The unexpected dominance of secondary over primary nucleation.

It is still a challenge to control the formation of particles in industrial crystallization processes. In such processes, new crystals can be generated either by primary or secondary nucleation. While in continuous stirred tank crystallization processes, secondary nucleation is thought to occur due to the shear or attrition of already present larger crystals; in antisolvent crystallization processes, where mixing at the inlets locally causes high supersaturations, primary nucleation is understood to be the main mechanism. We aim to show here that secondary nucleation is the dominant nucleation mechanism, even under conditions that are generally considered to be dominated by primary nucleation mechanisms. Measurements of primary and secondary nucleation rates under similar industrial crystallization conditions of sodium bromate in water, sodium chloride in water, glycine in water and isonicotinamide in ethanol show that the secondary nucleation rate is at least 6 orders of magnitude larger in all these systems. Furthermore, seeded fed-batch and continuous antisolvent crystallizations of sodium bromate under high local supersaturation, seeded with crystals of a specific handedness, result in a close to chirally pure crystalline product with the same handedness. This shows that indeed, enantioselective secondary nucleation is the dominant mechanism in these antisolvent crystallizations. It is even possible to use the enantioselective secondary nucleation mechanism to control the product chirality in such a process, making antisolvent crystallization a viable crystallization-enhanced deracemization technique, having a superior productivity compared to other crystallization-enhanced deracemization methods. Our finding of a dominant secondary nucleation mechanism, rather than primary nucleation, will have a strong impact on nucleation control strategies in industrial crystallization processes.

ProteinVR: Web-based molecular visualization in virtual reality.

Protein structure determines biological function. Accurately conceptualizing 3D protein/ligand structures is thus vital to scientific research and education. Virtual reality (VR) enables protein visualization in stereoscopic 3D, but many VR molecular-visualization programs are expensive and challenging to use; work only on specific VR headsets; rely on complicated model-preparation software; and/or require the user to install separate programs or plugins. Here we introduce ProteinVR, a web-based application that works on various VR setups and operating systems. ProteinVR displays molecular structures within 3D environments that give useful biological context and allow users to situate themselves in 3D space. Our web-based implementation is ideal for hypothesis generation and education in research and large-classroom settings. We release ProteinVR under the open-source BSD-3-Clause license. A copy of the program is available free of charge from http://durrantlab.com/protein-vr/, and a working version can be accessed at http://durrantlab.com/pvr/.

Revealing the photophysics of gold-nanobeacons via time-resolved fluorescence spectroscopy.

We demonstrate that time-resolved fluorescence spectroscopy is a powerful tool to investigate the conformation states of hairpin DNA on the surface of gold nanoparticles (AuNPs) and energy transfer processes in Au-nanobeacons. Long-range fluorescence quenching of Cy5 by AuNPs has been found to be in good agreement with electrodynamics modeling. Moreover, time-correlated single-photon counting (TCSPC) is shown to be promising for real-time monitoring of the hybridization kinetics of Au-nanobeacons, with up to 60% increase in decay time component and 300% increase in component fluorescence fraction observed. Our results also indicate the importance of the stem and spacer designs for the performance of Au-nanobeacons.

Effects of Secondary Metal Carbonate Addition on the Porous Character of Resorcinol-Formaldehyde Xerogels.

A deeper understanding of the chemistry and physics of growth, aggregation, and gelation processes involved in the formation of xerogels is key to providing greater control of the porous characteristics of such materials, increasing the range of applications for which they may be utilized. Time-resolved dynamic light scattering has been used to study the formation of resorcinol-formaldehyde gels in the presence of combinations of Group I (Na and Cs) and Group II (Ca and Ba) metal carbonates. It was found that the combined catalyst composition, including species and times of addition, is crucial in determining the end properties of the xerogels via its effect on growth of clusters involved in formation of the gel network. Combination materials have textural characteristics within the full gamut offered by each catalyst alone; however, in addition, combination materials that retain the small pores associated with sodium carbonate catalyzed xerogels exhibit a narrowing of the pore size distribution, providing an increased pore volume within an application-specific range of pore sizes. We also show evidence of pore size tunability while maintaining ionic strength, which significantly increases the potential of such systems for biological applications.

Effect of mixing, concentration and temperature on the formation of mesostructured solutions and their role in the nucleation of DL-valine crystals.

