Structures of ferrous eutectic liquids

Density functional calculations, at the B3LYP/6-311 + G(3df,2p) level, have been carried out for the complete series of β-chalcogenvinylaldehydes to investigate whether the topology of the charge density of these systems can provide some useful information on the strength of X–H⋯Y or X⋯H–Y intramolecular hydrogen bonds or on the strength of H–X⋯Y or X⋯Y–H intramolecular chalcogen–chalcogen interactions. We have shown that, in general, there exist good linear correlations between the charge density at the ring critical point and (a) the relative strength of these interactions as measured by appropriate homodesmotic reactions, (b) the X⋯Y chalcogen–chalcogen distance. It must be emphasized, however, that while the latter correlation is systematically fulfilled, the former is only observed when the relative strength of the intramolecular interaction, either a IHB or a chalcogen–chalcogen interaction, varies inversely with the chalcogen–chalcogen distance. Also importantly, the variation of the charge density at the ring critical point correlates both, with the change in the stability of the system and with the change in the chalcogen–chalcogen distance, when the IHB is replaced by the chalcogen–chalcogen interaction, through an internal rotation of the X–H or the Y–H group.

Glass structure and crystallization via two distinct thermal histories: Melt crystallization and glass crystallization

The interaction of the anionic surfactant sodium dodecylsulfate (SDS) with arginine, histidine, lysine, ornithine and tryptophan was studied by using reversed-phase thin-layer chromatography on unimpregnated cellulose layers. The relative strength of interaction and the impact of salt concentration and pH on it was calculated by using stepwise regression analysis. SDS did not interact with His, and its interaction with Trp was fairly weak. SDS strongly binds to dibasic amino acids; the relative strength of interaction is significantly higher with Lys and Orn than with Arg. These results indicate that SDS probably binds to the secondary amino groups of Lys in proteins. The impact of sodium chloride and acetic acid was similar, but the effect of sodium acetate was significantly weaker. The strong dependence of the strength of interaction on the salt concentration and on the pH of the eluent indicates that hydrophilic, probably electrostatic forces are involved in the SDS-protein interaction.

Molecular dynamics simulations of binary colloidal monolayers, i.e., monolayers consisting of mixtures of two different particle sizes, are presented. In the simulations, the colloid particles are located at an oil-water interface and interact via an effective dipole-dipole potential. In particular, the influence of the particle ratio on the configurations of the binary monolayers is investigated for two different relative interaction strengths between the particles, and the pair correlation functions corresponding to the binary monolayers are calculated. The simulations show that the binary monolayers can only form two-dimensional crystals for certain particle ratios, for example, 2:1, 6:1, etc., while, for example, for a particle ratio of 7:1 the monolayers are found to be in a disordered, glassy state. The calculations also reveal that in analogy to the Wigner lattice the configurations are very sensitive to the relative interaction strength between the particles but not to the absolute magnitude of the interaction strength, even when particle size effects are taken into account. Consequently, it is argued that a comparison between the calculated configurations and actual binary particle monolayer systems could provide useful information on the relative interaction strength between large and small particles. Possible mechanisms giving rise to disparities in the interaction strength between large and small particles are described briefly.

