- Open Access
Molecular dynamics simulation of humic substances
© Orsi 2014
- Received: 20 May 2014
- Accepted: 15 July 2014
- Published: 2 September 2014
Humic substances (HS) are complex mixtures of natural organic material which are found almost everywhere in the environment, and particularly in soils, sediments, and natural water. HS play key roles in many processes of paramount importance, such as plant growth, carbon storage, and the fate of contaminants in the environment. While most of the research on HS has been traditionally carried out by conventional experimental approaches, over the past 20 years complementary investigations have emerged from the application of computer modeling and simulation techniques. This paper reviews the literature regarding computational studies of HS, with a specific focus on molecular dynamics simulations. Significant achievements, outstanding issues, and future prospects are summarized and discussed.
- Humic substances
- Natural organic matter
- Soil organic matter
- Molecular dynamics
- Molecular modeling
- Molecular simulation
Humic substances (HS) consist of a large variety of natural organic molecules that originate from the decomposition, and related microbial activity, of dead biological material, especially plant tissues . HS are ubiquitous in the natural environment where they contribute to the regulation of many crucial ecological and environmental processes. For example, HS sustain plant growth and terrestrial life in general, and control the fate of environmental contaminants by acting as sorbents for toxic metal ions, radionuclides, and organic pollutants –. Furthermore, HS account for most of the planet’s organic material, and represent the most abundant reservoir of carbon ,. In fact, HS are receiving growing attention in recent years because of their potential role in land management strategies aimed at promoting carbon sequestration, to ultimately reduce atmospheric CO2 and hence help tackle climate change .
Over the past 20 years, traditional experimental investigation of HS has been compounded by various computer modeling and simulation approaches. This review focuses mainly on computational studies of HS conducted using the molecular dynamics method. After an introduction to the main methodological aspects, the available literature is categorized, summarized, and critically discussed.
The molecular dynamics simulation method
Molecular dynamics (MD) is a computer simulation technique which is widely used in science and engineering, and is employed to obtain equilibrium and transport properties for collections of discrete particles. MD is a powerful method to simulate matter at the molecular scale; applications can be found for a wide range of systems, from simple gases and liquids – to various complex materials including proteins –, lipid membranes –, polymers –, and carbon nanostructures –. Popular computer programs that implement MD include LAMMPS ,, GROMACS , AMBER , GROMOS , DL_POLY , and CHARMM . In this section, the main aspects of the MD method are summarized; more details can be found in dedicated books – and review articles –.
The first stage typically involves initializing the calculation by supplying the computer program with the coordinates of all atoms in the system (x), together with the models (V) which determine how the atoms interact. Such models are typically called potentials, or force fields. It should be noted that the focus of this summary is on fixed-charge biomolecular/organic force fields, as these are used predominantly in the simulation of humic substances. However, several other types of force fields exist, as documented extensively in the literature; in particular, significant progress has been recently made on polarizable models .
with Q i and Q j the corresponding charges and ε0 the permittivity of free space. In general, charges are assigned empirically to reproduce experimental observables such as known multipole moments or thermodynamic properties.
where m is the atom’s mass. Each iteration of this second stage advances the system in time by a typically small timestep (Δ t=10−15 s), and thus complete simulations normally require up to 106−109 iterations.
The third stage in Box 1 refers to the output data generated by the simulation. In particular, a trajectory is obtained consisting of consecutive snapshots of the system taken at regular time intervals during the simulation. The output trajectory is typically analyzed using statistical mechanics to obtain various thermodynamical and dynamical properties of interest, such as energy terms, average and local densities, diffusion and viscosity coefficients, mechanical parameters, and electrical potentials.
Simulations of humic substances
In this section, a number of representative molecular dynamics investigations of HS reported in the literature are reviewed. The studies considered are organized into different subsections corresponding to different types of systems investigated. It should be noted that in the literature HS are sometimes referred to as ‘NOM,’ ‘SOM,’ or ‘DOM’ ,. These acronyms stand for natural organic matter (NOM), soil organic matter (SOM), and dissolved organic matter (DOM). Specifically, NOM refers to a complex mixture of organic material that is found in water, soils, and sediments ,. SOM refers to all carbon-containing substances in soils . DOM is defined as the portion of NOM which passes through a filter of 0.45- μm pore size . For all of these three categories, HS represent a major constituent ,.
Modeling HS and their fundamental properties
Alvarez-Puebla and Garrido  studied the effect of pH on the aggregation of the TNB humic model –. By simulating the aggregation process, it was found that the molecular size increased with increased pH values due to intramolecular electrostatic repulsion, while the size of the aggregates decreased with increased pH because of increased repulsive intermolecular interactions . Alvarez-Puebla et al.  subsequently developed a modified version of the TNB model – aimed at better representing a set of experimental data on HS composition. A series of simulations were conducted to investigate HS aggregation as a function of the model’s ionic states, both in vacuo and in aqueous solution .
Leenheer et al.  developed a model of HS and used it for the interpretation of experimental data on metal-HS association. Kubicki and Apitz  later used the Leenheer model to predict equilibrium structures through classical molecular mechanics and quantum calculations and to test the effect of the specific computational methodology on the structures obtained. The Leenheer model  was also adopted by Porquet et al.  to investigate hydrogen bonding and clustering of neutral HS molecules in water.
While HS molecular models are typically constructed by assembling atoms manually into the desired compositions and geometries, an interesting alternative approach was developed by Diallo et al. , who proposed a series of structural models for soil HS by processing an extensive set of experimental data through an automated algorithm, implemented into specifically designed computational software. The model molecules obtained were relatively small, with an average molecular weight of ≈1,000 g mol −1. As opposed to the more traditional approaches, the method of Diallo et al.  has the advantage that only the appropriate isomers are selected when multiple structures can be deduced from the same set of analytical data.
The specific role of water in its interactions with HS was investigated by Aquino et al. ,. In this work, HS were represented by simple hydrocarbon chains containing hydrophilic (carboxyl) groups. The MD simulations showed that distant hydrophilic groups can be cross-linked by water molecular bridges ,.
In general, the HS models reviewed so far include molecules characterized by relatively low numbers of atoms, on the order of 100, yielding molecular weights of ≈1,000 g mol −1. However, the development of molecular models comprising substantially larger numbers of atoms has also been reported. In particular, significant work in this context has been carried out by Schulten and coworkers ,,, who proposed model HS molecules comprising over 1,000 atoms and corresponding molecular weights of up to and over 10,000 g mol −1.