We report investigations on the formation of mesostructured solutions in DL-valine-water-2-propanol mixtures, and the crystallization of DL-valine from these solutions. Mesostructured liquid phases, similar to those previously observed in aqueous solutions of glycine and DL-alanine, were observed using Dynamic Light Scattering and Brownian microscopy, in both undersaturated and supersaturated solutions below a certain transition temperature. Careful experimentation was used to demonstrate that the optically clear mesostructured liquid phase, comprising colloidal mesoscale clusters dispersed within bulk solution, is thermodynamically stable and present in equilibrium with the solid phase at saturation conditions. Solutions prepared by slow cooling contained mesoscale clusters with a narrow size distribution and a mean hydrodynamic diameter of around 200 nm. Solutions of identical composition prepared by rapid isothermal mixing of valine aqueous solutions with 2-propanol contained mesoscale clusters which were significantly larger than those observed in slowly cooled solutions. The presence of larger mesoscale clusters was found to correspond to faster nucleation. Observed induction times were strongly dependent on the rapid initial mixing step, although solutions were left undisturbed afterwards and the induction times observed were up to two orders of magnitude longer than the initial mixing period. We propose that mesoscale clusters above a certain critical size are likely to be the location of productive nucleation events.

Gelation mechanism of resorcinol-formaldehyde gels investigated by dynamic light scattering.

Xerogels and porous materials for specific applications such as catalyst supports, CO2 capture, pollutant adsorption, and selective membrane design require fine control of pore structure, which in turn requires improved understanding of the chemistry and physics of growth, aggregation, and gelation processes governing nanostructure formation in these materials. We used time-resolved dynamic light scattering to study the formation of resorcinol-formaldehyde gels through a sol-gel process in the presence of Group I metal carbonates. We showed that an underlying nanoscale phase transition (independent of carbonate concentration or metal type) controls the size of primary clusters during the preaggregation phase; while the amount of carbonate determines the number concentration of clusters and, hence, the size to which clusters grow before filling space to form the gel. This novel physical insight, based on a close relationship between cluster size at the onset of gelation and average pore size in the final xerogel results in a well-defined master curve, directly linking final gel properties to process conditions, facilitating the rational design of porous gels with properties specifically tuned for particular applications. Interestingly, although results for lithium, sodium, and potassium carbonate fall on the same master curve, cesium carbonate gels have significantly larger average pore size and cluster size at gelation, providing an extended range of tunable pore size for further adsorption applications.

Assessment of GAFF and OPLS Force Fields for Urea: Crystal and Aqueous Solution Properties.

Molecular simulations such as Monte Carlo, molecular dynamics, and metadynamics have been used to provide insight into crystallization phenomena, including nucleation and crystal growth. However, these simulations depend on the force field used, which models the atomic and molecular interactions, to adequately reproduce relevant material properties for the phases involved. Two widely used force fields, the General AMBER Force Field (GAFF) and the Optimized Potential for Liquid Simulations (OPLS), including several variants, have previously been used for studying urea crystallization. In this work, we investigated how well four different versions of the GAFF force field and five different versions of the OPLS force field reproduced known urea crystal and aqueous solution properties. Two force fields were found to have the best overall performance: a specific urea charge-optimized GAFF force field and the original all-atom OPLS force field. It is recommended that a suitable testing protocol involving both solution and solid properties, such as that used in this work, is adopted for the validation of force fields used for simulations of crystallization phenomena.

Cooperative self-assembly of peptide gelators and proteins.

Molecular self-assembly provides a versatile route for the production of nanoscale materials for medical and technological applications. Herein, we demonstrate that the cooperative self-assembly of amphiphilic small molecules and proteins can have drastic effects on supramolecular nanostructuring of resulting materials. We report that mesoscale, fractal-like clusters of proteins form at concentrations that are orders of magnitude lower compared to those usually associated with molecular crowding at room temperature. These protein clusters have pronounced effects on the molecular self-assembly of aromatic peptide amphiphiles (fluorenylmethoxycarbonyl- dipeptides), resulting in a reversal of chiral organization and enhanced order through templating and binding. Moreover, the morphological and mechanical properties of the resultant nanostructured gels can be controlled by the cooperative self-assembly of peptides and protein fractal clusters, having implications for biomedical applications where proteins and peptides are both present. In addition, fundamental insights into cooperative interplay of molecular interactions and confinement by clusters of chiral macromolecules is relevant to gaining understanding of the molecular mechanisms of relevance to the origin of life and development of synthetic mimics of living systems.

Rapid Assessment of Crystal Nucleation and Growth Kinetics: Comparison of Seeded and Unseeded Experiments.

In this work, we outlined an experimental workflow enabling the rapid assessment of primary and secondary nucleation and crystal growth kinetics. We used small-scale experiments in agitated vials with in situ imaging for crystal counting and sizing to quantify nucleation and growth kinetics of α-glycine in aqueous solutions as a function of supersaturation at isothermal conditions. Seeded experiments were required to assess crystallization kinetics when primary nucleation is too slow, especially at lower supersaturations often encountered in continuous crystallization processes. At higher supersaturations, we compared results from seeded and unseeded experiments and carefully analyzed interdependencies of primary and secondary nucleation and growth kinetics. This approach allows for the rapid estimation of absolute values of primary and secondary nucleation and growth rates without relying on any specific assumptions about functional forms of corresponding rate expressions used for estimation approaches based on fitting population balance models. Quantitative relationships between nucleation and growth rates at given conditions provide useful insights into crystallization behavior and can be explored to rationally manipulate crystallization conditions for achieving desirable outcomes in batch or continuous crystallization processes.