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Appendix 1: Model Details Appendix 2 Thermodynamics of assembly around a fluid cargo References Decision letter Author response Article and author information Metrics Abstract We computationally study how an icosahedral shell assembles around hundreds of molecules. Such a process occurs during the formation of the carboxysome, a bacterial microcompartment that assembles around many copies of the enzymes ribulose 1,5-bisphosphate carboxylase/ oxygenase and carbonic anhydrase to facilitate carbon fixation in cyanobacteria. Our simulations identify two classes of assembly pathways leading to encapsulation of many-molecule cargoes. In one, shell assembly proceeds concomitantly with cargo condensation. In the other, the cargo first forms a dense globule; then, shell proteins assemble around and bud from the condensed cargo complex. Although the model is simplified, the simulations predict intermediates and closure mechanisms not accessible in experiments, and show how assembly can be tuned between these two pathways by modulating protein interactions. In addition to elucidating assembly pathways and critical control parameters for microcompartment assembly, our results may guide the reengineering of viruses as nanoreactors that self-assemble around their reactants. https://doi.org/10.7554/eLife.14078.001 eLife digest Bacterial microcompartments are protein shells that are found inside bacteria and enclose enzymes and other chemicals required for certain biological reactions. For example, the carboxysome is a type of microcompartment that enables the bacteria to convert the products of photosynthesis into sugars. During the formation of a microcompartment, the outer protein shell assembles around hundreds of enzymes and chemicals. This formation process is tightly controlled and involves multiple interactions between the shell proteins and the cargo – the enzymes and other reaction ingredients – they will enclose. Understanding how to control which enzymes are encapsulated within microcompartments could help researchers to re-engineer the microcompartments so that they contain drugs or other useful products. Recent studies have used microscopy to visualize how microcompartments are assembled. However, most of the intermediate structures that form during assembly are too small and short-lived to be seen. It has therefore not been possible to explore in detail how shell proteins collect the necessary cargo and then assemble into an ordered shell with the cargo on the inside. Experiments alone are probably not enough to understand the process, especially since microcompartment assembly can currently only be studied within live cells or cellular extract. Within these complex environments it is difficult to determine the effect of any individual factor on the overall assembly process. Perlmutter, Mohajerani and Hagan have now taken a different approach by developing computational and theoretical models to explore how microcompartments assemble. Computer simulations showed that microcompartments could assemble by two pathways. In one pathway, the protein shell and cargo coalesce at the same time. In the other pathway, the cargo molecules first assemble into a large disordered complex, with the shell proteins attached on the outside. The shell proteins then assemble, carving out a piece of the cargo complex. The simulations showed that many factors affect how the shell assembles, such as the strengths of the interactions between the shell proteins and the cargo. They also identified a factor that controls how much cargo ends up inside the assembled shell. Perlmutter, Mohajerani and Hagan found that, in addition to revealing how microcompartments may assemble within their natural setting, the simulations provided guidance on how to re-engineer microcompartments to assemble around other components. This would enable researchers to create customizable compartments that self-assemble within bacteria or other host organisms, for example to carry out carbon fixation or make biofuels. A future challenge will be to investigate other aspects of microcompartment assembly, such as the factors that control the size of these compartments. https://doi.org/10.7554/eLife.14078.002 Introduction Encapsulation is a hallmark of biology. A cell must co-localize high concentrations of enzymes and reactants to perform the reactions that sustain life, and it must safely store genetic material to ensure long-term viability. While lipid-based organelles primarily fulfill these functions in eukaryotes, self-assembling protein shells take the lead in simpler organisms. For example, viruses surround their genomes with a protein capsid, while bacteria use large icosahedral shells known as bacterial microcompartments (BMCs) to sequester the enzymes and reactions responsible for particular metabolic pathways (Kerfeld et al., 2010; Axen et al., 2014; Shively et al., 1998; Bobik et al., 1999; Erbilgin et al., 2014; Petit et al., 2013; Price and Badger, 1991; Shively et al., 1973; Shively et al., 1973; Kerfeld and Erbilgin, 2015). Within diverse bacteria, BMC functions have been linked to bacterial growth, carbon fixation, symbiosis, or pathogenesis (Kerfeld and Erbilgin, 2015). Other protein-based compartments are found in bacteria and archea (e.g. encapsulins (Sutter et al., 2008) and gas vesicles (Pfeifer, 2012; Sutter et al., 2008)) and even eukaryotes (e.g. vault particles (Kickhoefer et al., 1998)), while some viruses may assemble around lipidic globules (Lindenbach and Rice, 2013; Faustino et al., 2014). Thus, understanding the factors that control microcompartment assembly and encapsulation is a central question in modern cell biology. From the perspectives of synthetic biology and nanoscience, there is great interest in reengineering BMCs or viruses as nanoreactors that spontaneously encapsulate enzymes and reagents in vitro (e.g. Luque et al., 2014; Douglas and Young, 1998; Rurup et al., 2014; Patterson et al., 2014; Patterson et al., 2012; Zhu et al., 2014; Rhee et al., 2011; Rurup et al., 2014; Wörsdörfer et al., 2012; Comas-Garcia et al., 2014), or as customizable organelles that assemble around a programmable set of core enzymes in vivo, introducing capabilities such as carbon fixation or biofuel production into bacteria or other organisms (e.g. Kerfeld and Erbilgin, 2015; Bonacci et