Specifically, Schulten and Schnitzer  designed a SOM molecule by hydrogen bonding a humic structure to a hexapeptide and a trisaccharide, obtaining a compound with molecular formula C 342H388O124N12 and corresponding molecular weight of 6,651 g mol −1. Based on the SOM model, Schulten  subsequently proposed a model for DOM and investigated complexes with xenobiotic substances. In general, xenobiotics are substances such as pollutants or pesticides, which are found in the environment yet are not naturally expected to be present. Schulten  performed molecular simulations of systems including DOM, water, and the xenobiotic pentachlorophenol (a pesticide), atrazine (a herbicide), and DDT (an insecticide). Geometry optimization calculations were performed to analyze energetics and hydrogen bonds. It was found that van der Waals forces and hydrogen bonds were the main contributors to the temporary retention of xenobiotic substances in DOM .
Sutton et al.  refined the Schulten DOM molecule, obtaining a compound with molecular formula C 447H421O272N15S2 and corresponding molecular weight of 10,419 g mol −1. This model was then simulated under conditions of increased hydration, typical of natural soil and water environments. In particular, the DOM molecule was surrounded by water molecules corresponding to a hydration layer of ≈5 Å thickness. The systems were shown to reproduce experimental physical and chemical properties of HS for several characteristic environmental conditions of soil. Specifically, results were obtained for density, hydrogen bonds, radius of gyration, and the Hildebrand solubility parameter .
Properties of model HS molecules
HS in complex with soil minerals
In general, HS are stabilized by their association with soil minerals, which prevent microbial attack and resulting rapid decomposition of HS ,. As a consequence, HS adsorption to minerals regulates the presence of carbon in soils . An improved understanding of organo-mineral interactions is thus highly desirable, as it could lead to new strategies for soil carbon retention and sequestration through stabilization of HS.
A pioneering MD simulation study in this area was carried out by Teppen et al. , who investigated trichloroethylene (C 2HCl3), taken as a basic compound representative of organic material, adsorbed on clay mineral surfaces (kaolinite and pyrophyllite) in the presence of water. By considering different levels of hydration, it was found that water can outcompete C 2HCl3 for adsorption at the clay surface .
Shevchenko et al.  simulated organo-mineral aggregates in water using a NOM model based on an oxidized lignin-carbohydrate complex. MD simulations were conducted using the simulated annealing approach, whereby structural optimization is obtained by cooling-heating cycles which allow energy barriers to be overcome, eventually leading to optimized geometries.
Petridis et al.  modeled an Al 2O3 mineral surface in contact with the organic compounds stearic acid and glucose. The aim of this work was to study the mechanism by which glucose accumulates in a layer between Al 2O3 and stearic acid, as observed experimentally. The simulations conducted revealed that glucose deposits onto Al 2O3 driven by a lower entropic penalty with respect to stearic acid .
HS and metal ions
Sutton et al.  simulated systems including the Schulten DOM molecule , water, and the Na + and Ca 2+ ions. It was found that Ca 2+ ions associate more strongly than Na + with the carboxylate groups of the humic molecule. Moreover, Ca 2+ was shown to promote better hydration of the humic molecule .
Alvarez-Puebla et al.  studied the interaction between brown humic acid (BHA) with Cu 2+, Ni 2+, and Co 2+ ions. BHAs are the most polar and soluble components of HS, because of their high content in carboxylic and phenolic acidic groups. The BHA structure was developed based on the TNB model –. Specifically, a BHA polymer was obtained by concatenating 13 TNB monomer units. Due to the high computational cost of simulating such a large molecule, Alvarez-Puebla et al.  did not include solvating water, although its effect was approximated by introducing frictional forces through a Langevin scheme . The BHA was observed to display higher affinity for Cu 2+ (most reactive), followed by Co 2+, and then by Ni 2+ (most inert). This behavior was attributed to electrostatic retention, a mechanism consistent with both experimental and simulation results .
The study by Xu et al.  was extended by Kalinichev and Kirkpatrick  and by Iskrenova-Tchoukova et al. , who considered the Na +, Mg 2+, and Ca 2+ ions. It was found that metal-NOM binding is primarily driven by electrostatic attraction between the positive ions and the negatively charged carboxylate groups of the NOM molecule (whereas phenolic groups were not significant binding sites). Moreover, the propensity for metal-NOM aggregate formation was found to be correlated with the charge to radius ratio and the size of the ions .
A rather original methodological study was performed by Kalinichev et al. , who considered the effects of different models and system sizes on the simulation results for a NOM-Ca 2+ association process. In particular, they tested combinations of the force fields CVFF , CHARMM , and AMBER , with the water models SPC  and TIP3P . The properties considered, which included radial distribution functions and potentials of mean force, were found to be fairly robust with respect to the different model parameters used .
HS and contaminants
Antimicrobials make up a large proportion of the contaminants detected in the environment –. The occurrence of antimicrobials in soil and water is caused by their widespread use in agriculture and medicine –, as well as their presence in a wide range of healthcare and household goods –. The detrimental effects of antimicrobials include the disruption of key microbial processes in soil, toxicity to organisms, and the development of microbial resistance –. These problems are significantly mitigated when antimicrobials are adsorbed in organic matter, such as HS. To gain insights into the adsorption process, Aristilde and Sposito  carried out molecular dynamics simulations of the binding of the antimicrobial ciprofloxacin by HS. Ciprofloxacin is a frequently prescribed antibiotic commonly found in hospital wastewaters . Regarding the HS component, Aristilde and Sposito  used the Schulten DOM model ,. The simulations showed that the ciprofloxacin-HS association involved the disruption of original hydrogen bonds within the DOM molecule and their replacement with intermolecular hydrogen bonds with ciprofloxacin .
Another class of ubiquitous contaminants is represented by polycyclic aromatic hydrocarbons (PAHs), which are highly toxic compounds that form as a result of the combustion of organic fuels such as coal, oil, and natural gas. It has been shown in a number of studies that organic matter can regulate the transport, fate, degradation, and bioavailability of PAHs –. Saparpakorn et al.  investigated by simulation the binding of PAHs to different HS models; in particular, they simulated Schulten’s SOM molecule  and implemented models for earlier molecules proposed by Buffle et al.  and by Stevenson . The simulations performed aimed at quantifying the role of intermolecular interactions, as well as docking energies and binding modes .
Schulten et al.  modeled complexes of HS and the xenobiotic diethyl phtalate (DEP), with the objective of investigating the sorption process. Interactions were studied between a single HS molecule and an increasing number of DEP molecules, from 1 to 30. From their simulations, Schulten et al.  were able to quantify the sorption process in terms of the different contributions from electrostatic, van der Waals, and hydrogen bonding interactions. In particular, sorption inside free-volume pockets of HS was observed to take place between a single HS molecule and up to seven DEP molecules, whereas additional DEP molecules were adsorbed at the HS surface .
