Thomas D.
Potter
,
Martin
Walker
and
Mark R.
Wilson
*
Department of Chemistry, Durham University, Lower Mountjoy, Stockton Road, Durham, DH1 3LE, UK. E-mail: mark.wilson@durham.ac.uk
First published on 11th September 2020
New coarse-grained models are introduced for a non-ionic chromonic molecule, TP6EO2M, in aqueous solution. The multiscale coarse-graining (MS-CG) approach is used, in the form of hybrid force matching (HFM), to produce a bottom-up CG model that demonstrates self-assembly in water and the formation of a chromonic stack. However, the high strength of binding in stacks is found to limit the transferability of the HFM model at higher concentrations. The MARTINI 3 framework is also tested. Here, a top-down CG model is produced which shows self-assembly in solution in good agreement with atomistic studies and transfers well to higher concentrations, allowing the full phase diagram of TP6EO2M to be studied. At high concentration, both self-assembly of molecules into chromonic stacks and self-organisation of stacks into mesophases occurs, with the formation of nematic (N) and hexagonal (M) chromonic phases. This CG-framework is suggested as a suitable way of studying a range of chromonic-type drug and dye molecules that exhibit complex self-assembly and solubility behaviour in solution.
In early studies, chromonic mesophases were noted in many drug11,12 and dye molecules13–19 with a disc-like shape. Indeed, the unusual solubility behaviour of many drugs and pharmaceutical excipients is often rooted in chromonic-type self-assembly in dilute solution.20 Recently, there has been significant interest in chromonic mesophases because of potential uses.21,22 Many of these arise from the ability to control supramolecular assembly within chromonics and the ability to generate chromonics mesophases that are easily aligned and dried down to produce ordered molecular films for use in a range of applications. These may include polarizers22–25 and optical retardation plates with negative birefringence.26 Additionally, chromonic tactoids have also been used to amplify chirality allowing the presence of small amounts of a chiral compound to be detected,27 and a number of potential uses arise from using chromonics as liquid crystal templates28 (e.g., in the production of new nanostructured materials).29 Chromonics have also been used to good effect within a fascinating new field of active matter: living liquid crystals. Here, the the interaction of the chromonic liquid crystals with microswimmers, such as bacteria, can be controlled.30 Self-organised structures emerge through the balance of bacterial activity and the anisotropic viscoelasticity of the chromonic liquid crystal,31 providing great potential for future biosensors.
Most chromonic mesogens are ionic: occurring as organic salts in aqueous solution. However, a number of anionic chromonics also exist. Here, the interplay between hydrophilic and hydrophobic interactions is particularly subtle; governing a delicate balance between fully dispersed molecules, chromonic aggregates and mesophases, and phase separation.32,33
TP6EO2M, (2,3,6,7,10,11-hexa-(1,4,7-trioxa-octyl)-triphenylene, Fig. 1) is the archetypal non-ionic chromonic mesogen.34–38 Here, a triphenylene (TP) core is solubilized by ethylene oxide (EO) chain of just the right length: shortening the arms significantly reduces the water solubility, while lengthening them prevents the molecules from forming any sort of aggregate.34–36 Recent computer simulations have shed new light on the behaviour of TP6EO2M. Atomistic simulations of molecules in solution have demonstrated the structure of aggregates (stacks with a unimolecular cross-section), calculated the association free energy in solution and demonstrated that aggregation is quasi-isodesmic.39 For isodesmic association, the addition of molecules to the stack occurs with the same change in free energy for each molecule. However, in this case, the binding free energy of two molecules, to form a dimer, is of the order of 2.5 kBT larger than subsequent additions to the stack. Through a detailed thermodynamic analysis, the calculations were able to show both entropic and enthalpic driving forces for self-assembly. While these are of similar magnitude, for TP6EO2M at 300 K, entropic factors are slightly greater than enthalpic ones in driving the self-assembly process.