al., 2012; Parsons et al., 2010; Choudhary et al., 2012; Lassila et al., 2014). However, the principles controlling such co-assembly processes have yet to be established, and it is not clear how to design systems to maximize encapsulation. In this article we take a step toward this goal, by developing theoretical and computational models that describe the dynamical encapsulation of hundreds of cargo molecules by self-assembling icosahedral shells. Although our models are general, we are motivated by recent experiments on a type of BMC known as the carboxysome (Kerfeld et al., 2010; Schmid et al., 2006; Iancu et al., 2007; Tanaka et al., 2008). Carboxysomes are large (40–400 nm), roughly icosahedral shells that encapsulate a dense complex of the enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) and other proteins to facilitate the Calvin-Bensen-Bassham cycle in autotrophic bacteria (Price and Badger, 1991; Shively et al., 1973; Shively et al., 1973; Iancu et al., 2007; 2010; Kerfeld et al., 2010; Tanaka et al., 2008). Recently, striking microscopy experiments visualized β-carboxysome shells assembling on and budding from procarboxysomes (the condensed complex of RuBisCO and other proteins found in the interior of carboxysomes) (Cameron et al., 2013; Chen et al., 2013). Genomic analysis suggests that many BMCs with diverse functions assemble via similar pathways (Cameron et al., 2013; Kerfeld and Erbilgin, 2015). However, the mechanisms of budding and pinch-off to close the shell remain incompletely understood because of the small size and transient nature of assembly intermediates. Moreover, experiments suggest that α-carboxysomes (another form of carboxysome) assemble by a different mechanism, in which shell assembly encapsulates an initially diffuse pool of RuBisCO (Iancu et al., 2010; Cai et al., 2015). The factors determining which of these assembly pathways occurs are unknown. BMC assembly is driven by a complex interplay of interactions among the proteins forming the external shell and the interior cargo. It is difficult, with experiments alone, to parse these interactions for those mechanisms and factors that critically influence assembly pathways, especially due to the lack of an in vitro assembly system. Models which can correlate individual factors to their effect on assembly are therefore an important complement to experiments. Previous experimental and theoretical studies of encapsulation by icosahedral shells, e.g. the assembly of viral capsids around their nucleic acid genomes (e.g. Hu and Shklovskii, 2007; Kivenson and Hagan, 2010; Elrad and Hagan, 2010; Perlmutter et al., 2013; 2014; Mahalik and Muthukumar, 2012; Zhang et al., 2013; Zhang and Linse, 2013; Hagan, 2008; Devkota et al., 2009; Dixit et al., 2006; Borodavka et al., 2012; Dykeman et al., 2013; 2014; Zlotnick et al., 2013; Johnson et al., 2004; Patel et al., 2015; Cadena-Nava et al., 2012; Comas-Garcia et al., 2012; 2014; Garmann et al., 2014a; 2014b; Malyutin and Dragnea, 2013), have demonstrated that the structure of the cargo can strongly influence assembly pathways and products. However, BMCs assemble around a cargo which is topologically different from a nucleic acid — a fluid complex comprising many, noncovalently linked molecules. We demonstrate here that changing the cargo topology leads to new assembly pathways and different critical control parameters. We present phase diagrams and analysis of dynamical simulation trajectories showing how the thermodynamics, assembly pathways, and emergent structures depend on the interactions among shell proteins and cargo molecules. Within distinct parameter ranges, we observe two classes of assembly pathways, which resemble those suggested for respectively α- or β-carboxysomes. We find that tunability of cargo loading is a key functional difference between the two classes of pathways. Shells assembled around a diffuse cargo can be varied from empty (containing almost no cargo) to completely full, whereas assembly around a condensed, procarboxysome-like complex invariably produces full shells. While we find that the encapsulated cargo becomes ordered due to confinement, complete crystalline order in the globule before encapsulation inhibits budding. We discuss these results in the context of recent observations on carboxysome assembly, and their implications for engineering BMCs, viruses or drug delivery vehicles that assemble around a fluid cargo (e.g. Refs. [Kerfeld and Erbilgin, 2015; Parsons et al., 2010; Choudhary et al., 2012; Lassila et al., 2014; Luque et al., 2014; Douglas and Young, 1998; Rurup et al., 2014; Patterson et al., 2014; Patterson et al., 2012; Zhu et al., 2014; Rhee et al., 2011; Rurup et al., 2014; Wörsdörfer et al., 2012]). Results Our model system is motivated by icosahedral viral capsids and BMCs (Tanaka et al., 2008; Kerfeld et al., 2010). Since icosahedral symmetry can accommodate at most 60 identical subunits, formation of large icosahedral structures requires subunits to assemble into different local environments. The subunits can be grouped into pentamers and hexamers, with 12 pentamers at the icosahedron vertices and the remaining subunits in hexamers. Viruses typically assemble from small oligomers of the capsid protein, which we refer to as the basic assembly unit (Hagan, 2014). Recent AFM experiments demonstrated that hexamers are the basic assembly unit during the assembly of BMC shell facets (Sutter et al., 2016), and the carboxysome major shell proteins crystallize as pentamers and hexamers (Tanaka et al., 2008). Motivated by these observations, our model considers two basic assembly units, one a pentamer and the other a hexamer, with interactions designed so that the lowest energy structure corresponds to a truncated icosahedron with 12 pentamers and 20 hexamers (Figure 1). While BMCs generally have more hexamers, our model is intended to explore the general principles of assembly around a fluid cargo rather than model a specific system. Further details of the model and a thermodynamic analysis are given in section 3 and the appendices. Figure 1 Download asset Open asset Description of the model. (A) Each shell subunit contains 'Attractors' (green circles) on the perimeter, a 'Top' (tan circle, 'T') in the center above the plane, and a 'Bottom' (purple circle, 'B' below the plane). (B) Interactions between complementary Attractors drive