Study on Thermophysical Properties and Phase Change Regulation Mechanism of Optically-Controlled Phase Change Materials: Synthesis, Crystal Structure and Molecular Dynamics.

Optically-controlled phase change materials, which are prepared by introducing molecular photoswitches into traditional phase change materials (PCMs), can convert and store solar energy into photochemical enthalpy and phase change enthalpy. However, the thermophysical properties of optically controlled PCMs, which are crucial in the practical, are rarely paid attention to. 4-(phenyldiazenyl)phenyl decanoate (Azo-A-10) is experimentally prepared as an optically-controlled PCMs, whose energy storage density is 210.0 kJ·kg-1, and the trans single crystal structure is obtained. The density, phase transition temperature, thermal conductivity, and other parameters in trans state are measured experimentally. Furthermore, a microscopic model of Azo-A-10 is established, and the thermophysical properties are analyzed based on molecular dynamics. The results show that the microstructure parameter (order parameters) and thermophysical properties (density, radial distribution function, self-diffusion coefficient, phase change temperature, and thermal conductivity) of partially or completely isomerized Azo-A-10, which are challenging to observe in experiments, can be predicted by molecular dynamics simulation. The optically-controlled phase change mechanism can be clarified according to the differences in microstructure. The optically-controlled switchability of thermophysical properties of an optically-controlled PCM is analyzed. This study provides ideas for the improvement, development, and application of optically-controlled PCMs in the future.

Simulation of oriented NMR spectra: Combining molecular dynamics and chemical shift tensor calculations.

Solid state NMR is widely used to study the orientation and other structural features of proteins and peptides in lipid bilayers. Using data obtained by PISEMA (Polarization Inversion Spin Exchange at Magic Angle) experiments, periodic spectral patterns arise from well-aligned α-helical molecules. Significant problems in the interpretation of PISEMA spectra may arise for systems that do not form perfectly defined secondary structures, like α-helices, or the signal pattern is disturbed by molecular motion. Here, we present a new method that combines molecular dynamics simulation with tensorial orientational constraints (MDOC) and chemical shift tensor calculations for the simulation and interpretation of PISEMA-like spectra. The calculations include the spectra arising from non α-helical molecules and molecules with non-uniform intrinsic mobility. In a first step, dipolar or quadrupolar interaction tensors drive molecular rotations and reorientations to obtain the proper mean values as observed in corresponding NMR experiments. In a second step, the coordinate snapshots of the MDOC simulations are geometry optimized with the isotropic 15 N chemical shifts as constraints using Bond Polarization Theory (BPT) to provide reliable 15 N CS tensor data. The averaged dipolar 1 H-15 N couplings and the δzz tensor components can then be combined to simulate PISEMA patterns. We apply this method to the ß-helical peptide gramicidin A (gA) and demonstrate that this method enables the assignment of most PISEMA resonances. In addition, MDOC simulations provide local order parameters for the calculated sites. These local order parameters reveal large differences in backbone mobility between L- and D-amino acids of gA.

Finite Temperature String with Order Parameter as Collective Variables for Molecular Crystal: A Case of Polymorphic Transformation of TNT under External Electric Field.

An external electric field is an effective tool to induce the polymorphic transformation of molecular crystals, which is important practically in the chemical, material, and energy storage industries. However, the understanding of this mechanism is poor at the molecular level. In this work, two types of order parameters (OPs) were constructed for the molecular crystal based on the intermolecular distance, bond orientation, and molecular orientation. Using the K-means clustering algorithm for the sampling of OPs based on the Euclidean distance and density weight, the polymorphic transformation of TNT was investigated using a finite temperature string (FTS) under external electric fields. The potential of mean force (PMF) was obtained, and the essence of the polymorphic transformation between o-TNT and m-TNT was revealed, which verified the effectiveness of the FTS method based on K-means clustering to OPs. The differences in PMFs between the o-TNT and transition state were decreased under external electric fields in comparison with those in no field. The fields parallel to the c-axis obviously affected the difference in PMF, and the relationship between the changes in PMFs and field strengths was found. Although the external electric field did not promote the convergence, the time of the polymorphic transformation was reduced under the external electric field in comparison to its absence. Moreover, under the external electric field, the polymorphic transformation from o-TNT to m-TNT occurred while that from m-TNT to o-TNT was prevented, which was explained by the dipole moment of molecule, relative permittivity, chemical potential difference, nucleation work and nucleation rate. This confirmed that the polymorphic transformation orientation of the molecular crystal could be controlled by the external electric field. This work provides an effective way to explore the polymorphic transformation of the molecular crystals at a molecular level, and it is useful to control the production process and improve the performance of energetic materials by using the external electric fields.