To obtain insights into the sorption of volatile organic compounds into HS, Shih et al.  studied the interaction between the TNB humic acid model – and toluene (representative volatile organic compound) in vacuo. Specifically, the diffusion coefficient of toluene was characterized as a function of temperature from 300 to 400 K. The results obtained are in qualitative agreement with experiment, in that diffusivities were observed to increase with temperature. However, the experimental data were slightly overestimated .
HS and water filtration
Satisfying the world’s population need for clean and drinking water is one of the greatest challenges of our time. To address this challenge, it is paramount to develop and optimize industrial processes aimed at filtering and desalinating sea water and municipal waste water. The currently most promising filtration technology relies on membranes operating in reverse osmosis plants. In these processes, the presence of HS is a fundamental aspect to consider. In fact, a key problem that greatly limits the efficiency of current filtration membranes is fouling, a phenomenon whereby particles deposit and accumulate on the membrane surface ultimately causing a reduction in the filtering performance. A major category of fouling agents is represented by organic substances, particularly HS –.
A number of MD studies have been devoted to different aspects of the fouling process. Ahn et al.  investigated the effects of metal ions on the adsorption of a NOM model  onto the surface of polyethersulfone membranes. It was found that divalent ions (Mg 2+ and Ca 2+) induce fouling by promoting aggregation of NOM molecules . However, the interactions between NOM and the filtration membrane were not explicitly investigated.
Myat et al.  investigated possible specific mechanisms of interaction between representative organic foulants. Specifically, they focused on the biopolymer bovine serum albumin (BSA)  and the polysaccharide sodium alginate, taken to be representative of high molecular weight compounds typically found in surface and waste waters. Moreover, they considered the TNB humic acid model – as representative of HS. No water was explicitly included. Simulations of a BSA-HS complex revealed the presence of various electrostatic and hydrophobic interactions, as well as hydrogen bonding. On the other hand, analysis of an alginate-HS complex highlighted the presence of exclusively ion-mediated interactions. The simulation results were found to be consistent with corresponding experimental data .
Achievements, issues, and future prospects
Over the past 20 years, a growing number of computer models have been developed and applied to study many important structures and processes involving humic substances (HS), including their basic molecular properties ,,–,, their aggregation behavior ,,,,, their interaction with various substances including minerals ,–,, ions ,,,–, and contaminants ,,,,,,,, and their fouling capability in relation to membrane-based water filtration technologies ,,.
These investigations yielded considerable molecular-level insights into the structure and function of HS, as summarized in the previous sections of this review. However, a few issues should be considered. In particular, it is important to bear in mind that none of the HS models developed so far correspond to real humic molecules. Rather, the models represent putative compounds obtained by assembling molecular building blocks which are known experimentally to be most prevalent in HS. Furthermore, several investigations, especially among the earliest simulations reported, focused on energy minimization calculations, with the aim of finding the most energetically favorable (optimized) conformations for a molecule or molecular aggregate ,,,,,,. However, it should be noted that energy optimization methods yield properties corresponding to a temperature of 0 K, as only the potential energy is considered, while there is no kinetic energy in the system. When temperature and thermal motion are important, as is typically the case for systems of organic and biological molecules, full MD simulations, while computationally more demanding than optimizations, are to be preferred. A final issue to highlight involves the fact that many simulation studies of HS did not include hydrating water (in vacuo assumption) ,–,,,,,,,,–. As already pointed out elsewhere ,,,, HS are hydrated in reality, and water interactions with HS are likely to influence important properties. For example, the large molecular dipole of water is expected to interact strongly with HS polar groups, and hydrogen bonds between water and HS are expected to be prevalent. The presence of appropriate amounts of water in MD simulations of HS is therefore recommended.
In terms of future prospects, there is an expectation that specific HS structures will be accurately identified from experiment, opening up opportunities for MD simulations of realistic HS compounds. As a result, simulated systems will likely become larger and more complex, and hence also more computationally expensive. While this could represent an obstacle, there are reasons to be optimistic. From a hardware perspective, the continuous increase in computational power will keep extending the attainable simulation times and sizes. Moreover, ongoing research in multiscale methods – promises to substantially improve simulation efficiency in the near future. Self-assembly simulations of large numbers of different HS molecules might soon become a reality, opening up the opportunity to study and quantify atomic-level properties within realistic HS supramolecular structures.
More generally, the study of HS in the foreseeable future will have great relevance for several areas of key global importance. Owing to the role of HS in controlling CO 2 in the ecosystem, advances in HS research could lead to new solutions for carbon capture and storage, thus contributing to address the urgent global challenge of increasingly rapid climate change . Moreover, a better understanding of HS can be instrumental in increasing food production to satisfy the needs of a growing population , as well as in optimizing filtration technologies to obtain clean and drinking water . While experimental research will always be essential, in the years to come, molecular simulations of HS are expected to become increasingly useful, particularly for providing a more detailed understanding of experimental observations, for guiding the design of new experiments, and for predicting properties and phenomena at the molecular scale.
