Exploring the Macroscopic Properties of Humic Substances Using Modeling and Molecular Simulations
Round 1
Reviewer 1 Report
Comments to authors
This study used the Vienna Soil Organic Matter Modeler 2 (VSOMM2) to create models of 15 samples of the standard humic substances. The humic substances, humic acids or fulvic acids, correspond to different stocks of four source samples taken from soil, peat, leonardite, and blackwater river, denominated Elliott Soil, Pahokee Peat, Leonardite, and Suwannee River, respectively.
This exciting work expands the understanding of soil organic matter at a molecular level in terms of its chemical composition and macroscopic properties.
In addition, I suggest that Figures 2-10 use different symbols to indicate the experiment conditions.
Section 4.5 Extrapolation to Soil Organic Matter can expend more issues, such as the role of counterions.
Author Response
We thank the reviewer for the positive evaluation of our work. Following up on the suggestions, we have differentiated the symbols for the various conditions in the figures, to facilitate a recognition of the curves.
To address the point concerning the counter ions, we have added the following paragraph to section 4.5:
In the current models, cations were added to neutralize the molecular systems, and their important role in bridging the interactions between SOM molecules was observed. However, the concentration of further inorganic ion species in real samples will have additional effect on the interactions and dynamics of these molecules. Future work should involve a systematic study on the concentration effects of ions in the samples.
Reviewer 2 Report
• The main question considered in the research is the use of molecular dynamics simulations to give an idea of several properties of soil organic matter as a function of their chemical composition.
• The topic is original and appropriate in Agronomy and supplements a specific gap in the field for several properties of soil organic matter as a function of their chemical composition.
• Compared to other published material, the theme adds work to our understanding of Soil Organic Matter at a molecular level regarding chemical composition and macroscopic Properties.
• The study is in-depth methodologically and does not need improvement. The study is an example of the power of simulations that allows them to look at the structure and dynamics of modelled systems in detail.
• The evidence and arguments aligned with the conclusions and relate to a fundamental issue. Researchers prove that the number of carboxyl groups and their cation interactions generally determine the overall behaviour of the studied systems. They find that the cation type and pH further alter the properties, while aromatic content plays a surprisingly small role.
• The references are appropriate.
• The material is illustrated by enough tables and figures cited correctly.
Author Response
We thank the reviewer for the positive evaluation of our work.
Reviewer 3 Report
The Methods seem to be reliable, but this reviewer suggests adding more detail into the Methods section. For example, the authors write “We have tested different equilibration schemes for our system. . .” Why not provide details about these schemes here as well as other details about how the simulations were run. I see that some of these details are in sections A.2 and A.3.
Why are there no images of the models?
Also, the authors describe creating models to represent various types of humic matter, but they should note that humic matter compositions will be an average of a collection of various molecules, so one model molecule cannot represent the range of behaviors even for one type of humic acid.
The properties calculated are very useful. However, humic acids will often occur as adsorbed species even in rivers as they are bound to colloids. The authors should at least mention this as the current manuscript gives the impression that behavior is purely aqueous in nature.
It would be useful for the authors to test the structures produced for their ability to reproduce spectroscopic (e.g., IR/Raman, NMR, etc.) data. For example –
Watts, HD; Mohamed, MNA; Kubicki, JD (2011) Comparison of multi-standard and TMS-standard calculated NMR shifts for coniferyl alcohol and application of the multi-standard method to lignin dimers. J. Physical Chemistry B, 115, 1958-1970.
Trout C. C., Kubicki J. D. (2005) Correlation of observed and model vibrational frequencies for aqueous organic acids: UV resonance Raman spectra and molecular orbital calculations of benzoic, salicylic, and phthalic acids. Spectrochimica Acta Part A: Mol. & Biomol. Spectroscopy, 61, 2622-2633.
Trout C. C. and Kubicki J. D. (2004) UV resonance Raman spectra and molecular orbital calculations of salicylic and phthalic acids complexed to Al3+ in solution and on mineral surfaces. J. Phys. Chem. A, 108: 11580-11590.
Author Response
Q: The Methods seem to be reliable, but this reviewer suggests adding more detail into the Methods section. For example, the authors write “We have tested different equilibration schemes for our system. . .” Why not provide details about these schemes here as well as other details about how the simulations were run. I see that some of these details are in sections A.2 and A.3.