Experimental and Theoretical Investigation of External Electric-Field-Induced Crystallization of TKX-50 from Solution by Finite-Temperature String with Order Parameters as Collective Variables for Ionic Crystals.

External electric fields are an effective tool to induce phase transformations. The crystallization of ionic crystals from solution is a common phase transformation. However, understanding of mechanisms is poor at the molecular level. In this work, we carried out an experimental and theoretical investigation of the external electric-field-induced crystallization of TKX-50 from saturated formic acid solution by finite-temperature string (FTS) with order parameters (OPs) as collective variables for ionic crystals. The minimum-free-energy path was sketched by the string method in collective variables. The results show that the K-means clustering algorithm based on Euclidean distance and density weights can be used for enhanced sampling of the OPs in external electric-field-induced crystallization of ionic crystal from solution, which improves the conventional FTS. The crystallization from solution is a process of surface-mediated nucleation. The external electric field can accelerate the evolution of the string and decrease the difference in the potential of mean forces between the crystal and the transition state. Due to the significant change in OPs induced by the external electric field in nucleation, the crystalline quality was enhanced, which explains the experimental results that the external electric field enhanced the density, detonation velocity, and detonation pressure of TKX-50. This work provides an effective way to explore the crystallization of ionic crystals from solution at the molecular level, and it is useful for improving the properties of ionic crystal explosives by using external electric fields.

Predicting protein flexibility with AlphaFold.

AlphaFold2 has revolutionized protein structure prediction from amino-acid sequence. In addition to protein structures, high-resolution dynamics information about various protein regions is important for understanding protein function. Although AlphaFold2 has neither been designed nor trained to predict protein dynamics, it is shown here how the information returned by AlphaFold2 can be used to predict dynamic protein regions at the individual residue level. The approach, which is termed cdsAF2, uses the 3D protein structure returned by AlphaFold2 to predict backbone NMR NH S2 order parameters using a local contact model that takes into account the contacts made by each peptide plane along the backbone with its environment. By combining for each residue AlphaFold2's pLDDT confidence score for the structure prediction accuracy with the predicted S2 value using the local contact model, an estimator is obtained that semi-quantitatively captures many of the dynamics features observed in experimental backbone NMR NH S2 order parameter profiles. The method is demonstrated for a set nine proteins of different sizes and variable amounts of dynamics and disorder.

Influence of the Thiophene Ring on the Molecular Order of Structurally Simple π-Conjugated Smectogens: 13 C NMR Study.

Structurally simple rod-like π-conjugated mesogens with thiophene directly connected to phenyl, biphenyl, and fluorenone rings with terminal chains are synthesized respectively. The occurrence of smectic A/smectic C phases is concurred by a hot-stage optical polarising microscope (HOPM), differential scanning calorimetry (DSC), and X-ray diffraction (XRD). The static 1D and 2D 13 C nuclear magnetic resonance (NMR) studies in the liquid crystalline phase are carried out to find the alignment-induced chemical shifts (AIS) and 13 C-1 H dipolar couplings. The orientational order parameters of the mesogens determined from 13 C-1 H dipolar couplings disclose that the long axis is not only collinear to the C3-C4 bond of the thiophene ring but also for the local axes of phenyl and biphenyl rings. For fluorenone-based mesogen, the molecular biaxiality is found to be high owing to the increased breadth of the molecule. The study unveils that the orientation of thiophene and the phenyl rings is similar in the current mesogens in stark contrast to mesogens, where thiophene is connected to phenyl rings through linking groups.