- Stevenson FJ: Humus chemistry: genesis, composition, reactions. 1994, Wiley, HobokenGoogle Scholar
- Kördel W, Dassenakis M, Lintelmann J, Padberg S: The importance of natural organic material for environmental processes in waters and soils (technical report). Pure Appl Chem. 1997, 69 (7): 1571-1600.Google Scholar
- Kukkonen J: Binding of organic pollutants to humic and fulvic acids: influence of ph and the structure of humic material. Chemosphere. 1997, 34 (8): 1693-1704.Google Scholar
- Pignatello JJ: Soil organic matter as a nanoporous sorbent of organic pollutants. Adv Colloid Interface Sci. 1998, 76: 445-467.Google Scholar
- Lal R: Soil carbon sequestration impacts on global climate change and food security. Science. 2004, 304 (5677): 1623-1627.PubMedGoogle Scholar
- Hayes MHB, MacCarthy P, Malcolm RL, Swift R: Humic substances II. In search of structure. 1989, Wiley, HobokenGoogle Scholar
- Hayes MH, Clapp CE: Humic substances: considerations of compositions, aspects of structure, and environmental influences. Soil Sci. 2001, 166 (11): 723-737.Google Scholar
- Piccolo A: The supramolecular structure of humic substances. Soil Sci. 2001, 166 (11): 810-832.Google Scholar
- Piccolo A: The supramolecular structure of humic substances: a novel understanding of humus chemistry and implications in soil science. Adv Agronomy. 2002, 75: 57-134.Google Scholar
- Piccolo A, Conte P, Cozzolino A: Chromatographic and spectrophotometric properties of dissolved humic substances compared with macromolecular polymers. Soil Sci. 2001, 166 (3): 174-185.Google Scholar
- Piccolo A, Conte P, Trivellone E, van Lagen B, Buurman P: Reduced heterogeneity of a lignite humic acid by preparative HPSEC following interaction with an organic acid. Characterization of size-separates by Pyr-GC-MS and 1H-NMR spectroscopy. Environ Sci Technol. 2002, 36 (1): 76-84.PubMedGoogle Scholar
- Piccolo A: Aggregation and disaggregation of humic supramolecular assemblies by NMR diffusion ordered spectroscopy (DOSY-NMR). Environ Sci Technol. 2007, 42 (3): 699-706.Google Scholar
- Nebbioso A, Piccolo A: Advances in humeomics: enhanced structural identification of humic molecules after size fractionation of a soil humic acid. Analytica Chimica Acta. 2012, 720: 77-90.PubMedGoogle Scholar
- Nebbioso A, Piccolo A: Basis of a humeomics science: chemical fractionation and molecular characterization of humic biosuprastructures. Biomacromolecules. 2011, 12 (4): 1187-1199.PubMedGoogle Scholar
- Piccolo A, Nardi S, Concheri G: Micelle-like conformation of humic substances as revealed by size exclusion chromatography. Chemosphere. 1996, 33 (4): 595-602.PubMedGoogle Scholar
- Nardi S, Concheri G: Macromolecular changes of humic substances induced by interaction with organic acids. Eur J Soil Sci. 1996, 47 (3): 319-328.Google Scholar
- Wershaw RL: Molecular aggregation of humic substances. Soil Sci. 1999, 164 (11): 803-813.Google Scholar
- Stoddard SD, Ford J: Numerical experiments on stochastic behavior of a Lennard-Jones gas system. Phys Rev A. 1973, 8: 1504-1512.Google Scholar
- Adams DJ, Adams EM, Hills GJ: The computer simulation of polar liquids. Mol Phys. 1979, 38: 387-400.Google Scholar
- Sokhan VP, Tildesley DJ: The free surface of water: molecular orientation, surface potential and nonlinear susceptibility. Mol Phys. 1997, 92: 625-640.Google Scholar
- Orsi M: Comparative assessment of the ELBA coarse-grained model for water. Mol Phys. 2014, 112: 1566-1576.Google Scholar
- Vega C, Abascal JL: Simulating water with rigid non-polarizable models: a general perspective. Phys Chem. 2011, 13: 19663-19688.Google Scholar
- Mackerell AD: Empirical force fields for biological macromolecules: overview and issues. J Comput Chem. 2004, 25: 1584-1604.PubMedGoogle Scholar
- Soncini M, Vesentini S, Ruffoni D, Orsi M, Deriu MA, Redaelli A: Mechanical response and conformational changes of alpha-actinin domains during unfolding: a molecular dynamics study. Biomechan Model Mechanobiol. 2007, 6: 399-407.Google Scholar
- Deriu MA, Soncini M, Orsi M, Patel M, Essex JW, Montevecchi FM, Redaelli A: Anisotropic elastic network modeling of entire microtubules. Biophys J. 2010, 99: 2190-2199.PubMed CentralPubMedGoogle Scholar
- Parton DL, Klingelhoefer JW, Sansom MSP: Aggregation of model membrane proteins, modulated by hydrophobic mismatch, membrane curvature, and protein class. Biophys J. 2011, 101: 691-699.PubMed CentralPubMedGoogle Scholar
- Nielsen SO, Ensing B, Ortiz V, Moore PB, Klein ML: Lipid bilayer perturbations around a transmembrane nanotube: a coarse grain molecular dynamics study. Biophys J. 2005, 88: 3822-3828.PubMed CentralPubMedGoogle Scholar
- Xiang T-X, Anderson BD: Liposomal drug transport: a molecular perspective from molecular dynamics simulations in lipid bilayers. Adv Drug Deliv Rev. 2006, 58: 1357-1378.PubMedGoogle Scholar
- Orsi M, Sanderson W, Essex JW, Kettner C (2007) Molecular interactions–bringing chemistry to life. In: Hicks MG (ed), 85–205.. Beilstein-Institut, Frankfurt.Google Scholar
- Orsi M, Haubertin DY, Sanderson WE, Essex JW: A quantitative coarse-grain model for lipid bilayers. J Phys Chem B. 2008, 112: 802-815.PubMedGoogle Scholar
- Orsi M, Essex JW (2010) Molecular simulations and biomembranes: from biophysics to function. In: Biggin PC Sansom MSP (eds), 76–90.. RSC, Cambridge.Google Scholar
- Orsi M, Michel J, Essex JW (2010) Coarse-grain modelling of DMPC and DOPC lipid bilayers. J Phys: Condens Matter 22: 155106.Google Scholar
- Lyubartsev AP, Rabinovich AL: Recent development in computer simulations of lipid bilayers. Soft Matter. 2011, 7: 25-39.Google Scholar
- Orsi M, Essex JW (2011) The ELBA force field for coarse-grain modeling of lipid membranes. PLoS ONE 6: 28637.Google Scholar
- Essex JW: Physical properties of mixed bilayers containing lamellar and nonlamellar lipids: insights from coarse-grain molecular dynamics simulations. Faraday Discuss. 2013, 161: 249-272.PubMedGoogle Scholar