TP6EO2M has also been studied at a far more coarse-grained level. Dissipative particle dynamics (DPD) calculations of a simple model (based on TP6EO2M) recently allowed the simulation of a full chromonic phase diagram (including N and M phases) using 1000 chromonic mesogens in water.40 Within the isotropic phase, this system was large enough to obtain the exponential distribution of column sizes predicted for isodesmic (or quasi-isodesmic) association. Intriguingly, DPD calculations also suggest that more complex stacks with two and three molecule cross-sections (together with novel lamellar chromonics) may be possible through appropriate substitution of EO for hydrophobic–lipophobic chains.10
In the current study, we introduce new coarse-grained models for TP6EO2M, with the aim of reproducing the key features of self-assembly in solution and also demonstrating chromonic mesophase formation. Such models are challenging to produce: stack formation occurs over periods of tens, or in most cases, hundreds of nanoseconds; and mesophase formation occurs on considerable longer time scales. Initially, we investigate the popular multiscale coarse-graining (MS-CG) approach via hybrid force matching. Models are produced that exhibit chromonic self-assembly in solution and the formation of chromonic stacks but we also show that MS-CG methodology has a range of implementation challenges when applied to complex chromonic systems that limit its transferability and representability. However, we demonstrate that the versatile MARTINI framework, in the form of MARTINI 3,41 provides a very suitable model to show both self-assembly and mesophase formation.
In the current study, we first tested the possibility of developing a bottom-up hybrid force mapped (HFM) model based on the multiscale coarse-graining (MS-CG) methodology,47 employing an atomistic reference system based on the model of Akinshina et al.39 In addition, for the main part of this work, we consider a top-down coarse-grained model for TP6EO2M based on the new MARTINI 3 framework.41,48 In both cases, we use the mapping scheme shown in Fig. 1. Herein, TP6EO2M consists of four bead types: the central core (CC), outer core (CO), inner arm (AI) and outer arm (AO). For the HFM model, the CC and CO beads and the AI and AO beads, use the same interaction parameters, with the respective bead pairs differing only by their masses.
Additionally, for HFM a single site water model is used, which is convenient for matching to a reference atomistic model. For the MARTINI 3 model, different interaction parameters are used for each bead type and, as usual, MARTINI water is mapped to a cluster of 4 water molecules. Both models employed the same set of bonded interactions, with bonds and angles described by simple harmonic potentials. Bond lengths and angles were taken from the energy minimized atomistic structure, mapped onto the coarse-grained representation. Six improper dihedrals were required to keep the core of the molecule planar, but no other dihedral interactions were used in this model. Intramolecular interaction parameters are given in the ESI.† We note in passing that, in these systems, the act of coarse-graining changes the balance of entropic and enthalpic driving forces for self-assembly. For example, the choice of one-site for each water or four water molecules to a single site will change the entropy gain from water molecules liberated by molecular association. However, if the free energy change of association is captured well by the model, this is expected to be sufficient to represent chromonic behaviour.
Two separate reference systems were used for different HFM models: an equilibrated chromonic stack of 10 TP6EO2M molecules in 20928 waters (2.5 wt%); and a pre-equilibrium mixture of short stacks of varying sizes, with 50 TP6EO2M and 14433 water molecules (15.3 wt%). The starting configuration for the stacked reference was obtained by equilibration of a pre-assembled stack for 300 ps at constant NVT, followed by 80 ns at constant NPT. Production runs for these systems were each simulated for 20 ns, and snapshots of positions and forces were taken every 5 ps.
Coarse-grained simulations were carried out at 280 K and employed the simulation parameters described in detail below and in the ESI,† together with the same methodology used for the atomistic calculations. Nonbonded Lennard-Jones cutoffs of 1.5 nm and 1.2 nm were used for the HFM and MARTINI models respectively. For the MARTINI model, it is common to use time steps of up to 20 fs, which further improves the computational speed-up of the model. However, in this case, the highly interconnected bonded structure of the aromatic core resulted in very unstable molecular dynamics with such long time steps. Hence, a smaller value of 2 fs was chosen for TP6EO2M for all coarse-grained simulations. While this removes one advantage of coarse-graining, the model still represents a significant speed-up compared to the atomistic model, both in a reduction of sites and in evolution through phase space.