subunit dimerization, with the Top-Top repulsions (tan arrow) tuned to favor the subunit-subunit angle in a complete shell. Complementary pairs of attractors are indicated by green arrows in (A) for the pentamer-hexamer interface and in (B) for the hexamer-hexamer interface. (C) Bottom psuedoatoms bind cargo molecules (terra cotta circles, 'C'), while excluder atoms (blue and brown pseudoatoms in (D)) placed in the plane of the pentagon experience excluded volume interactions with the cargo. (D) The positions of excluder atoms in the lowest energy shell geometry, a truncated icosahedron with 12 pentamers (blue) and 20 hexamers (brown). https://doi.org/10.7554/eLife.14078.003 To understand how assembly around multiple cargo molecules depends on the relative strengths of interactions between components, we performed dynamical simulations as a function of the parameters controlling shell subunit-subunit (εSS), shell subunit-cargo (εSC), and cargo-cargo (εCC) interaction strengths. All energy values are given in units of the thermal energy, kB⁢T. We focus on parameters for which shell subunit-subunit interactions are too weak to drive assembly in the absence of cargo (εSS≤4.5). Except where mentioned otherwise, the cargo diameter is set equal to the circumradius of a shell subunit. For the simulated density of cargo particles, the phase behavior (in the absence of shells) corresponds to a vapor at εCC=1.3, liquid-vapor phase coexistence for εCC∈[1.6,2.0] (the phase coexistence boundary is slightly below εCC=1.6), and a solid phase at εCC=3.0. We find that tuning εCC through phase coexistence dramatically alters the typical assembly process. Strong cargo interactions (εCC≥1.6) drive formation of a globule followed by assembly and budding of a shell, such as observed for β-carboxysomes (Figure 2A, Simulation Video 1), while under weak interactions (εCC<1.6) shell assembly usually proceeds in concert with cargo encapsulation (Figure 2B, Simulation Video 2), as suggested for assembly of α-carboxysomes. We now elaborate on these classes of assembly pathways, and how the resulting assembly products depend on parameter values. Figure 2 with 2 supplements see all Download asset Open asset Snapshots illustrating typical assembly trajectories. (A) Multi-step assembly involving an amorphous globule of cargo and shell subunits. (B) Single-step assembly, in which shell assembly drives local cargo condensation. and (C) when shell-cargo interactions are too weak to condense the cargo. The values of the cargo-cargo (εCC), shell subunit-cargo (εSC), and subunit-subunit (εSS) interaction strengths are listed above each panel (all energies are in units of the thermal energy kB⁢T), and the time (in units of 106 timesteps) is noted below each image. The color scheme here and throughout the manuscript is: Red=Cargo, Blue=Pentagon Excluder, Brown=Hexagon Excluder. Attractor and Bottom pseudoatoms are omitted to aid visibility. Videos of assembly trajectories are included below. https://doi.org/10.7554/eLife.14078.004 Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Animation of a typical simulation showing assembly around a cargo globule. Parameters are εCC=1.6, εSC=7, and εSS=2.5. https://doi.org/10.7554/eLife.14078.007 Video 2 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Animation of a typical simulation showing simultaneous assembly and cargo condensation. Parameters are εCC=1.3, εSC=9, and εSS=3.5. https://doi.org/10.7554/eLife.14078.008 Assembly and budding from a cargo globule We begin by discussing assembly behavior when the cargo-cargo interactions are strong enough to drive equilibrium phase coexistence (εCC≥1.6). Near the phase boundary (εCC=1.6) a system of pure cargo particles is metastable on the timescales we simulate. However, for εSC>4, adding shell subunits drives nucleation of a cargo globule with shell subunits adsorbed on the surface. The subsequent fate of the globule depends on parameter values; typical simulation end-states are shown as a function of parameter values in Figure 3. For moderate interaction strengths (2.5≤εSS≤3.5) the globule grows to a large size, typically containing at least twice the cargo molecules that can be packaged within a complete shell. Adsorbed shell subunits then reversibly associate to form ordered clusters. Once a cluster acquires enough inter-subunit interactions to be a stable nucleus, it grows by coagulation of additional subunits or other adsorbed clusters. For the parameter set corresponding to Figure 2A, nucleation is fast in comparison to cluster growth, and thus two nuclei grow simultaneously. The last three images show the system immediately preceding and following detachment of the lower shell. Missing only one of its 32 subunits, the shell is connected to the remainder of the droplet only by a narrow neck of cargo. Insertion of the final subunit breaks the neck and completes shell detachment. The complete shell contains 120–130 cargo particles, which is slighty above random close packing (≈120 particles) but below fcc density (≈150 particles, see appendix 1.2). Figure 3 with 3 supplements see all Download asset Open asset Results of assembly around a cargo globule. (A) The most frequently observed assembly outcome is overlaid on a color map of the theoretical free energy density difference Δ⁢fassem (Equation (3)) between assembled shells and the unassembled globule. Results are plotted against the shell-cargo adsorption strength εSC and the shell-shell interaction strength εSS for indicated values of the cargo-cargo interaction strength εCC. (B) Representative snapshots of the predominant assembly outcomes shown in (A). https://doi.org/10.7554/eLife.14078.009 Figure 3—source data 1 List of all simulation outcomes for Figures 3A,5A. https://doi.org/10.7554/eLife.14078.010 Download elife-14078-fig3-data1-v3.zip Figure 3—source data 2 Criteria used to categorize assembly outcomes. The sizes of each cargo globule and shell assemblage, and associations between shell assemblages and cargo globules, were determined by clustering. The outcome was then categorized according to the criteria listed in this table. https://doi.org/10.7554/eLife.14078.011 Download elife-14078-fig3-data2-v3.zip Increasing the shell-shell interaction strength drives faster shell assembly and closure, thus limiting the size of the globule before budding. For the largest interaction