- Kremer K, Grest GS: Dynamics of entangled linear polymer melts: a molecular-dynamics simulation. J Chem Phys. 1990, 92 (8): 5057-5086.Google Scholar
- Varnik F, Baschnagel J, Binder K: Molecular dynamics results on the pressure tensor of polymer films. J Chem Phys. 2000, 113: 4444-4453.Google Scholar
- Rapaport DC (2002) Molecular dynamics simulation of polymer helix formation using rigid-link methods. Phys Rev E 66: 011906.Google Scholar
- Barrat J-L, Baschnagel J, Lyulin A: Molecular dynamics simulations of glassy polymers. Soft Matter. 2010, 6 (15): 3430-3446.Google Scholar
- Belytschko T, Xiao S, Schatz G, Ruoff R (2002) Atomistic simulations of nanotube fracture. Phys Rev B 65(23): 235430.Google Scholar
- Coluci VR, Pugno NM, Dantas SO, Galvao DS, Jorio A (2007) Atomistic simulations of the mechanical properties of ’super’ carbon nanotubes. Nanotechnology 18(33): 335702.Google Scholar
- Zang J, Ryu S, Pugno N, Wang Q, Tu Q, Buehler MJ, Zhao X: Multifunctionality and control of the crumpling and unfolding of large-area graphene. Nat Mater. 2013, 12 (4): 321-325.PubMed CentralPubMedGoogle Scholar
- Plimpton S: Fast parallel algorithms for short-range molecular dynamics. J Comput Phys. 1995, 117: 1-19.Google Scholar
- LAMMPS molecular dynamics simulator. . Accessed 10 June 2014., [http://lammps.sandia.gov]
- Hess B, Kutzner C, Lindahl E: Gromacs 4, algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput. 2008, 4: 435-447.PubMedGoogle Scholar
- Case DA, Darden TA, Cheatham TE, Simmerling CL, Wang J, Duke RE, Luo R, Walker RC, Zhang W, Merz KM, Roberts B, Hayik S, Roitberg A, Seabra G, Swails J, Goetz AW, Kolossváry I, Wong KF, Paesani F, Vanicek J, Wolf RM, Liu J, Wu X, Brozell SR, Steinbrecher T, Gohlke H, Cai Q, Ye X, Wang J, Hsieh MJ, et al: AMBER 12. 2012, University of California, San FranciscoGoogle Scholar
- Kunz A-PE, Allison JR, Geerke DP, Horta BAC, Hünenberger PH, Riniker S, Schmid N, van Gunsteren WF: New functionalities in the GROMOS biomolecular simulation software. J Comput Chem. 2012, 33 (3): 340-353.PubMedGoogle Scholar
- Todorov IT, Smith W, Trachenko K, Dove MT: Journal of Materials Chemistry. 2006, 16: 1911-1918.Google Scholar
- Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M: CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem. 1983, 4: 187-217.Google Scholar
- Allen MP, Tildesley DJ: Computer simulation of liquids. 1987, Oxford Science, OxfordGoogle Scholar
- Leach AR: Molecular modelling - principles and applications. 2001, Prentice Hall, HarlowGoogle Scholar
- Frenkel D, Smit B: Understanding molecular simulation. 2002, Academic, LondonGoogle Scholar
- Schlick T: Molecular modeling and simulation - an interdisciplinary guide. 2002, Springer, New YorkGoogle Scholar
- Rapaport DC: The art of molecular dynamics simulation. 2004, Cambridge University Press, CambridgeGoogle Scholar
- Sutmann G: Classical molecular dynamics. Quantum Simul Complex Many-body Syst: Theory Algorithms. 2002, 10: 211-254.Google Scholar
- Allen MP (2004) Introduction to molecular dynamics simulation23(Comput Soft Matter): 1–28.Google Scholar
- Binder K, Horbach J, Varnik F: Molecular dynamics simulations. J Phys: Condens Matter. 2004, 16: 429-453.Google Scholar
- van Gunsteren WF, Bakowies D, Baron R, Chandrasekhar I, Christen M, Daura X, Gee P, Geerke DP, Glaettli A, Huenenberger PH, Kastenholz MA, Ostenbrink C, Schenk M, Trzesniak D, van der Vegt NFA: Biomolecular modeling: goals, problems, perspectives. Angew Chem-Int Edit. 2006, 45: 4064-4092.Google Scholar
- Lopes PE, Huang J, Shim J, Luo Y, Li H, Roux B, MacKerell Jr AD: Polarizable force field for peptides and proteins based on the classical drude oscillator. J Chem Theory Comput. 2013, 9 (12): 5430-5449.PubMed CentralPubMedGoogle Scholar
- Swope WC, Andersen HC, Berens PH, Wilson KR: A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: application to small water clusters. J Chem Phys. 1982, 76: 637-649.Google Scholar
- Schulten H-R: Interactions of dissolved organic matter with xenobiotic compounds molecular modeling in water. Environ Toxicol Chem. 1999, 18 (8): 1643-1655.Google Scholar
- Sutton R, Sposito G, Diallo MS, Schulten H-R: Molecular simulation of a model of dissolved organic matter. Environ Toxicol Chem. 2005, 24 (8): 1902-1911.PubMedGoogle Scholar
- Kögel-Knabner I: The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter. Soil Biol Biochem. 2002, 34 (2): 139-162.Google Scholar
- Parsi Z, Hartog N, Górecki T: Analytical pyrolysis as a tool for the characterization of natural organic matter–a comparison of different approaches. J Anal Appl Pyrolysis. 2007, 79 (1): 9-15.Google Scholar
- Schulten H-R: Analytical pyrolysis and computational chemistry of aquatic humic substances and dissolved organic matter. J Anal Appl Pyrolysis. 1999, 49 (1): 385-415.Google Scholar
- Vepsäläinen M, Ghiasvand M, Selin J, Pienimaa J, Repo E, Pulliainen M: Investigations of the effects of temperature and initial sample pH on natural organic matter (nom) removal with electrocoagulation using response surface method (rsm). Separation Purif Technol. 2009, 69 (3): 255-261.Google Scholar
- Jansen SA, Malaty M, Nwabara S, Johnson E, Ghabbour E, Davies G, Varnum JM: Structural modeling in humic acids. Materials Sci Eng: C. 1996, 4 (3): 175-179.Google Scholar
- Davies G, Fataftah A, Cherkasskiy A, Ghabbour EA, Radwan A, Jansen SA, Kolla S, Paciolla MD, Sein Jr LT, Buermann W, Balasubramanian M, Budnick J, Xing B (1997) Tight metal binding by humic acids and its role in biomineralization. J Chem Soc Dalton Trans: 4047–4060.Google Scholar
- Kubicki J, Apitz S: Models of natural organic matter and interactions with organic contaminants. Org Geochem. 1999, 30 (8): 911-927.Google Scholar
- Sein LT, Varnum JM, Jansen SA: Conformational modeling of a new building block of humic acid approaches to the lowest energy conformer. Environ Sci Technol. 1999, 33 (4): 546-552.Google Scholar