Machine Learning-Derived Correlations for Scale-Up and Technology Transfer of Primary Nucleation Kinetics.

Scaling up and technology transfer of crystallization processes have been and continue to be a challenge. This is often due to the stochastic nature of primary nucleation, various scale dependencies of nucleation mechanisms, and the multitude of scale-up approaches. To better understand these dependencies, a series of isothermal induction time studies were performed across a range of vessel volumes, impeller types, and impeller speeds. From these measurements, the nucleation rate and growth time were estimated as parameters of an induction time distribution model. Then using machine learning techniques, correlations between the vessel hydrodynamic features, calculated from computational flow dynamic simulations, and nucleation kinetic parameters were analyzed. Of the 18 machine learning models trained, two models for the nucleation rate were found to have the best performance (in terms of % of predictions within experimental variance): a nonlinear random Forest model and a nonlinear gradient boosting model. For growth time, a nonlinear gradient boosting model was found to outperform the other models tested. These models were then ensembled to directly predict the probability of nucleation, at a given time, solely from hydrodynamic features with an overall root mean square error of 0.16. This work shows how machine learning approaches can be used to analyze limited datasets of induction times to provide insights into what hydrodynamic parameters should be considered in the scale-up of an unseeded crystallization process.

Salt-induced control of supramolecular order in biocatalytic hydrogelation.

Biocatalytic action and specific ion effects are both known to have dramatic effects on molecular self-assembly and hydrogelation. In this paper, we demonstrate that these effects are highly cooperative. Biocatalytic hydrogelation of Fmoc peptides in the presence of salts combines kinetic (through enzymatic catalysis) and thermodynamic (specific ion and protein templating) contributions when applied in combination. Spectroscopic data (obtained by fluorescence spectroscopy and circular dichroism) revealed that hydrophobic interactions are greatly affected, giving rise to differential chiral organization and supramolecular structure formation. The kinetic effects of catalytic action could be removed from the system by applying a heat/cool cycle, giving insight into the thermodynamic influence of both protein and salt on these systems and showing that the effects of catalysis, templating, and salts are cooperative. The variable molecular interactions are expressed as variable material properties, such as thermal stability and mechanical strength of the final gel-phase material. To gain more insight into the role of the enzyme, beyond catalysis, in the underlying mechanism, static light scattering is performed, which indicates the different mode of aggregation of the enzyme molecules in the presence of different salts in aqueous solution that may play a role to direct the assembly via templating. Overall, the results show that the combination of specific salts and enzymatic hydrogelation can give rise to complex self-assembly behaviors that may be exploited to tune hydrogel properties.

Validity of particle size analysis techniques for measurement of the attrition that occurs during vacuum agitated powder drying of needle-shaped particles.

Analysis of needle-shaped particles of cellobiose octaacetate (COA) obtained from vacuum agitated drying experiments was performed using three particle size analysis techniques: laser diffraction (LD), focused beam reflectance measurements (FBRM) and dynamic image analysis. Comparative measurements were also made for various size fractions of granular particles of microcrystalline cellulose. The study demonstrated that the light scattering particle size methods (LD and FBRM) can be used qualitatively to study the attrition that occurs during drying of needle-shaped particles, however, for full quantitative analysis, image analysis is required. The algorithm used in analysis of LD data assumes the scattering particles are spherical regardless of the actual shape of the particles under evaluation. FBRM measures a chord length distribution (CLD) rather than the particle size distribution (PSD), which in the case of needles is weighted towards the needle width rather than their length. Dynamic image analysis allowed evaluation of the particles based on attributes of the needles such as length (e.g. the maximum Feret diameter) or width (e.g. the minimum Feret diameter) and as such, was the most informative of the techniques for the analysis of attrition that occurred during drying.

Tuning Interfacial Concentration Enhancement through Dispersion Interactions to Facilitate Heterogeneous Nucleation.

