A Systematic Coarse-Grained Model for Methylcellulose Polymers: Spontaneous Ring Formation at Elevated Temperature

Huang, Wenjun; Ramesh, Rahul; Jha, Prateek K.; Larson, Ronald G.

doi:10.1021/acs.macromol.5b02373

Cited by 54 publications

(86 citation statements)

References 50 publications

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“…Accordingly, the original CG potentials exhibit multiple valleys. For such cases, the method by Huang et al cannot be used to parameterize the bonded CG potentials, which requires well resolved single peaks in the probability distribution functions of CG bond and angle. To bypass such complex features, the original bonded CG potentials were fitted by the analytical functions (Equation (3)), as shown in Figure .…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, the bonded CG potentials are summations of the functions of the bond length l , the bond angle θ, and the dihedral angle φ, as follows

U_{bonded}^{CG} = \sum U_{normalb} + \sum U_{normala} + \sum U_{normald}

whereas the nonbonded CG potentials are van der Waals interactions regarding that each super‐atom is charge‐neutral and thus the electrostatic Coulomb interactions can be ignored. According to the previous works, it seems that the analytical potentials are better to exhibit good transferability than the numerical (i.e., IBI, usually tabulated) potentials. In one recent paper by Huang et al, some advantages using the analytical potentials were summarized.…”

Section: Computational Detailsmentioning

confidence: 99%

“…According to the previous works, it seems that the analytical potentials are better to exhibit good transferability than the numerical (i.e., IBI, usually tabulated) potentials. In one recent paper by Huang et al, some advantages using the analytical potentials were summarized. For convenience, the harmonic functions are employed for the CG bond and angle potentials, and the Ryckaert–Bellemans functions are used for the CG dihedral potentials; the shifted Lennard–Jones (LJ) 12–6 potential forms are adopted for the nonbonded CG potentials, as follows

U_{normalb} (l) = \frac{k_{normalb}}{2} {(l - l_{0})}^{2}

U_{normala} (θ) = \frac{k_{normala}}{2} {(θ - θ_{0})}^{2}

U_{normald} (ϕ) = \sum_{n normal= 0}^{5} C_{n} {(\cos (ϕ - 180 °))}^{2}

U_{n b} (r) = 4 ε ({(\frac{σ}{r})}^{12} - {(\frac{σ}{r})}^{6}) + S_{LJ} (r)

where l 0 , θ 0 , φ 0 , C 0 – C 5 , σ, ε are the parameters to be derived, and the S LJ is a shifted function to smooth the nonbonded CG potentia...…”

Section: Computational Detailsmentioning

confidence: 99%

“…By contrast, the IBI potentials that are parameterized to match the structural distributions cannot surely reproduce the thermomechanical properties of the systems . Note that the analytical CG potentials have been increasingly employed in the recent multiscale simulations of polymers . In particular, some studies employed the nonstandard LJ potentials (i.e., LJ9‐6, LJ7‐4, LJ4‐6) that were softer than the standard LJ12‐6 potentials.…”

Section: Computational Detailsmentioning

confidence: 99%

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Multiscale Modeling Scheme for Simulating Polymeric Melts: Application to Poly(Ethylene Oxide)

2017

Macro Theory & Simulations

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polyolefins, from which the volumetric expansive coefficients and glass transition temperatures (T g ) could be determined. Up to now, this method has been extensively applied to complex polymers, [4] including dendrimers [5,6] and crosslinked networks, [7,8] to examine effects of various factors (i.e., molecular weight, [9] tacticity, [10] confinement [11] ) on the volumetric properties.However, in order to achieve repeatable results, adequate thermodynamics equilibrium should be usually attained in these AA MD simulations. Although a great variety of schemes have been proposed to improve the sampling efficiency, restrained by the current computational power, the productive AA MD simulations are only generally suitable for investigating short-term behaviors of small short-chain polymer (namely, oligomer) systems. To study specific polymer systems at the wide timeand space-scales, one of quite promising schemes is multiscale modeling, which groups every AA segment into one super-atom or coarse-grained (CG) bead. [12,13] In this scheme, it is critical to obtain the "potential energy" (referred to free energy or potential of mean force in most cases) that can accurately describe chain structure and interbead interactions. [14,15] Numerous CG methods have been developed, [16] among which most typical ones include iterative Boltzmann inversion (IBI), [17] force match, [18][19][20] inverse Monte Carlo, [21] conditional reversible work (CRW), [22,23] and MARTINI, [24] etc. Mainly, these methods differ in the target functions that are used to parameterize the nonbonded CG potentials. For example, the IBI method [17] adopts the structural distributions, the CRW method [22] uses the interaction free energy, and the MARTINI method [24] aims to reproduce the partitioning free energies.In general, the CG potentials parameterized until now suffer from representability and transferability. [16,25,26] Namely, even at the same state as the parameterized one with some properties, simulations with these CG potentials cannot reproduce other properties; and CG potentials that are parameterized at some state cannot reliably be used at other states. In order to improve the predictive power of CG simulations, numerous studies have been carried out during the past decade. Here only some of typical ones are mentioned. As one of early works, Qian et al. [27] modified the IBI potentials by a temperature factor to reproduce the thermal expansion coefficients over a broad range of temperature. This method was called one-point extrapolation Multiscale SimulationsThe poly(ethylene oxide) (PEO) is employed as one typical example to demonstrate a new multiscale modeling scheme for simulating high-molecularweight polymeric melts. In this scheme, the structural distributions and the densities at five elevated temperatures at 1 atm, which are obtained from molecular dynamics (MD) simulations of all-atomistic oligomeric melt, are employed as the target functions to parameterize the coarse-grained (CG) potentials. The extensive CG MD simulations rep...