- Alvarez-Puebla RA, Garrido JJ: Effect of pH on the aggregation of a gray humic acid in colloidal and solid states. Chemosphere. 2005, 59 (5): 659-667.PubMedGoogle Scholar
- Alvarez-Puebla R, Valenzuela-Calahorro C, Garrido J: Theoretical study on fulvic acid structure, conformation and aggregation: a molecular modelling approach. Sci Total Environ. 2006, 358 (1): 243-254.PubMedGoogle Scholar
- Leenheer J, Brown G, MacCarthy P, Cabaniss S: Models of metal binding structures in fulvic acid from the Suwannee River, Georgia. Environ Sci Technol. 1998, 32 (16): 2410-2416.Google Scholar
- Porquet A, Bianchi L, Stoll S: Molecular dynamic simulations of fulvic acid clusters in water. Colloids Surf A: Physicochem Eng Aspects. 2003, 217 (1): 49-54.Google Scholar
- Diallo MS, Simpson A, Gassman P, Faulon JL, Johnson JH, Goddard WA, Hatcher PG: 3-D structural modeling of humic acids through experimental characterization, computer assisted structure elucidation and atomistic simulations. 1. Chelsea soil humic acid. Environ Sci Technol. 2003, 37 (9): 1783-1793.PubMedGoogle Scholar
- Aquino AJ, Tunega D, Pasalic H, Schaumann GE, Haberhauer G, Gerzabek MH, Lischka H: Molecular dynamics simulations of water molecule-bridges in polar domains of humic acids. Environ Sci Technol. 2011, 45 (19): 8411-8419.PubMedGoogle Scholar
- Aquino AJ, Tunega D, Schaumann GE, Haberhauer G, Gerzabek MH, Lischka H: Study of solvent effect on the stability of water bridge-linked carboxyl groups in humic acid models. Geoderma. 2011, 169: 20-26.Google Scholar
- Schulten H-R, Schnitzer M: Chemical model structures for soil organic matter and soils. Soil Sci. 1997, 162 (2): 115-130.Google Scholar
- Sutton R, Sposito G: Molecular simulation of humic substance–Ca-montmorillonite complexes. Geochimica et Cosmochimica Acta. 2006, 70 (14): 3566-3581.Google Scholar
- von Lützow M, Kögel-Knabner I, Ekschmitt K, Flessa H, Guggenberger G, Matzner E, Marschner B: Som fractionation methods: relevance to functional pools and to stabilization mechanisms. Soil Biol Bioch. 2007, 39 (9): 2183-2207.Google Scholar
- Teppen BJ, Yu C-H, Miller DM, Schäfer L: Molecular dynamics simulations of sorption of organic compounds at the clay mineral/aqueous solution interface. J Comput Chem. 1998, 19 (2): 144-153.Google Scholar
- Shevchenko SM, Bailey GW, Akim LG: The conformational dynamics of humic polyanions in model organic and organo-mineral aggregates. J Mol Struct: THEOCHEM. 1999, 460 (1): 179-190.Google Scholar
- Petridis L, Ambaye H, Jagadamma S, Kilbey SM, Lokitz BS, Lauter V, Mayes M: Spatial arrangement of organic compounds on a model mineral surface: implications for soil organic matter stabilization. Environ Sci Technol. 2013, 48: 79-84.PubMedGoogle Scholar
- Tipping E: Cation Binding by Humic Substances. 2002, Cambridge University Press, CambridgeGoogle Scholar
- Cation binding by humic substances. Environ Geol. 2003, 43: 615-616.Google Scholar
- Alvarez-Puebla RA, Valenzuela-Calahorro C, Garrido JJ: Retention of co(ii), Ni(ii), and Cu(ii) on a purified brown humic acid. Modeling and characterization of the sorption process. Langmuir. 2004, 20 (9): 3657-3664. PMID:15875396PubMedGoogle Scholar
- Schneider T, Stoll E (1978) Molecular-dynamics study of a three-dimensional one-component model for distortive phase transitions17(Phys Rev B): 1302–1322.Google Scholar
- Xu X, Kalinichev AG: 133Cs and 35Cl NMR spectroscopy and molecular dynamics modeling of Cs + and Cl − complexation with natural organic matter. Geochimica et Cosmochimica Acta. 2006, 70 (17): 4319-4331.Google Scholar
- Kalinichev A, Kirkpatrick R: Molecular dynamics simulation of cationic complexation with natural organic matter. Eur J Soil Sci. 2007, 58 (4): 909-917.Google Scholar
- Iskrenova-Tchoukova E, Kalinichev AG, Kirkpatrick RJ: Metal cation complexation with natural organic matter in aqueous solutions: molecular dynamics simulations and potentials of mean force. Langmuir. 2010, 26 (20): 15909-15919.PubMedGoogle Scholar
- Kalinichev AG, Iskrenova-Tchoukova E, Clark MM, Ahn W-Y, Kirkpatrick RJ: Effects of Ca 2+ on supramolecular aggregation of natural organic matter in aqueous solutions: a comparison of molecular modeling approaches. Geoderma. 2011, 169: 27-32.Google Scholar
- Dauber-Osguthorpe P, Roberts VA, Osguthorpe DJ, Wolff J, Genest M, Hagler AT: Structure and energetics of ligand binding to proteins: Escherichia coli dihydrofolate reductase-trimethoprim, a drug-receptor system. Proteins: Struct Funct Bioinformatics. 1988, 4 (1): 31-47.Google Scholar
- Bashford D, Bellott M, Evanseck JD, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph-McCarthy D, Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T, Nguyen DT, Prodhom B, Reiher WEIII, Roux B, Schlenkrich M, Smith JC, Stote R, Straub J, Watanabe M, Wiorkiewicz-Kuczera J, Yin D, Karplus M: All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B. 1998, 102: 3586-3616.PubMedGoogle Scholar
- Case DA, Darden TA, Cheatham TE, Simmerling CL, Wang J, Duke RE, Luo R, Merz KM, Pearlman DA, Crowley M, Walker RC, Zhang W, Wang B, Hayik S, Roitberg A, Seabra G, Wong KF, Paesani F, Wu X, Brozell S, Tsui V, Gohlke H, Yang L, Tan C, Mongan J, Hornak V, Cui G, Beroza P, Mathews DH, Schafmeister C, et al: Amber 9. 2006, University of California, San FranciscoGoogle Scholar
- Berendsen HJC, Postma JPM, van Gunsteren WF, Hermans J (1981) Intermolecular Forces(Pullman B, ed.), Reidel, Dordrecht.Google Scholar
- Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML: Comparison of simple potential functions for simulating liquid water. J Chem Phys. 1983, 79: 926-935.Google Scholar
- Kolpin DW, Furlong ET, Meyer MT, Thurman EM, Zaugg SD, Barber LB, Buxton HT: Pharmaceuticals, hormones, and other organic wastewater contaminants in US streams, 1999-2000: a national reconnaissance. Environ Sci Technol. 2002, 36: 1202-1211.PubMedGoogle Scholar