Classical molecular dynamics simulations were used to investigate how dispersion (van der Waals) interactions between non-polar, hydrophobic surfaces and aqueous glycine solutions affect the solution composition, molecular orientation, and dynamics at the interface. Simulations revealed that dispersion interactions lead to a major increase in the concentration of glycine at the interface in comparison with the bulk solution, resulting from a competition between solute and solvent molecules to be or not to be near the interface. This can then lead to kinetic and/or structural effects facilitating heterogeneous nucleation of glycine at non-polar surfaces, in agreement with recent observations for tridecane, graphene, and polytetrafluoroethylene. A novel parameterization process was developed to map a model surface with tunable dispersion interactions to heptane, tridecane, and graphite materials. The model surface was capable of reproducing the solution structure observed in fully atomistic simulations with excellent agreement and also provided good agreement for dynamic properties, at a significantly reduced computational cost. This approach can be used as an effective tool for screening materials for heterogeneous nucleation enhancement or suppression, based on non-specific dispersion interactions based on bulk material molecular properties, rather than interfacial functional groups, templating or confinement effects.

Temperature Dependence of Solubility Predicted from Thermodynamic Data Measured at a Single Temperature: Application to α, ß, and γ-Glycine.

Understanding of solid-liquid equilibria for polymorphic systems is crucial for rational design and efficient operation of crystallization processes. In this work, we present a framework to determine the temperature dependent solubility based on experimentally accessible thermodynamic data measured at a single temperature. Using this approach, we investigate aqueous solubility of α, ß, and γ-glycine, which, despite numerous studies, have considerable quantitative uncertainty, in particular for the most stable (γ) and the least stable (ß) solid forms. We benchmark our framework on α-glycine giving predictions in excellent agreement with direct solubility measurements between 273-340 K, using only thermodynamic data measured at the reference temperature (298.15 K). We analyze the sensitivity of solubility predictions with respect to underlying measurement uncertainty, as well as the excess Gibbs free energy models used to derive required thermodynamic quantities before providing solubility predictions for ß and γ-glycine between 273-310 and 273-330 K, respectively. Crucially, this approach to predict solubility as a function of temperature does not rely on measurement of solute melting properties which will be particularly useful for compounds that undergo thermal decomposition or polymorph transition prior to melting.

Modeling Diffusive Mixing in Antisolvent Crystallization.

Diffusion controls local concentration profiles at interfaces between segregated fluid elements during mixing processes. This is important for antisolvent crystallization, where it is intuitively argued that local concentration profiles at interfaces between solution and antisolvent fluid elements can result in significant supersaturation overshoots over and above that at the final mixture composition, leading to poorly controlled nucleation. Previous work on modeling diffusive mixing in antisolvent crystallization has relied on Fickian diffusion, where concentration gradients are the driving force for diffusion. This predicts large overshoots in the supersaturation at interfaces between solution and antisolvent, as is often intuitively expected. However, chemical potential gradients provide a more physically realistic driving force for diffusion, and in highly nonideal solutions, such as those in antisolvent crystallization, this leads to nonintuitive behavior. In particular, as solute diffusion toward antisolvent is severely hindered, it can diffuse against its concentration gradient away from antisolvent. We apply thermodynamically consistent diffusion model based on the multicomponent Maxwell-Stefan formulation to examine diffusive mixing in a nonideal antisolvent crystallization system. Large supersaturation overshoots above that at the final mixture composition are not found when a thermodynamically consistent approach is used, demonstrating that these overshoots are modeling artifacts and are not expected to be present in physical systems. In addition, for certain conditions, localized liquid-liquid spinodal demixing is predicted to occur during the diffusive mixing process, even when the final mixture composition is outside the liquid-liquid phase separation region. Intermittent spinodal demixing driven by diffusive mixing may provide a novel explanation for differences of nucleation behaviors among various antisolvents.

Molecular Dynamics Investigation of Clustering in Aqueous Glycine Solutions.

Recent experiments with undersaturated aqueous glycine solutions have repeatedly exhibited the presence of giant liquid-like clusters or nanodroplets around 100 nm in diameter. These nanodroplets re-appear even after careful efforts for their removal and purification of the glycine solution. The composition of these clusters is not clear, although it has been suggested that they are mainly composed of glycine, a small and very soluble amino acid. To gain insights into this phenomenon, we study the aggregation of glycine in aqueous solutions at concentrations below the experimental solubility limit using large-scale molecular dynamics simulations under ambient conditions. Three protonation states of glycine (zwitterion = GLZ, anion = GLA, and cation = GLC) are simulated using molecular force fields based on the 1.14*CM1A partial charge scheme, which incorporates the OPLS all-atom force field and TIP3P water. When initiated from dispersed states, we find that giant clusters do not form in our simulations unless salt impurities are present. Moreover, if simulations are initiated from giant cluster states, we find that they tend to dissolve in the absence of salt impurities. Therefore, the simulation results provide little support for the possibility that the giant clusters seen in experiments are composed purely of glycine (and water). Considering that strenuous efforts are made in experiments to remove impurities such as salt, we propose that the giant clusters observed might instead result from the aggregation of reaction products of aqueous glycine, such as diketopiperazine or other oligoglycines which may be difficult to separate from glycine using conventional methods, or their co-aggregation with glycine.