Supercooled water in two dimensions: Structure and thermodynamics of the Mercedes-Benz model.

The two-dimensional Mercedes-Benz model of water has been studied by molecular simulations over a wide range of thermodynamic conditions as an attempt to locate the supercooled region where a liquid-liquid separation and, potentially, also other structures may occur. Both the correlation functions and a number of local structure factors have been used to identify different structural arrangements. These include, in addition to the hexatic phase, also the hexagon, pentagon, and quadruplet arrangements. All these structures result from the competition between the hydrogen bonding and Lennard-Jones interactions and their effect at different temperatures and pressures. Based on the obtained results, an attempt is made to sketch a (rather complex) phase diagram of the model.

SARS-coronavirus-2 infections: biological instabilities characterized by order parameters.

A four-variable virus dynamics TIIV model was considered that involves infected cells in an eclipse phase. The state space description of the model was transferred into an amplitude space description which is the appropriate general, nonlinear physics framework to describe instabilities. In this context, the unstable eigenvector or order parameter of the model was determined. Subsequently, a model-based analysis of viral load data from eight symptomatic COVID-19 patients was conducted. For all patients, it was found that the initial SARS-CoV-2 infection evolved along the respective patient-specific order parameter, as expected by theoretical considerations. The order parameter amplitude that described the initial virus multiplication showed doubling times between 30 min and 3 h. Peak viral loads of patients were linearly related to the amplitudes of the patient order parameters. Finally, it was found that the patient order parameters determined qualitatively and quantitatively the relationships between the increases in virus-producing infected cells and infected cells in the eclipse phase. Overall, the study echoes the 40 years old suggestion by Mackey and Glass to consider diseases as instabilities.

TRACT revisited: an algebraic solution for determining overall rotational correlation times from cross-correlated relaxation rates.

Accurate rotational correlation times ([Formula: see text]) are critical for quantitative analysis of fast timescale NMR dynamics. As molecular weights increase, the classic derivation of [Formula: see text] using transverse and longitudinal relaxation rates becomes increasingly unsuitable due to the non-trivial contribution of remote dipole-dipole interactions to longitudinal relaxation. Derivations using cross-correlated relaxation experiments, such as TRACT, overcome these limitations but are erroneously calculated in 65% of the citing literature. Herein, we developed an algebraic solutions to the Goldman relationship that facilitate rapid, point-by-point calculations for straightforward identification of appropriate spectral regions where global tumbling is likely to be dominant. The rigid-body approximation of the Goldman relationship has been previously shown to underestimate TRACT-based rotational correlation time estimates. This motivated us to develop a second algebraic solution that employs a simplified model-free spectral density function including an order parameter term that could, in principle, be set to an average backbone S2 ≈ 0.9 to further improve the accuracy of [Formula: see text] estimation. These solutions enabled us to explore the boundaries of the Goldman relationship as a function of the H-N internuclear distance ([Formula: see text]), difference of the two principal components of the axially-symmetric 15N CSA tensor ([Formula: see text]), and angle of the CSA tensor relative to the N-H bond vector ([Formula: see text]). We hope our algebraic solutions and analytical strategies will increase the accuracy and application of the TRACT experiment.

A Methionine Chemical Shift Based Order Parameter Characterizing Global Protein Dynamics.

Coupling of side chain dynamics over long distances is an important component of allostery. Methionine side chains show the largest intrinsic flexibility among methyl-containing residues but the actual degree of conformational averaging depends on the proximity and mobility of neighboring residues. The 13 C NMR chemical shifts of the methyl groups of methionine residues located at long distances in the same protein show a similar scaling with respect to the values predicted from the static X-ray structure by quantum methods. This results in a good linear correlation between calculated and observed chemical shifts. The slope is protein dependent and ranges from zero for the highly flexible calmodulin to 0.7 for the much more rigid calcineurin catalytic domain. The linear correlation is indicative of a similar level of side-chain conformational averaging over long distances, and the slope of the correlation line can be interpreted as an order parameter of the global side-chain flexibility.