FTcgFcg(x1,…,xM) = FTcgFref | (1) |
Here, we adopt the hybrid approach to force matching (hybrid force matching, HFM) in which some parts of the interaction potential are specified directly (e.g., the intramolecular force field), and the subsequent forces that arise from these terms are subtracted prior to fitting. The molecular structure of TP6EO2M introduces some complications to the HFM procedure. The rigidity of the aromatic core of TP6EO2M means that, at some distances, the intramolecular interactions dominate the forces in the reference system. The effect of this on the interaction potentials is highlighted in the ESI,† where sharp peaks are seen in through-space interaction potentials computed by trial HFM calculations when only 1–2 and 1–3 excluded interactions are employed. A solution to this was to determine which pairs of coarse-grained beads were causing the sharp peaks by examining the effect on the coarse-grained force field of excluding certain pairs. The interactions which were found to contribute to the issue were then excluded from the atomistic reference. These include all 1–2, 1–3 and 1–4 interactions, as well as the 1–5 interactions arising from the rigidity of the core, as identified in the ESI.† When carrying out coarse-grained simulations, the through-space interactions between these additional excluded pairs were modelled using the non-bonded potentials obtained from the force matching calculations. This is a valid approach in the HFM framework, in which there are no restrictions on which interactions are excluded, and how the excluded interactions are modelled.
As indicated in Section 3, we examined several variants of the HFM approach using neutral (FM-N) and charged beads (FM-Q), and applying a pressure correction to the latter (FM-QP). The models discussed in this section were calculated from 2000 frames of the atomistic reference systems, corresponding to 10 ns, of the stack reference simulation.
Interaction | σ/nm | ε/kJ mol−1 | |||
---|---|---|---|---|---|
N0 | N0a | N1 | N1a | ||
C–C | 0.330 | 1.45 | 1.45 | 1.45 | 1.45 |
AO–AO | 0.330 | 1.70 | 1.45 | 1.70 | 1.45 |
AI–AI | 0.400 | 2.54 | 2.11 | 2.54 | 2.11 |
AO–AI | 0.370 | 2.07 | 1.57 | 2.07 | 1.57 |
C–AO | 0.330 | 1.20 | 1.20 | 1.20 | 1.20 |
C–AI | 0.370 | 1.57 | 1.57 | 1.57 | 1.57 |
W–W | 0.470 | 4.65 | 4.65 | 4.65 | 4.65 |
W–C | 0.411 | 1.19 | 1.19 | 1.19 | 1.19 |
W–AO | 0.41 | 2.40 | 2.15 | 2.67 | 2.40 |
W–AI | 0.440 | 3.02 | 2.81 | 3.23 | 3.02 |
(2) |
(3) |
d = uij × ujk. | (4) |
In practice, Snematic is calculated by diagonalisation of the ordering tensor:
(5) |
The degree of positional order was analysed using the two-dimensional pair distribution function,10g(u,v), for pairs of TP6EO2M molecules. For this quantity, the system director for a frame was determined. Two orthogonal vectors, u and v, were then defined normal to n. u and v were calculated by projecting the distance between a pair of molecules (calculated from the centres of mass of the cores) along these two vectors. A vertical cutoff along the global system director of 0.5 nm was applied so that only pairs within a thin segment of the box were considered.
The unexpected shape of the potentials in the initial HFM model arises from the way that electrostatics are treated in the parametrisation process. It is assumed that (in the spirit of the MARTINI model) all coarse-grained beads are neutral. However, if the charges from the atomistic model were carried over to the coarse-grained model, the AI and CC beads would be neutral, but the AO and CO beads would have very small net charges of −0.2 e and + 0.2 e respectively. The electrostatic forces between the charged beads are included in the force matching and are effectively averaged over all the beads. This is the origin of the repulsive like interactions and the attractive unlike interactions. For TP6EO2M, this is less than optimal for two reasons: the electrostatic interactions should ideally apply only to the AO and CO beads (not all of the beads); and long-ranged electrostatic interactions are being implicitly included in the short-range vdW potentials, and any long-range effects are not being treated fully within the coarse-grained simulations.
