether the detected large HS sizes can be assigned to the presence of large aggregates and the formation of supramolecular structures [2]. Although strong interactions between HS molecules may lead to small stable aggregates (e.g., dimers) persisting during dilution, the modeling suggests that the formation and decomposition of HS associates is "a step-wise" process, and, together with experimental data on LHA dialysis proposes that large-size HS molecules (aggregated or not) may need to be taken into account while examining HS properties in aqueous solutions.[1] Escalona, Y., Petrov, D., & Oostenbrink, C. (2021). Vienna soil organic matter modeler 2 (VSOMM2). Journal of Molecular Graphics and Modelling, 103, 107817.[2] Borisover, M., Petrov, D., Oostenbrink, C., & Galicia-Andrés, E. (2025). Diluting humic substances in water in molecular dynamics simulations: Are aggregates stable? Colloids and Surfaces A: Physicochemical and Engineering Aspects, 704, 135507.

Sorption-desorption hysteresis (SDH) is often observed in liquid phase (solution) sorption experiments with various chemicals on complex natural materials, including soils and sediments. Sorption-desorption interactions with soils and sediments are of significant fundamental and applied interest since they control the transport and fate of chemicals in environmental systems. SDH expressed as a difference between sorption and desorption isotherms determined in solutions may demonstrate time-independent behavior. This work aims to propose a concept that could mechanistically explain and allow predictions of time-independent SDH in three different scenarios: (1) sorbed molecules are entrapped and physically blocked from their exchange with the environment; (2) sorbed molecules are irreversibly bound to sorbent matrix such that the sorption sites capable of irreversible binding are not fully occupied in the presence of non-zero concentrations of solutes; (3) SDH is associated with forming of a non-relaxed sorbent state where the free exchange of sorbate molecules with the environment occurs. The proposed concept introduces the gates present in a sorbent matrix and capable of concentration-dependent cooperative opening/closure, thus acting as a switch: sorbate interactions with sorption sites are allowed at increased solute concentrations but not the opposite. Coupling the gates distribution with the distribution of sorption sites allows addressing each scenario of interest and explaining time-independent SDH. The models developed within the concept can represent and even predict desorption data using a minimal number of adjustable parameters. This predictive potential may be improved by accounting for the assumptions introduced while developing the models.

A growing world population together with a decrease in water resources have resulted in a deficit in water sources for agriculture. Nations with limited water resources are already using recycled treated water as a vital water source for irrigation. In 2019, treated wastewater made up 45% of the overall agricultural water consumption in Israel. Since recycling systems work continuously and are directly connected to irrigation reservoirs, there is a constant risk of irrigation system malfunctions in the treatment process. In cases of fecal origin, this may result in pathogens entering the irrigation water, the soil, and eventually the food chain. Early warning systems for biological water contamination are important in large-scale processing, yet less studied. This work proposes such a system, with a functional prototype for irrigation-water monitoring by measuring fluorescence spectra in the range of 300 – 520 nm at excitation wavelengths of 280 nm and 340 nm, to capture both tryptophan-like fluorescence and humic-like fluorescence. The prototype was successfully tested under simulated field conditions with treated irrigation water as the base substance, and milk injections to simulate dissolved organic matter breaches. Near real-time operation enabled recording biological and chemical dynamics within the substance for the sake of monitoring drift in process control.

Microplastics are ubiquitous in aquatic systems and break down into submicron particles that can interact with aquatic toxic chemicals. These interactions may affect the detection of toxicants when using bacteria as a biomonitoring tool. This study examined the effects of model polystyrene (PS)-based submicron particles on the detection of aqueous geno- and cytotoxicity by genetically modified bioluminescent (GMB) bacteria. The toxicities were tested in three treated wastewater (TWW) effluents before and after chlorination. The PS plastics included negatively charged sulfate-coated (S-PS) and pristine (P-PS) particles of different sizes (0.1, 0.5, and 1.0 µm) that were present at different concentrations. Chlorinated or not, the S-PS and P-PS particles per se were not toxic to the GMB bacteria. However, exposure of PS particles to TWW effluents can significantly reduce the measured geno- and cytotoxicity. Adsorption of toxic compounds to polymer particles can limit the ability of the bacteria to detect those compounds. This masking effect may be mitigated by TWW chlorination, possibly due to the formation of new toxic material. Due to interactions between toxic TWW constituents and the plastics particles, water samples containing particle-associated contaminants and/or their transformation products may be declared non-toxic, based on bacterial tests as a biomonitoring tool.