On the Use of Side-Chain NMR Relaxation Data to Derive Structural and Dynamical Information on Proteins: A Case Study Using Hen Lysozyme.

Values of S2CH and S2NH order parameters derived from NMR relaxation measurements on proteins cannot be used straightforwardly to determine protein structure because they cannot be related to a single protein structure, but are defined in terms of an average over a conformational ensemble. Molecular dynamics simulation can generate a conformational ensemble and thus can be used to restrain S2CH and S2NH order parameters towards experimentally derived target values S2CH (exp) and S2NH (exp). Application of S2CH and S2NH order-parameter restraining MD simulation to bond vectors in 63 side chains of the protein hen egg white lysozyme using 51 S2CH (exp) target values and 28 S2NH (exp) target values shows that a conformational ensemble compatible with the experimentally derived data can be obtained by using this technique. It is observed that S2CH order-parameter restraining of C-H bonds in methyl groups is less reliable than S2NH order-parameter restraining because of the possibly less valid assumptions and approximations used to derive experimental S2CH (exp) values from NMR relaxation measurements and the necessity to adopt the assumption of uniform rotational motion of methyl C-H bonds around their symmetry axis and of the independence of these motions from each other. The restrained simulations demonstrate that side chains on the protein surface are highly dynamic. Any hydrogen bonds they form and that appear in any of four different crystal structures, are fluctuating with short lifetimes in solution.

SEIR order parameters and eigenvectors of the three stages of completed COVID-19 epidemics: with an illustration for Thailand January to May 2020.

By end of October 2020, the COVID-19 pandemic has taken a tragic toll of 1150 000 lives and this number is expected to increase. Despite the pandemic is raging in most parts of the world, in a few countries COVID-19 epidemics subsided due to successful implementations of intervention measures. A unifying perspective of the beginnings, middle stages, and endings of such completed COVID-19 epidemics is developed based on the order parameter and eigenvalue concepts of nonlinear physics, in general, and synergetics, in particular. To this end, a standard susceptible-exposed-infected-recovered (SEIR) epidemiological model is used. It is shown that COVID-19 epidemic outbreaks follow a suitably defined SEIR order parameter. Intervention measures switch the eigenvalue of the order parameter from a positive to a negative value, and in doing so, stabilize the COVID-19 disease-free state. The subsiding of COVID-19 epidemics eventually follows the remnant of the order parameter of the infection dynamical system. These considerations are illustrated for the COVID-19 epidemic in Thailand from January to May 2020. The decay of effective contact rates throughout the three epidemic stages is demonstrated. Evidence for the sign-switching of the dominant eigenvalue is given and the order parameter and its stage-3 remnant are identified. The presumed impacts of interventions measures implemented in Thailand are discussed in this context.

Systematic Analysis of Emergent Collective Motion Produced by a 3D Hybrid Zonal Model.

Emergent patterns of collective motion are thought to arise from local rules of interaction that govern how individuals adjust their velocity in response to the relative locations and velocities of near neighbours. Many models of collective motion apply rules of interaction over a metric scale, based on the distances to neighbouring group members. However, empirical work suggests that some species apply interactions over a topological scale, based on distance determined neighbour rank. Here, we modify an important metric model of collective motion (Couzin et al. in J Theor Biol 218(1):1-11, 2002), so that interactions relating to orienting movements with neighbours and attraction towards more distant neighbours operate over topological scales. We examine the emergent group movement patterns generated by the model as the numbers of neighbours that contribute to orientation- and attraction-based velocity adjustments vary. Like the metric form of the model, simulated groups can fragment (when interactions are influenced by less than 10-15% of the group), swarm and move in parallel, but milling does not occur. The model also generates other cohesive group movements including cases where groups exhibit directed motion without strong overall alignment of individuals. Multiple emergent states are possible for the same set of underlying model parameters in some cases, suggesting sensitivity to initial conditions, and there is evidence that emergent states of the system depend on the history of the system. Groups that do not fragment tend to stay relatively compact in terms of neighbour distances. Even if a group does fragment, individuals remain relatively close to near neighbours, avoiding complete isolation.