This problem can be addressed within HFM by subtracting electrostatic interactions prior to the force matching. To do this, the electrostatic interactions arising from the mapping of the atomistic charges onto the coarse-grained beads were calculated for each frame of the mapped trajectory: giving the coarse-grained electrostatic forces for the system. The electrostatic forces were then subtracted from the reference forces (after the exclusion of intramolecular interactions), giving a new reference trajectory which did not include the coarse-grained electrostatic forces. The new reference was then used for force matching. Coarse-grained simulations of this model, FM-Q, included explicit charges on the AO (−0.2 e) and CO (+0.2 e) beads, and the electrostatic forces were calculated using the PME method. It should be noted that this method can only deal with electrostatic interactions between atoms which map onto charged beads; this neglects, for example, any electrostatic interactions involving water, which is neutral overall. However, this is still expected to be an improvement over the FM-N model.
The FM-Q model potentials are shown in Fig. 2. The interactions involving water are very similar to those in the FM-N model since the water beads are neutral in both models. However, the other interactions have changed significantly. The core–core interactions are now strongly attractive, the arm–arm is more weakly attractive and the core–arm interaction is overall slightly repulsive, but with two small attractive wells. Comparing the two models, the dominant influence of the implicit electrostatic interactions on the potentials of the FM-N model is apparent.
The two coarse-grained models exhibit subtly different behaviour in simulations. For both models, a stack of 10 molecules (2.5 wt%) remains stable over a 100 ns simulation. However, spontaneous self-assembly of the stack occurred over very different timescales (see ESI†). For FM-N, two short chromonic stacks assembled within 35 ns, with a 10-molecule stack forming within 100 ns. The FM-Q model assembled a 10-molecule chromonic stack within only 4 ns of simulation time. The difference between the two models was even more pronounced at a higher concentration (starting from 16 dispersed 16 TP6EO2M molecules at 4.7 wt%). Here, the FM-Q model formed a 16-molecule stack within 20 ns of simulation but over 100 ns the FM-N model was only able to form smaller stacks. These often formed and then disassembled later in the simulation, and side-on aggregation of two stacks was observed at several points in the simulated trajectory.
Partial radial distribution functions (RDFs), calculated for the FM-Q model, are plotted in Fig. 3. These show good overall agreement with the atomistic simulations but with significant quantitative differences where the coarse-grained model is unable to pick up the local structure. For example, for the C–W RDF (Fig. 3d), a difference in the atomistic and coarse-grained models is seen between 0.5 and 1.0 nm, where some of the local interactions have been lost. This region is less well-sampled than the others in the stacked configuration, as parts of the central core are usually hidden from water molecules.
Fig. 3 Site–site intermolecular RDFs calculated from a stack of 10 TP6EO2M molecules, using the FM-Q and atomistic models: (a) C–C, (b) C–A, (c) A–A, (d) C–W, (e) A–W and (f) W–W distributions. |
Two distinct measures of the distance between two molecules in a stack have been used in previous studies of chromonic stacking. The first, dcom, is simply the distance between the centres of mass of two molecules, while the stacking distance, dS, is the distance between the molecules projected along the average of the vectors normal to the cores of the two molecules. Using both measures together provides insights into the nature of the intermolecular stacking: distributions of the two quantities will show different features depending on, for example, whether there are offsets between adjacent molecules, or bends in the stack. These quantities were calculated for the TP6EO2M stack simulated using the atomistic and FM-Q models and are plotted in Fig. 4. The positions of the first peaks in both distributions are well represented by the FM-Q model; the model gives a dS of 0.370 nm and dcom of 0.380 nm, which compare well to the atomistic values of 0.365 nm and 0.375 nm. However, the FM-Q distribution functions lack the tails that are present in the atomistic distributions at longer distances. These arise from flexibility in the molecular stacking leading to some bent configurations of a chromonic stack. Hence, long-distance behaviour is less well-represented within the coarse-grained model. The reason for this can be seen from calculated PMFs (see ESI†). For dimers, the free energy of association, ΔassociationG, calculated from the FM-Q PMF is −219 kJ mol−1, which is more than 5 × the atomistic value.39 (For the FM-N model, ΔassociationG is −180 kJ mol−1; this is lower than the FM-Q value, but still significantly higher than the atomistic value.) Although it is known that there will be a contribution to the free energy of association arising from the density-dependence of the CG-potentials, it is still clear that the HFM approach leads to intermolecular potentials that significantly over-estimate the weak binding between chromonic molecules.