strength we simulated (εSS=4.5) the globule typically does not exceed the size of a single shell, and multiple globules nucleate within the simulation box (Figure 2—figure supplement 1). This observation could place an upper bound on shell-shell interaction strengths, since multiple nucleation events were rare in the carboxysome assembly experiments (Cameron et al., 2013) (however, we discuss potential complicating factors within the cellular environment below). To quantify the relationship between assembly mechanism and parameter values, we calculate an assembly order parameter, defined as the maximum number of unassembled subunits adsorbed onto a globule during an assembly trajectory. The order parameter is shown as a function of the interaction strengths in Figure 4. For εCC≥1.6 and εSS≤3 we observe large values of the order parameter (e.g. >32, the red and yellow regions in Figure 4), which formation of a large amorphous globule with the to carboxysome shell assembly (Cameron et al., 2013). Figure with 1 supplement see all Download asset Open asset of assembly on shell-cargo and shell-shell interaction The assembly order parameter, defined as the maximum number of unassembled shell subunits adsorbed on a globule at any during a is shown as a function of εSC and εSS for indicated values of the cargo-cargo interaction εCC. of adsorbed unassembled subunits the two step assembly mechanism (Figure whereas values to simultaneous assembly and cargo (Figure Other assembly products of the parameter ranges, we observe classes of outcomes. weak shell-shell interactions to drive For and the cargo vapor phase is and the system no cargo on simulated timescales discuss below). cargo-cargo or shell-cargo interactions in unassembled where a cargo globule forms but the shell subunits on its to εSS we observe assembly on the leading to complete shells or two classes of In the first one or more shells almost but to from the droplet within simulated for when the subunit-cargo interaction does not a strong enough for the last to the cargo and close the shell. strong interactions drive the other of in which an of assembled shells the system of free subunits before any shells are In this it is also to observe in which within shells. the cargo-cargo interaction multiple narrow the parameter that leads to complete assembly and detachment. cargo globules nucleate at multiple within the simulation the of the the of εSC required for cargo resulting in shells a parameter We also observe a we refer to as in which shell assembly to the globule thus does not even to the attached The is especially for when the cargo even in the absence of shell encapsulation. For and of the number of nucleation we typically observe into a large globule. shell assembly and cargo For the cargo forms an equilibrium vapor phase in the absence of shell subunits. However, above values of εSS and the diffuse cargo molecules drive nucleation of shell The subsequent assembly depends on the shell-cargo interaction For εSC (Figure assembly only a cargo leading to but empty shells. For εSC (Figure 2B, and Simulation Video 2), the shell-cargo interactions drive local of cargo molecules. assembly and cargo then in the mechanism for assembly of α-carboxysomes (Iancu et al., 2010). Thus, tuning the shell-cargo interaction dramatically cargo with a from empty to shells around This the equilibrium (Figure by a complete shell to cargo molecules. This effect is to the of vapor below its inside of In assembly around a globule only full shells. Figure with 2 supplements see all Download asset Open asset Results of assembly around a cargo with weak interactions (A) The most frequently observed assembly outcome as a function of εSS and The of outcomes for is shown in Figure supplement and a data containing the outcome for each at each parameter set is included (Figure 3—source data 1). (B) Representative snapshots for the outcomes shown in (A). The complete shell outcomes are shown with the and to enable the encapsulated cargo. (C) The number of cargo molecules encapsulated by shells assembled in simulations is to the results of equilibrium simulations The results are all complete shells any assembled at each of the simulations were performed for simulations with and assembled shells at and were up to Assembly of full shells pathway, Figure or Figure is typically two of faster than assembly of empty shells (Figure This the key that the cargo in shell during all of molecules initially shell nucleation by interactions among clusters. the of a condensed globule a large for adsorption of additional subunits, the of subunits to the capsid, thus its The condensed cargo of the last subunits, which are by as noted for simulations of empty capsids et al., Figure how the products of assembly around cargo with weak interactions depends on parameters. While moderate parameter values lead to complete assembly, weak εSC and εSS of Figure shell leading to the In the of large εSC but weak εSS the shell-cargo interaction small disordered globules cargo particles, lower of Figure while under strong subunit and weak cargo interactions shells nucleate but cannot condense the leading to the complete but assembly for assembly around a strong interactions lead to and shells. However, the predominant of is now shell the cargo is below its the condensed globule leads to a on the shell subunits, which can the shell and thus closure of a shell. model The free energy model the parameter values required for shell assembly with no parameters map in Figure Since it is an equilibrium model and only considers the free energy difference between complete and unassembled it cannot between parameter values that lead to complete assembly or at the but simulation However, the thermodynamic does suggest that the simulations resulting in shells would on a We not show Δ⁢fassem in Figure because the globule is than assembled shells for εCC=1.3, but the of shells in our simulations roughly the of the equilibrium (Figure supplement 1). of other parameters or To investigate the results above depend on within our we performed of additional we performed simulations in which the between cargo diameter in shell subunit size was shown in Figure supplement assembly is most for our cargo diameter which the model was but assembly occurs for cargo varied a factor of we performed assembly simulations with cargo molecules with a