Magic-Angle-Pulse Driven Separation of Degenerate 1 H Transitions in Methyl Groups of Proteins: Application to Studies of Methyl Axis Dynamics.

Dynamics of protein side chains is one of the principal determinants of conformational entropy in protein structures and molecular recognition events. We describe NMR experiments that rely on the use of magic-angle pulses for efficient isolation of degenerate 1 H transitions of the I=3/2 manifold of 13 CH3 methyl groups, and serve as 'building blocks' for the measurement of transverse spin relaxation rates of the fast- and slow-relaxing 1 H transitions - the primary quantitative reporters of methyl axis dynamics in selectively {13 CH3 }-methyl-labelled, highly deuterated proteins. The magic-angle-pulse driven experiments are technically simpler and, in the absence of relaxation, predicted to be 2.3-fold more sensitive than previously developed analogous schemes. Validation of the methodology on a sample of {13 CH3 }-labeled ubiquitin demonstrates quantitative agreement between order parameters of methyl three-fold symmetry axis obtained with magic-angle-pulse driven experiments and other established NMR techniques, paving the way for studies of methyl axis dynamics in human DNAJB6b chaperone, a protein that undergoes exchange with high-molecular-weight oligomeric species.

COVID-19 interventions in some European countries induced bifurcations stabilizing low death states against high death states: An eigenvalue analysis based on the order parameter concept of synergetics.

Taking a dynamical systems perspective, COVID-19 infections are assumed to spread out in a human population via an instability. Conversely, government interventions to reduce the spread of the disease and the number of fatalities may induce a bifurcation that stabilizes a desirable state with low numbers of COVID-19 cases and associated deaths. The key characteristic feature of an infection dynamical system in this context is the eigenvalue that determines the stability of the states under consideration and is known in synergetics as the order parameter eigenvalue. Using a SEIR-like infection disease model, the relevant order parameter and its eigenvalue are determined. A three stage methodology is proposed to track and estimate the eigenvalue through time. The method is applied to COVID-19 infection data reported from 20 European countries during the period of January 1, 2020 to June 15. It is shown that in 15 out of the 20 countries the eigenvalue switched its sign suggesting that during the reporting period an intervention bifurcation took place that stabilized the desirable low death state. It is shown that the eigenvalue analysis also allows for a ranking of countries by the degree of the stability of the infection-free state. For the investigated countries, Ireland was found to exhibit the most stable infection-free state. Finally, a six point classification scheme is suggested with groups 5 and 6 including countries that failed to stabilize the desirable infection-free low death state. In doing so, tools for assessing the effectiveness of government interventions are provided that are at the heart of bifurcation theory, in general, and synergetics, in particular.

Solar Energy Transformation Strategies by Ecosystems of the Boreal Zone (Thermodynamic Analysis Based on Remote Sensing Data).

The hypothesis of an increase in free energy (exergy) by ecosystems during evolution is tested on direct measurements. As a measuring system of thermodynamic parameters (exergy, information, entropy), a series of measurements of reflected solar radiation in bands of Landsat multispectral imagery for 20 years is used. The thermodynamic parameters are compared for different types of ecosystems depending on the influx of solar radiation, weather conditions and the composition of communities. It is shown that maximization of free energy occurs only in a succession series (time scale of several hundred years), and on a short evolutionary time scale of several thousand years, various strategies of energy use are successfully implemented at the same time: forests always maximize exergy and, accordingly, transpiration, meadows-disequilibrium and biological productivity in summer, and swamps, due to a prompt response to changes in temperature and moisture, maintaining disequilibrium and productivity throughout the year. On the basis of the obtained regularities, we conclude that on an evolutionary time scale, the thermodynamic system changes in the direction of increasing biological productivity and saving moisture, which contradicts the hypothesis of maximizing free energy in the course of evolution.