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Section: Resultsmentioning

confidence: 99%

“…Furthermore, the bonded CG potentials are summations of the functions of the bond length l , the bond angle θ, and the dihedral angle φ, as follows

U_{bonded}^{CG} = \sum U_{normalb} + \sum U_{normala} + \sum U_{normald}

Section: Computational Detailsmentioning

confidence: 99%

U_{normalb} (l) = \frac{k_{normalb}}{2} {(l - l_{0})}^{2}

U_{normala} (θ) = \frac{k_{normala}}{2} {(θ - θ_{0})}^{2}

U_{normald} (ϕ) = \sum_{n normal= 0}^{5} C_{n} {(\cos (ϕ - 180 °))}^{2}

U_{n b} (r) = 4 ε ({(\frac{σ}{r})}^{12} - {(\frac{σ}{r})}^{6}) + S_{LJ} (r)

where l 0 , θ 0 , φ 0 , C 0 – C 5 , σ, ε are the parameters to be derived, and the S LJ is a shifted function to smooth the nonbonded CG potentia...…”

Section: Computational Detailsmentioning

confidence: 99%

Section: Computational Detailsmentioning

confidence: 99%

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Multiscale Modeling Scheme for Simulating Polymeric Melts: Application to Poly(Ethylene Oxide)

2017

Macro Theory & Simulations

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“…Studying the self-assembly of model diblock copolymers having unsubstituted glucose/cellobiose as hydrophilic heads and regioselectively methylated or permethylated glucose units as hydrophobic tails with a total DP of ≤ 28, Nakagawa et al [5] showed the necessity of a sequence of at least 10 permethylated glucosyl units for gelation of methylcellulose to happen. Fibril formation prior to gelation was also observed and further studied using a systematic coarsegrained model [6][7][8][9][10][11][12][13][14]. Taking into account the role of highly methylated glucose units in thermogelation of methylcellulose, Adden et al [15] suggested the cationic ring-opening polymerization of differently substituted cyclodextrins as a potential method for synthesis of glucan ether block copolymers having blocks of permethylated glucose units.…”

Section: Introductionmentioning

confidence: 99%

1,4-D-Glucan block copolymers: synthesis and comprehensive structural characterization

Hashemi

Mischnick

2020

Anal Bioanal Chem

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Multi-block glucans comprising permethylated and partially methylated blocks are compounds of interest. In order to monitor their formation by transglycosylation of corresponding starting glucans, a method has been developed and applied to model compounds. This method allows determining the average length of the blocks and the progress of incorporation of methyl blocks in partially methylated sequences with a random distribution. The method, comprising liquid chromatography mass spectrometry (LC-MS) and electrospray ionization collision-induced dissociation tandem mass spectrometry (ESI-CID-MS n ) measurements of two types of peralkylated glucans representing derivatives of the target compounds, is comprehensively described and discussed. ESI-MS n allows looking into the sequences of oligomeric domains. In addition, transglycosylation is followed by attenuated total reflection FTIR and NMR spectroscopy.

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Controlling the Drug Release Rate and Targeted Drug Delivery to the Desired Site by Molecular Simulation

Bairagee,

Panchawat,

Jain

et al. 2024

Drug Delivery Systems Using Quantum Computing

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A Systematic Coarse-Grained Model for Methylcellulose Polymers: Spontaneous Ring Formation at Elevated Temperature

Cited by 54 publications

References 50 publications

Multiscale Modeling Scheme for Simulating Polymeric Melts: Application to Poly(Ethylene Oxide)

Multiscale Modeling Scheme for Simulating Polymeric Melts: Application to Poly(Ethylene Oxide)

1,4-D-Glucan block copolymers: synthesis and comprehensive structural characterization

Controlling the Drug Release Rate and Targeted Drug Delivery to the Desired Site by Molecular Simulation

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