The interaction of 11 sulfosuccinic acid ester anionic surfactants with hydroxypropyl-β-cyclodextrin (HPβCD) were determined with reversed-phase thin-layer chromatography and the relative strength of interaction was calculated. The relationship between the strength of interaction and the physicochemical parameters of anionic surfactants was elucidated with principal component analysis (PCA). HPβCD interacted with the anionic surfactants decreasing their hydrophobicity. The distribution of the points of the strength of interaction and physicochemical parameters on the two dimensional nonlinear map of PC loadings suggested that the strength of interaction between the anionic surfactants and HPβCD is of mixed steric character, with hydrophobic and electronic forces being involved in the interaction.

We present a first-principles study of the electronic structures and properties of ideal (atomically sharp) LaAlO3/SrTiO3 (001) heterointerfaces and their variants such as a new class of quantum well systems. We demonstrate the insulating-to-metallic transition as a function of the LaAlO3 film thickness in these systems. After the phase transition, we find that conduction electrons are bound to the n-type interface while holes diffuse away from the p-type interface, and we explain this asymmetry in terms of a large hopping matrix element that is unique to the n-type interface. We build a tight-binding model based on these hopping matrix elements to illustrate how the conduction electron gas is bound to the n-type interface. Based on the `polar catastrophe' mechanism, we propose a new class of quantum wells at which we can manually control the spatial extent of the conduction electron gas. In addition, we develop a continuous model to unify the LaAlO3/SrTiO3 interfaces and quantum wells and predict the thickness dependence of sheet carrier densities of these systems. Finally, we study the external field effect on both LaAlO3/SrTiO3 interfaces and quantum well systems. Our systematic study of the electronic reconstruction of LaAlO3/SrTiO3 interfaces may serve as a guide to engineering transition metal oxide heterointerfaces.