Fig. 4 Distributions of dS (dashed lines) and dcom (solid lines) for a stack of 10 TP6EO2M molecules, calculated using the atomistic and FM-Q models. |
We briefly investigated two possible avenues to improve binding. Firstly, the use of a linear pressure correction for HFM42 was applied to the 10-molecule system. While this allowed the pressure to be well-represented in the 10-molecule CG simulation (and also transferred well to representing the pressure for chromonic dimer in water), the PMF calculated gave a ΔassociationG of −232 kJ mol−1.
Secondly, we note from observation of the trajectories at dcom > 4.5 used for atomic PMFs, that one possible reason for the poor representation of the thermodynamics of dimer formation in HFM is that there are many intermolecular orientations, which are not usually sampled by the standard stack reference system. To try and account for this, we also tried an alternative reference simulation consisting of a mixture of shorter stacks in solution, where it was hypothesised that more distances and orientations would be sampled for TP6EO2M, including those contributing to the interactions between the stacks. The starting structure for this reference was 50 monomers dispersed in water, which were allowed to assemble into short stacks over 20 ns. The HFM model calculated using this reference, referred to as FM-D (see Fig. 2 for the CG interaction potentials), produces a CG model where the core–core interactions are substantially reduced in strength of attraction, while other interactions are similar. Simulation of this model over long times leads to a mixture of monomers and short stacks (similar to the reference simulation), and simulation of a longer stack leads to fragmentation into shorter fragments.
The results highlight the difficulties of making a truly representative bottom-up model for chromonics based on an atomic reference. For the coarse-grained model to be truly representative of the system, it must be able to capture even those configurations which are rarely seen. A possible future solution to this could be a multi-state parametrisation, where both a stack and a dispersed system are used as references, along with local-density (L-D) potentials,68–70 which in this case could be used as a metric for whether a molecule is a monomer, in the middle of a stack or at the stack end (altering the interaction with other molecules accordingly). A related approach has been used for a hydrophobic polymer, where the L-D potential described whether a particular region of a polymer was part of an aggregate.71
Starting from a pre-assembled stack of 10 TP6EO2M molecules, the chromonic stack was found to be stable over 100 ns for all 4 models. Fig. 6 shows dcom and dS distributions calculated for the nearest neighbours in the stack. The stacking distances are in quite good agreement with the atomistic data (Fig. 4) but noting that the dcom values are shifted to slightly higher values compared to dS, indicating slight offsets between adjacent molecules in the stack. The dcom distribution has a long tail compared to the HFM model of Fig. 4, indicating that the MARTINI 3 stacks have a similar degree of flexibility as the atomistic model. Here, the MARTINI 3 models are less tightly anchored in stacks, leading to greater stack flexibility. The models do exhibit some variation in their stacking distance, despite using the same bead type for the core. This may be rationalised in terms of solubility; the models with more soluble A beads have a larger dS, allowing for greater solvation of the hydrophilic arms.
Fig. 6 Distributions of dcom (solid lines) and dS (dotted lines) for each of the four MARTINI 3 models. |
Fig. 7 shows a comparison between site–site intermolecular RDFs from MARTINI 3 models and the atomistic simulations. The CG models capture the key structural features of the atomistic model, albeit with sharper peaks in the RDFs arising from the fact that MARTINI sites are at the centre of beads and the atomistic results use centres of mass of beads (which can fluctuate slightly for each bead).