- Halden RU, Paull DH: Co-occurrence of triclocarban and triclosan in US water resources. Environ Sci Technol. 2005, 39: 1420-1426.PubMedGoogle Scholar
- Higgins CP, Paesani ZJ, Chalew TEA, Halden RU: Bioaccumulation of triclocarban in Lumbriculus variegatus. Environ Toxicol Chem. 2009, 28: 2580-2586.PubMed CentralPubMedGoogle Scholar
- Boxall AB, Kolpin DW, Halling-Sørensen B, Tolls J: Peer reviewed: are veterinary medicines causing environmental risks?. Environ Sci Technol. 2003, 37 (15): 286-294.Google Scholar
- Boxall AB, Johnson P, Smith EJ, Sinclair CJ, Stutt E, Levy LS: Uptake of veterinary medicines from soils into plants. J Agric Food Chem. 2006, 54 (6): 2288-2297.PubMedGoogle Scholar
- Lee LS, Carmosini N, Sassman SA, Dion HM, Sepulveda MS: Agricultural contributions of antimicrobials and hormones on soil and water quality. Adv Agronomy. 2007, 93: 1-68.Google Scholar
- Kümmerer K: Significance of antibiotics in the environment. J Antimicrob Chemother. 2003, 52 (1): 5-7.PubMedGoogle Scholar
- Schweizer HP: Triclosan: a widely used biocide and its link to antibiotics. FEMS Microbiol Lett. 2001, 202: 1-7.PubMedGoogle Scholar
- Chalew TEA, Halden RU: Environmental exposure of aquatic and terrestrial biota to triclosan and triclocarban. J Am Water Resour Assoc. 2009, 45: 4-13.Google Scholar
- Orsi M, Noro MG, Essex JW: Dual-resolution molecular dynamics simulation of antimicrobials in biomembranes. J R Soc Interface. 2011, 8: 826-841.PubMed CentralPubMedGoogle Scholar
- Aiello AE, Larson EL, Levy SB: Consumer antibacterial soaps: effective or just risky?. Clin Infect Dis. 2007, 45: 137-147.Google Scholar
- Aryal N, Reinhold DM: Phytoaccumulation of antimicrobials from biosolids: impacts on environmental fate and relevance to human exposure. Water Res. 2011, 45 (17): 5545-5552.PubMedGoogle Scholar
- Oliver SP, Murinda SE, Jayarao BM: Impact of antibiotic use in adult dairy cows on antimicrobial resistance of veterinary and human pathogens: a comprehensive review. Foodborne Pathogens Disease. 2011, 8 (3): 337-355.PubMedGoogle Scholar
- Marshall BM, Levy SB: Food animals and antimicrobials: impacts on human health. Clin Microbiol Rev. 2011, 24 (4): 718-733.PubMed CentralPubMedGoogle Scholar
- Aristilde L, Sposito G: Binding of ciprofloxacin by humic substances: a molecular dynamics study. Environ Toxicol Chem. 2010, 29 (1): 90-98.PubMedGoogle Scholar
- Hartmann A, Alder AC, Koller T, Widmer RM: Identification of fluoroquinolone antibiotics as the main source of umuC, genotoxicity in native hospital wastewater. Environ Toxicol Chem. 1998, 17 (3): 377-382.Google Scholar
- Haigh SD: A review of the interaction of surfactants with organic contaminants in soil. Sci Total Environ. 1996, 185 (1): 161-170.Google Scholar
- Käcker T, Haupt ET, Garms C, Francke W, Steinhart H: Structural characterisation of humic acid-bound pah residues in soil by 13c-cpmas-nmr-spectroscopy: evidence of covalent bonds. Chemosphere. 2002, 48 (1): 117-131.PubMedGoogle Scholar
- Laor Y, Rebhun M: Evidence for nonlinear binding of PAHs to dissolved humic acids. Environ Sci Technol. 2002, 36 (5): 955-961.PubMedGoogle Scholar
- Golobočanin DD, Škrbić BD, Miljević NR (2004) Principal component analysis for soil contamination with pahs Chemometrics Intell Lab Syst72(2): 219–223.Google Scholar
- Zhou W, Zhu L: Distribution of polycyclic aromatic hydrocarbons in soil–water system containing a nonionic surfactant. Chemosphere. 2005, 60 (9): 1237-1245.PubMedGoogle Scholar
- Zhang H, Luo Y, Wong M, Zhao Q, Zhang G: Distributions and concentrations of pahs in Hong Kong soils. Environ Pollut. 2006, 141 (1): 107-114.PubMedGoogle Scholar
- Saparpakorn P, Kim JH, Hannongbua S: Investigation on the binding of polycyclic aromatic hydrocarbons with soil organic matter: a theoretical approach. Molecules. 2007, 12 (4): 703-715.PubMedGoogle Scholar
- Buffle J, Greter FL, Haerdi W: Measurement of complexation properties of humic and fulvic acids in natural waters with lead and copper ion-selective electrodes. Anal Chem. 1977, 49 (2): 216-222.PubMedGoogle Scholar
- Schulten H-R, Thomsen M, Carlsen L: Humic complexes of diethyl phthalate: molecular modelling of the sorption process. Chemosphere. 2001, 45 (3): 357-369.PubMedGoogle Scholar
- Wang Z, Chen J, Sun Q, Peijnenburg WJ: C 60-dom interactions and effects on c 60 apparent solubility A molecular mechanics and density functional theory study. Environ Int. 2011, 37 (6): 1078-1082.PubMedGoogle Scholar
- Bosi S, Spalluto G, Prato M: Fullerene derivatives: an attractive tool for biological applications. Eur J Med Chem. 2003, 38: 913-923.PubMedGoogle Scholar
- Nakamura E, Isobe H: Functionalized fullerenes in water. The first 10 years of their chemistry, biology, and nanoscience. Acc Chem Res. 2003, 36: 807-815.PubMedGoogle Scholar
- Oberdörster G, Sharp Z, Atudorei A, Elder V, Gelein R, Kreyling W, Cox C: Translocation of inhaled ultrafine particles to the brain. Inhal Toxicol. 2004, 16: 437-445.PubMedGoogle Scholar
- Sayes CM, Fortner JD, Guo W, Lyon D, Boyd AM, Ausman KD, Tao YJ, Sitharaman B, Wilson LJ, Hughes JB, West JL, Colvin VL: The differential cytotoxicity of water-soluble fullerenes. Nano Lett. 2004, 4: 1881-1887.Google Scholar
- Sun Q, Xie H-B, Chen J, Li X, Wang Z: Molecular dynamics simulations on the interactions of low molecular weight natural organic acids with C 60. Chemosphere. 2013, 92 (4): 429-434.PubMedGoogle Scholar
- Wu F, Bai Y, Mu Y, Pan B, Xing B: Fluorescence quenching of fulvic acids by fullerene in water. Environ Pollut. 2013, 172: 100-107.PubMedGoogle Scholar
- Molecular dynamic simulations of the sorption of toluene in a dry humic acid model: a preliminary study. Colloids Surf A: Physicochem Eng Aspects. 2006, 275 (1): 183-186.Google Scholar