Tropical Monsoon Forest Thermodynamics Based on Remote Sensing Data.

This paper addresses thermodynamic variables that characterize the energy balance and structure of the solar energy transformation by the ecosystems of deciduous tropical forests. By analyzing the seasonal dynamics of these variables, two main states of the thermodynamic system are determined: the end of the drought season and the end of the wet season. Two sub-systems of solar energy transformation are also defined: a balance system that is responsible for the moisture transportation between the ecosystem and atmosphere; and a structural bioproductional system responsible for biological productivity. Several types of thermodynamic systems are determined based on the ratio between the invariants of the variables. They match the main classes of the landscape cover. A seasonal change of thermodynamic variables for different types of thermodynamic systems is additionally studied. The study reveals that temperature above the forest ecosystems is about 4° lower than above the open areas during most of the year.

Effect of monovalent ion binding on molecular dynamics of the S100-family calcium-binding protein calbindin D9k.

Calbindin D9k is a member of the S100 subfamily of EF-hand calcium binding proteins, and has served as an important model system for biophysical studies. The fast timescale dynamics of the calcium-free (apo) state is characterized using molecular dynamics simulations. Order parameters for the backbone NH bond vectors are determined from the simulations and compared with experimentally derived values, with a focus on the dynamics of calcium-binding site I. There is a significant discrepancy between simulated and experimental order parameters for site I residues in the case of no ion bound in site I. However, it was found in the simulations that a Na+ ion can bind in site I, and the resulting order parameters determined from the simulations are in excellent agreement with experiment. Comparisons are made to X-ray structures of other S100 family members in which Na+ ions were observed or suggested to be bound in site I. © 2019 Wiley Periodicals, Inc.

Interpretation of Seemingly Contradictory Data: Low NMR S2 Order Parameters Observed in Helices and High NMR S2 Order Parameters in Disordered Loops of the Protein hGH at Low pH.

At low pH, human growth hormone (hGH) adopts a partially folded state, in which the native helices are maintained, but the long loop regions and side-chain packing become disordered. Some of the S2 order parameters for backbone N-H vectors derived from NMR relaxation measurements on hGH at low pH initially seem contradictory. Three isolated residues (15, 20, and 171) in helices A and D exhibit low order parameter values (<0.5) indicating flexibility, whereas residue 143 in the centre of a long flexible loop region has a high order parameter (0.82). Using S2 order parameter restraining MD simulations, this paradox has been resolved. Low S2 values in helices are due to the presence of a mixture of 310 -helical and α-helical hydrogen bonds. High S2 values in relatively disordered parts of a protein may be due to fluctuating networks of hydrogen bonds between the backbone and the side chains, which restrict the motion of N-H bond vectors.

Loss of conformational entropy in protein folding calculated using realistic ensembles and its implications for NMR-based calculations.

The loss of conformational entropy is a major contribution in the thermodynamics of protein folding. However, accurate determination of the quantity has proven challenging. We calculate this loss using molecular dynamic simulations of both the native protein and a realistic denatured state ensemble. For ubiquitin, the total change in entropy is TΔSTotal = 1.4 kcal⋅mol(-1) per residue at 300 K with only 20% from the loss of side-chain entropy. Our analysis exhibits mixed agreement with prior studies because of the use of more accurate ensembles and contributions from correlated motions. Buried side chains lose only a factor of 1.4 in the number of conformations available per rotamer upon folding (ΩU/ΩN). The entropy loss for helical and sheet residues differs due to the smaller motions of helical residues (TΔShelix-sheet = 0.5 kcal⋅mol(-1)), a property not fully reflected in the amide N-H and carbonyl C=O bond NMR order parameters. The results have implications for the thermodynamics of folding and binding, including estimates of solvent ordering and microscopic entropies obtained from NMR.