Interaction of some steroid drugs with β-cyclodextrin polymer

Purpose. Composites on the basis x (As 2 S 3 ) y (Sb 2 S 3 ) z (SbI 3 ) glasses are suitable for creation of ferroelectric glassceramics, nonlinear dielectrics, memory media, elements for infrared and nonlinear optics, various sensors. This paper presents the results of thermal, X-ray diffraction and Raman studies of the conditions of formation and nature of crystalline inclusions in the glass matrix of the As 2 S 3 -Sb 2 S 3 -SbI 3 system during their thermal treatment. Methods. Differential thermal analysis (DTA), Raman and X-Ray diffraction spectroscopy. DTA curves were carried out in the temperature interval of 293–700 K. Heating rate was equal 3 K/min. Results. Danoheterogenous structure of glasses in As 2 S 3 -Sb 2 S 3 -SbI 3 system established on the basis of Raman spectra investigations. Their structural network is formed by only binary structural groups with heteropolar bonds (AsS 3/2 , SbS 3/2 , SbI 3 , AsI 3 ) and contains a certain amount molecular fragments with homopolar As-As and S-S bonds (As 4 S 4 , S 8 ). It has been established that the crystallization of glasses takes place in several stages. DTA curves of glasses recorded at 3 K/min revealed three exothermic effects. For example, for 40(As 2 S 3 )30(Sb 2 S 3 )30(SbI 3 ) glass temperatures maxima of these effects are equal 341, 398 and 530 K. The first and second effects are less pronounced due to nucleus formation and the formation of nanocrystals in glass matrix. The structure of the phase that arises in the glass matrix at annealing (348 and 398 K) corresponds to the structure of the crystalline SbSI. The formation of triple units SbS 2/2 I occurs as a result of the glass structural relaxation during its softening, which is accompanied by breaking and switching of homopolar and heteropolar chemical bonds in the binary groups that form the structural network of glasses. This process is accompanied by the diffusion of atoms in a distances of interatomic order. The sizes of antimony sulfoiodsde crystalline inclusions increase with annealing temperature and annealing time. The matrixes of the annealed As 2 S 3 -Sb 2 S 3 -SbI 3 system glasses, in which crystalline SbSI inclusions of different dimensions are formed, are determined mainly by As 4 S 4 structural groups linked together by fragments of chains or rings of sulfur atoms. Conclusions. DTA curves, X–ray powder diffraction patterns and Raman spectra of glasses in As 2 S 3 -Sb 2 S 3 -SbI 3 were investigated. The nanoheterogeneous structure of glasses was established. The crystallization of glasses takes place in several stages. The structure of the phase that arises in the glass matrix at annealing corresponds to the structure of crystalline antimony sulfoiodide. The sizes of SbSI crystalline inclusions are dependent on the heat treatment regimes.

Based on the exact solution method, the ground state and quench dynamics properties of one-dimensional single-spin flipped Fermi gas with repulsion interaction are studied. With the Bethe wave function, the single-body correlation function and two-body correlation function of the ground state and those between different eigen-states can be reduced into a summation of simple functions, thereby greatly reducing the computational difficulty. For the system in the ground state, the single-body correlation functions and two-body correlation functions as well as momentum distributions for spin-up particles are investigated in real space with different interaction strengths. As the interaction strength increases, the number of nodes in the single-body correlation function remains unchanged, while the amplitude of oscillation decreases. Meanwhile, the number of peaks in the two-body correlation function increases by one due to interaction, indicating that the spin-down particle behaves as a spin-up particle. The momentum distribution becomes more smooth around Fermi surface with the interaction strength increasing. The interaction quench dynamics is investigated. The system is prepared in the ground state of ideal Fermi gas, and then the interaction strength is quenched to a finite positive value. The system evolves under time-dependent Schrödinger equation. The overlap between the initial state and eigen-state of post-quench interaction strength is expressed in the form of continued multiplication. The square of the modulus of this overlap, which represents the occupation probability, is calculated. We find that the occupation probabilities of the ground state and doubly degenerated excited state always have the first and the second largest value for an arbitrary interaction strength, respectively, which means that the difference in eigenenergy between these two states gives the primary period of oscillation. For relatively large particle number (&lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ N\geqslant10$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="2-20231425_M2.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="2-20231425_M2.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;), the primary period always does not change under different interaction strengths.It is found that in the case of interaction quenching, the momentum distribution and the correlation function show periodic oscillations. When the interaction strength is adjusted to a relatively small value, the oscillation periodicity is well-defined and the oscillation amplitude is small. The system can be approximated by a two-level model. When the interaction strength increases to a very large value, the oscillation periodicity worsens and the amplitude increases, but a primary period remains unchanged. Although the overall deviation is far from the initial state, it is very close to the initial state at time &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ t=mL^2/(2\pi\hbar)$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="2-20231425_M3.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="2-20231425_M3.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;. This is because the difference between most energy eigenvalues is almost an integral multiple of energy unit &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ 2\times\left(2\pi/L\right)^{2}$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="2-20231425_M4.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="2-20231425_M4.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;.