R: We thank the reviewer for the positive evaluation of our work.
Regarding the comments with respect to the methodology used, we note that the study of the different equilibration schemes was an extensive part of our previous work. We have emphasized this in Appendix A3 and added an explicit reference to the literature.
After the minimization steps of VSOMM2, we tested different equilibration schemes 617 consisted of a series of molecular dynamics simulations at different temperatures (420 K, 618 360 K, and 300 K) following our previous results [19]. In this work, the systems were 619 equilibrated using a scheme that corresponds to a simulation of 10 ns of equilibration at 620 420 K, followed by 10 ns at 360 K and then 15 ns at 300 K, using an initial density of 900 kg 621 m−3. All simulations were performed at a constant pressure of 1 bar.
Q: Why are there no images of the models?
R: Our models are complex mixtures of a large number of different molecules. Images of the models are highly complex and no differences between the different samples would be visible. We have added an image of one representative example in figure A1 in the appendix.
Q: Also, the authors describe creating models to represent various types of humic matter, but they should note that humic matter compositions will be an average of a collection of various molecules, so one model molecule cannot represent the range of behaviors even for one type of humic acid.
R: We fully agree with the reviewer. For this reason, the systems actually consist of 40 different molecules, each built up by 5 building blocks. While this is still a crude approximation of the real diversity of soil organic matter, it goes far beyond using a single molecule.
In Appendix A2, we added the explicit statement:
Therefore our models contain 200 building blocks in total per system, approximating the true diversity in humic substance samples.
Q: The properties calculated are very useful. However, humic acids will often occur as adsorbed species even in rivers as they are bound to colloids. The authors should at least mention this as the current manuscript gives the impression that behavior is purely aqueous in nature.
R: We agree with the reviewer that the interactions with further soil components, such as clay minerals is highly relevant for the soil. We emphasize this limitation of our models in section 4.5, where we talk about the aggregation state of soil organic matter. We have added to this section:
Furthermore, soil organic matter will rarely be observed as a single pure species, but interactions with other soil components lead to a complex matrix, consisting of soil minerals, organic matter, ionic species and water. The absorption of organic matter on clay minerals is a topic of ongoing research, to which molecular simulations will also contribute further [38].
Q: It would be useful for the authors to test the structures produced for their ability to reproduce spectroscopic (e.g., IR/Raman, NMR, etc.) data. For example – Watts, HD; Mohamed, MNA; Kubicki, JD (2011) Comparison of multi-standard and TMS-standard calculated NMR shifts for coniferyl alcohol and application of the multi-standard method to lignin dimers. J. Physical Chemistry B, 115, 1958-1970.
Trout C. C., Kubicki J. D. (2005) Correlation of observed and model vibrational frequencies for aqueous organic acids: UV resonance Raman spectra and molecular orbital calculations of benzoic, salicylic, and phthalic acids. Spectrochimica Acta Part A: Mol. & Biomol. Spectroscopy, 61, 2622-2633.
Trout C. C. and Kubicki J. D. (2004) UV resonance Raman spectra and molecular orbital calculations of salicylic and phthalic acids complexed to Al3+ in solution and on mineral surfaces. J. Phys. Chem. A, 108: 11580-11590.
R: We agree with the reviewer that this would be an important validation of the models. However, to construct the models, we rely on the use of data derived from NMR spectroscopy as input for the modeler. Subsequently comparing the reproducibility of such spectra would then lead to an unfair validation. Furthermore, the models consist of a large number (40) of molecules, of at least 75 heavy atoms. Reliably computing the spectroscopic properties of such complex molecules in their matrix with water molecules and counter ions is not an easy undertaking.
We have added in section 4.1.
VSOMM2 creates simplistic models, which represent an average set of possible organic 349 compounds presented in SOM. Validation of the models by computing spectroscopic 350 properties [34–36] would be a powerful way to confirm the validity of the models. However, 351 due to the complexity of the molecular systems, computing the vibrational or NMR spectra 352 is not a simple undertaking. Furthermore, care must be taken that the compositional 353 information from NMR experiments was directly used as input to create the models, 354 excluding these properties from a validation experiment.