- Lee S, Cho J, Elimelech M: Combined influence of natural organic matter (nom) and colloidal particles on nanofiltration membrane fouling. J Membrane Sci. 2005, 262 (1): 27-41.Google Scholar
- Wang Z, Zhao Y, Wang J, Wang S: Studies on nanofiltration membrane fouling in the treatment of water solutions containing humic acids. Desalination. 2005, 178 (1): 171-178.Google Scholar
- Li Q, Elimelech M: Synergistic effects in combined fouling of a loose nanofiltration membrane by colloidal materials and natural organic matter. J Membrane Sci. 2006, 278 (1): 72-82.Google Scholar
- Jarusutthirak C, Mattaraj S, Jiraratananon R: Influence of inorganic scalants and natural organic matter on nanofiltration membrane fouling. J Membrane Sci. 2007, 287 (1): 138-145.Google Scholar
- Her N, Amy G, Chung J, Yoon J, Yoon Y: Characterizing dissolved organic matter and evaluating associated nanofiltration membrane fouling. Chemosphere. 2008, 70 (3): 495-502.PubMedGoogle Scholar
- Xiang Y, Liu Y, Mi B, Leng Y: Hydrated polyamide membrane and its interaction with alginate: a molecular dynamics study. Langmuir. 2013, 29 (37): 11600-11608.PubMedGoogle Scholar
- Ahn W-Y, Kalinichev AG, Clark MM: Effects of background cations on the fouling of polyethersulfone membranes by natural organic matter: experimental and molecular modeling study. J Membrane Sci. 2008, 309 (1): 128-140.Google Scholar
- Hughes ZE, Gale JD: A computational investigation of the properties of a reverse osmosis membrane. J Mater Chem. 2010, 20 (36): 7788-7799.Google Scholar
- Hughes ZE, Gale JD: Molecular dynamics simulations of the interactions of potential foulant molecules and a reverse osmosis membrane. J Mater Chem. 2012, 22 (1): 175-184.Google Scholar
- Myat DT, Stewart MB, Mergen M, Zhao O, Orbell JD, Gray S: Experimental and computational investigations of the interactions between model organic compounds and subsequent membrane fouling. Water Res. 2014, 48: 108-118.PubMedGoogle Scholar
- Majorek KA, Porebski PJ, Dayal A, Zimmerman MD, Jablonska K, Stewart AJ, Chruszcz M, Minor W: Structural and immunologic characterization of bovine, horse, and rabbit serum albumins. Mol Immunol. 2012, 52 (3): 174-182.PubMed CentralPubMedGoogle Scholar
- Momany FA, Dombrink-Kurtzman MA: Molecular dynamics simulations on the mycotoxin fumonisin B1. J Agric Food Chem. 2001, 49 (2): 1056-1061.PubMedGoogle Scholar
- Mahfoud R, Maresca M, Santelli M, Pfohl-Leszkowicz A, Puigserver A, Fantini J: pH-dependent interaction of fumonisin B1 with cholesterol physicochemical and molecular modeling studies at the air-water interface. J Agric Food Chem. 2002, 50 (2): 327-331.PubMedGoogle Scholar
- Thiele-Bruhn S: Molecular modeling of soil organic matter: squaring the circle?. Geoderma. 2011, 166 (1): 1-14.Google Scholar
- Schulten H-R: The three-dimensional structure of humic substances and soil organic matter studied by computational analytical chemistry. Fresenius’ J Anal Chem. 1995, 351 (1): 62-73.Google Scholar
- Schulten H-R: The three-dimensional structure of soil organo-mineral complexes studied by analytical pyrolysis. J Anal Appl Pyrolysis. 1995, 32: 111-126.Google Scholar
- Schulten H-R, Leinweber P: Characterization of humic and soil particles by analytical pyrolysis and computer modeling. J Anal Appl Pyrolysis. 1996, 38 (1): 1-53.Google Scholar
- Shevchenko SM, Bailey GW: Non-bonded organo-mineral interactions and sorption of organic compounds on soil surfaces: a model approach. J Mol Struct: Theochem. 1998, 422 (1): 259-270.Google Scholar
- Schulten H, Leinweber P, Schnitzer M, Huang P, Senesi N, Buffle J: Analytical pyrolysis and computer modelling of humic and soil particles. Environmental particles: structure and surface reactions of soil particles. 1998, Wiley, Chichester, 281-324.Google Scholar
- Schulten H-R, Leinweber P: New insights into organic-mineral particles: composition, properties and models of molecular structure. Biol Fertil Soils. 2000, 30 (5–6): 399-432.Google Scholar
- Johnson J (2001) Binding of hydrophobic organic compounds to dissolved humic substances: a predictive approach based on computer assisted structure elucidation, atomistic simulations and Flory-Huggins solution theory. Humic Subst Struct Models Funct 273: 221.Google Scholar
- Kubicki J (2000) Molecular modeling of humic and fulvic acid. In: Abstracts of Papers of the American Chemical Society, 361–361. vol. 220.Google Scholar
- Ayton GS, Noid WG, Voth GA: Multiscale modeling of biomolecular systems: in serial and in parallel. Curr Opin Struct Biol. 2007, 17: 192-198.PubMedGoogle Scholar
- Sherwood P, Brooks BR, Sansom MSP: Multiscale methods for macromolecular simulations. Curr Opin Struct Biol. 2008, 18: 630-640.PubMedGoogle Scholar
- Michel J, Orsi M, Essex JW: Prediction of partition coefficients by multiscale hybrid atomic level/coarse-grain simulations. J Phys Chem B. 2008, 112: 657-660.PubMedGoogle Scholar
- Orsi M, Sanderson WE, Essex JW: Permeability of small molecules through a lipid bilayer: a multiscale simulation study. J Phys Chem B. 2009, 113: 12019-12029.PubMedGoogle Scholar
- Orsi M, Essex JW: Permeability of drugs and hormones through a lipid bilayer: insights from dual-resolution molecular dynamics. Soft Matter. 2010, 6: 3797-3808.Google Scholar
- Kamerlin SCL, Vicatos S, Dryga A, Warshel A: Coarse-grained (multiscale) simulations in studies of biophysical and chemical systems. Annu Rev Phys Chem. 2011, 62: 41-64.PubMedGoogle Scholar
- Chen Y, Aviad T (1990) Effects of humic substances on plant growth. In: Humic substances in soil and crop sciences: selected readings (humic substances), Soil Science Society of America, 161–186, USA.Google Scholar
- Matilainen A, Gjessing ET, Lahtinen T, Hed L, Bhatnagar A: An overview of the methods used in the characterisation of natural organic matter (nom) in relation to drinking water treatment. Chemosphere. 2011, 83 (11): 1431-1442.PubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.