Fig. 7 Site–site intermolecular RDFs calculated from a stack of 10 TP6EO2M molecules, using the MARTINI 3 and atomistic models: (a) C–C, (b) C–A, (c) A–A, (d) A–W. |
In the ESI† we include additional information about the arrangements of molecules within chromonic stacks. For all models, the distribution of pairwise tilt angles is strongly peaked around zero tilt. Adjacent molecules within a stack are staggered so that chains do not directly overlap. This increases solvation of chains but also allows a larger preferred distance between hydrophilic chains in comparison to the aromatic cores. For the atomistic model, the distribution function for the azimuthal angle for adjacent cores in a stack is peaked at exactly 60, 180 and 300 degrees (corresponding to three identical staggered arrangements of adjacent cores) whereas each of these peaks is split into two within the MARTINI 3 model, due to the packing effects of the two CO beads at the edge of each outer aromatic ring. Hence, the choice of mapping means that the azimuthal distribution function cannot be exactly reproduced within the coarse-grained framework.
The PMFs calculated using the MARTINI 3 models are as shown in Fig. 8. The ΔassociationG values from these PMFs, for the N0, N1, N0a and N1a models respectively, are: −46.3 ± 1.5, −36.7 ± 1.5, −42.6 ± 1.5 and −32.4 ± 1.4 kJ mol−1. These all represent a significant improvement over the HFM models, in terms of reproducing the atomistic free energy for dimer association (40.7 kJ mol−1) calculated by Akinshina et al.39 The differences between the PMFs are consistent with the behaviour of the models in simulations i.e., N0 and N0a models have slightly deeper potential wells. All of the CG PMFs have a very small barrier to association (2–3 kJ mol−1). The origin of this barrier is from solvation. As two TP6EO2M molecules approach each other, they are initially fully solvated by water but at around 1.5–1.7 nm as the two molecules start to interact there is insufficient room between the two molecules for all of the beads to be fully solvated for all orientations. However, the attraction between the TP6EO2M cores is too small to cancel this out at these long distances.
These large scale simulations provide further insight into the representability of the four MARTINI 3 models. All of the models produced chromonic stacks during the simulations. However, the N0 and N0a models, which performed well in the small-scale simulations, led to phase separation at this concentration, forming distinct liquid TP6EO2M and water regions. The N1 and N1a models, on the other hand, formed long chromonic stacks which were fully solvated in water. Representative simulation snapshots from the N0 and N1 models are shown in Fig. 9.
Fig. 9 Final configurations after 500 ns of simulation from a starting point of 1000 TP6EO2M molecules in water, using the (a) N0 and (b) N1 MARTINI 3 models. |
The difference in behaviour between the models can be attributed to the solubility of the beads used for the hydrophilic arms. The N0 and N0a beads are insufficiently soluble so that when multiple long stacks are modelled, they preferentially aggregate together. The higher solubility of the N1 and N1a beads prevents this from happening. These results highlight the difficulties in parametrising models for hierarchical systems like chromonic liquid crystals; a model which is representative at one scale may not perform as well at another scale and the balance between core–core, core–arm, core–water and arm–water interactions all subtly change association behaviour.
Even though the formation of chromonic stacks was observed in the first few nanoseconds of simulation of the N1 and N1a models, the self-assembly of these stacks into the nematic phase was not seen over 500 ns of simulation. The evolution of the nematic order parameter, Snematic, shows the system order parameter rarely fluctuates above 0.2. Here, many of the chromonic stacks are observed to be continuous over periodic boundary conditions and are tangled up with other stacks. For the nematic phase to be formed, these stacks would first need to break and disentangle themselves. Given the speed with which chromonic stacks are formed and the high free energy driving force for molecular stacking, it is extremely unlikely that this will be observed within a reasonable simulation timescale using standard molecular dynamics without the use of an alignment field (e.g., magnetic field) or shear.
However, to look at potential nematic (N) and hexagonal (M) phases, an untangled hexagonal columnar system was preassembled and allowed to evolve and observed for different concentrations. Here, starting structures were constructed by placing 30 columns with 30 TP6EO2M molecules each in a hexagonal arrangement within a cuboidal simulation box and solvated with varying amounts of MARTINI 3 water corresponding to TP6EO2M/water concentrations of 27.1, 39.4, 55.8 and 71.4 wt%. All starting structures had the same initial volume, so the volumes of the boxes were allowed to equilibrate before any data was gathered, employing semi-isotropic pressure coupling to allow the cross-section of the nematic phase and the lengths of the columns to change independently. The system densities equilibrated within 200 ps for all of the concentrations, and remained stable throughout the simulations; equilibrated densities are given in Table 2. Each system was simulated for a total of 500 ns to look for the transition to the hexagonal phase.