Formation of Ln2Si2O7-Mullite eutectic (Ln = Y and Yb) micro-structure by crystallization of glass

Herein, various molecular balances used for comparing the strengths of intramolecular noncovalent interactions are reviewed. Our overview indicates that considerable quantitative insight into the strength of noncovalent interactions can be gained through the careful design of molecular balances. Many exciting opportunities certainly exist for the design of further new balances to quantify and dissect the relative strengths of noncovalent interactions as a function of solvation and the importance of the many factors that contribute to overall molecular recognition. However, even simple model molecules can show a multiplicity of intramolecular noncovalent interactions acting in a combined fashion. It is therefore essential to undertake a detailed computational analysis to identify all possible noncovalent interactions present in a selected molecular balance prior to a quantitative experimental assessment of the strength of a particular noncovalent interaction. It is also argued that the words "torsion" and "molecular balance" seem to have become inextricably linked and, in consequence, even top pan and seesaw balances have been mistakenly referred to in these terms.

Prion-like low-complexity domains (PLCDs) are involved in the formation and regulation of distinct biomolecular condensates that form via phase separation coupled to percolation. Intracellular condensates often encompass numerous distinct proteins with PLCDs. Here, we combine simulations and experiments to study mixtures of PLCDs from two RNA-binding proteins, hnRNPA1 and FUS. Using simulations and experiments, we find that 1:1 mixtures of A1-LCD and FUS-LCD undergo phase separation more readily than either of the PLCDs on their own due to complementary electrostatic interactions. Tie line analysis reveals that stoichiometric ratios of different components and their sequence-encoded interactions contribute jointly to the driving forces for condensate formation. Simulations also show that the spatial organization of PLCDs within condensates is governed by relative strengths of homotypic versus heterotypic interactions. We uncover rules for how interaction strengths and sequence lengths modulate conformational preferences of molecules at interfaces of condensates formed by mixtures of proteins.

This work aimed to study the influence of the Li, Na, and Bi oxides on the electrical properties of silica glasses. The initial compositions were based on the patents of Andrew and Herczog (1981) and Beljustin et al. (1973). A mixture design (DoE) was used to form 10 compositions (mixes). The compositions were initially studied by DSC to determine their glass transition (T g ) and crystallization (T c ) temperatures and the effect of each oxide on them. The compositions were melted in alumina crucibles in a muffle furnace at 1200 °C with isotherms at 500 and 800 °C and the glasses were poured into graphite molds. After annealing, glass samples were characterized by XRD, FTIR, and electrical impedance spectroscopy (EIS) at room temperature. As a result, the higher charge transfer resistance (R CT ) takes place for the sample with the higher silica content, and the lower R CT is achieved for the samples with the lowest content of silica and the highest content of bismuth oxide. Therefore, the ionic conductivity is favorable for compositions with the highest content of Bi and glass structures, that favor ionic mobility. • The electrical properties of SiO 2 -Na 2 O-Li 2 O-Bi 2 O 3 glass-ceramic system were studied. • The glass system was prepared by melting, annealing and heat treatment. • Li, Na and Bi ions effect was studied on the electrical properties of silica glass. • Lower charge transfer resistance was achieved for the highest content of Bi oxide. • Ionic conductivity is favorable for the highest content of Bi and glass structure.

The commercially ubiquitous liquid electrolytes for lithium-ion batteries have several shortcomings in terms of safety. Therefore, development of solid electrolytes, especially those that are glass-based, has been gaining increasing interest in recent times. However, the fundamental understanding of the changes in the glass structure and the corresponding changes in the properties due to the addition of dopants is necessary for the development of glasses. Therefore, here, we report a study on the role of vanadium on the glass structure, ionic conduction, crystallization behavior, and other properties of lithium silicate-based glasses (23Li2O–2.64K2O–2.64Al2O3–71.72SiO2) as a solid electrolyte for high-temperature Li-ion battery applications. Furthermore, we proposed a mathematical model to describe/quantify the ion-conducting channels' connectivity in glasses. The experimental glass structures were assessed using 29Si, 51V, 27Al nuclear magnetic resonance, Fourier transform infrared, and ultraviolet–visible spectroscopy techniques. The ionic conductivity was measured by impedance spectroscopy, and the crystallization behavior was studied by optical microscopy and X-ray diffraction. Furthermore, molecular dynamics simulations were also used to gain structural insights of the glasses. In the designed compositions, the addition of vanadium decreased the overall concentration of Li+ ions. However, the results revealed that the ionic conductivity improved with the addition of vanadium in spite of a decrease in the number of charge carriers. This suggests that vanadium makes the pathways easier for the conducting ions. Thus, we conclude that vanadium modifies the conduction channels to promote better hoping of the ions from one site to another.