Concentration/wt% | ρ/kg m−3 | S nematic (N1a) | LC phase |
---|---|---|---|
27.1 | 1093 | 0.914 | N |
39.4 | 1129 | 0.915 | N |
55.8 | 1181 | 0.922 | M |
71.4 | 1238 | 0.931 | M |
These simulations provided further insights into which bead type best represents the arm beads. Here, the N1 and N1a models exhibited significantly different behaviour. Using the N1 model, there was a tendency at higher concentrations (∼55 wt%) for the stacks to form small clusters of columns with ethylene oxide arms in close proximity, with stacks that were not fully solvated (see ESI†). At these higher concentrations, the N1a model shows the M (CH) phase as predicted by the phase diagram, while the formation of clusters of columns by the N1 model disrupts the formation of regular hexagonal packing. Despite the excellent performance of the N1 model in simulating the self-assembly of short stacks and close agreement to atomistic and experimental values of ΔassociationG, it is far less representative at higher concentrations.
The N1a model was therefore used for more detailed analysis of the system across the concentration range. The nematic order parameter, Snematic, was calculated for each concentration to quantify the orientational order of each system, and the values are shown in Table 2. In each case, Snematic is greater than 0.9, showing long-range orientational order. The value increases slightly with TP6EO2M concentration; which is expected, as increased concentration leads to closer packing of the columns.
Two-dimensional pair distribution functions are shown in Fig. 10, along with the configurations after 500 ns showing the cross-section of the nematic/hexagonal phase in Fig. 11. The two lower concentrations show clear liquid behaviour in two dimensions; combined with the Snematic values, this indicates the nematic phase. Between 39.4% and 55.8%, there is a clear transition from liquid-like to hexagonal ordering in close agreement with experiment.7,36
Fig. 10 Two dimensional g(u,v) from simulations of pre-assembled TP6EO2M columnar phases at concentrations of (a) 27.1, (b) 39.4, (c) 55.8 and (d) 71.4 wt%. |
Fig. 11 Configurations taken after 500 ns of simulations of the N1a model, starting from pre-assembled columnar structures at (a) 27.1, (b) 39.4, (c) 55.8 and (d) 71.4 wt%. |
It should be noted that these simulations are effectively modelling a series of infinitely long stacks. A system with a distribution of sizes might be expected to behave slightly differently as the presence of short stacks in the system may disrupt hexagonal packing, resulting in a slightly more disordered M phase. Despite this limitation, the results here show that the MARTINI 3 force field is well-suited to modelling the concentration dependence of the TP6EO2M/water phase diagram.
We find that the hybrid force matching method is successful in reproducing chromonic stacks in solution. However, the binding between CG molecules is found to be very high, reflecting the lack of transferability between state points that occurs with force matching methods. In future, it will be interesting to see whether CG potentials that can adjust to their environment (such as density-dependent CG potentials) could be employed to improve transferability in chromonics and similar systems.
We also tested MARTINI models for TP6EO2M. These are also found to be sensitive to the hydrophilic–hydrophobic balance of different parts of the molecule in solution. However, we were able to demonstrate a model that shows good self-assembly behaviour in solution and can also be used in the nematic (N) and hexagonal (M) phases. For such a simple CG model this demonstrates a high degree of transferability over a substantial concentration range across the phase diagram, opening up possibilities of using these models to study a wide range of drug and dye molecules that show chromonic-type assembly in solution.
Footnote |
† Electronic supplementary information (ESI) available: Intramolecular components of the coarse-grained force field, hybrid force matching intermolecular potentials (C–C interaction), snapshot configurations showing self-assembly in solution, structural data for chromonic stacks, PMFs for TP6EO2M dimers, snapshots from liquid crystal phases. See DOI: 10.1039/d0sm01157f |
This journal is © The Royal Society of Chemistry 2020 |