Complex Phase Behavior Research Articles

Cheap portable electroformed giant unilamellar vesicles preparation kit

The membrane of a cell separates the internal and external media of the cell and contributes to a variety of important processes, including gradient maintenance and signal transduction. Synthetic lipid-made vesicles are commonly utilized as cell membrane model systems. These could be liposomes or giant unilamellar vesicles (GUVs) in most cases. Liposomes are typically less than 0.5 microns in size, limiting their use for most microscopy experiments. GUVs are a form of liposomes that ranges in size from 5 to 200 microns and are ideal for examining complex phase behaviors of biomembranes using the classical optical setting. This study details the step-by-step development of a portable, light and low-cost kit for generating GUVs by electroformation. Our kit contains an in-built electronic circuitry, and the GUV generation setup, consisting of 3 ITO-coated glasses with heating electrode connections. Approximately 600 µl of GUVs can be produced in one experiment, while the amount could be increased by changing the dimensions of the GUV generation setup. Finally, the originality of the study comes from the fact that many users from different fields unfamiliar with electronics can use our home-built cost-effective approach instead of their expensive commercial counterparts.

Structural, Chemical and Magnetic Characterization of Quartz Sand from Cluj Area, Romania for Future Beneficiation in the Glass Industry.

For use in crystal glass production, quartz sand must contain less than 0.09% iron. If the sand contains more than 0.09% Fe, the iron must be removed. In the present study, quartz sand from tailings ponds near the Cluj area of Romania is analyzed for potential use in the glass industry, after magnetic separation. The particle size distribution of raw sand was determined, and mineralogical analyses was realized. Using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX), morphology and elemental distribution maps on the particle was performed. The evolution of the iron content versus the particle size was analyzed. Using X-ray diffraction, the phases occurring in the sand were investigated in relation to the particle size. Magnetic separation with two magnets, having different energy, was performed for identifying the phases attached to the magnetic particles. Magnetic hysteresis measurements evidenced complex and multiple iron phase behavior.

Challenges in modeling microemulsion phase behavior

AbstractModeling the complex phase behavior of microemulsions has been the subject of research for decades for hydrocarbon oil and organic contaminant recoveries. The empirical Hand model has been the standard scheme for calculating phase behavior in reservoir simulators. On the other hand, the hydrophilic–lipophilic deviation (HLD) model in conjunction with the net curvature and average curvature models (HLD‐NAC) has provided some theoretical basis for modeling microemulsions. While the HLD‐NAC has been successful in describing salinity scans, only recently an HLD‐NAC approach was devised that could represent binodal curves with physical behavior, resulting in a succession of new microemulsion flash algorithms. While these new algorithms claimed to include the effects of cosolvent, there are only few works in the literature, which give an in‐depth description of the cosolvent modeling. Furthermore, to the best of our knowledge, no work has investigated the capability of HLD‐NAC models for predicting data for different cosolvent concentrations nor did any work present a comprehensive study of the application of HLD‐NAC against Hand model for formulations including cosolvents. This paper aims to contribute in filling such gap. We modify different versions of the new HLD‐NAC approach with physical binodal curves by providing more flexibility into controlling the binodal heights with the cosolvent concentration and investigate how well they can calibrate and predict data. Experimental data sets were considered here. The results suggest that some of the HLD‐NAC approaches produced negative solubilization ratios and the Hand model gave better predictions of the ternary diagram for extreme salinity values.

Energy Transfer in Aqueous Light Harvesting Antennae Based on Brush-like Inter-Conjugated Polyelectrolyte Complexes.

Conjugated polyelectrolytes (CPEs) have the potential to serve as building blocks of artificial light-harvesting systems. This is primarily due to their delocalized electronic states and potential for hierarchical self-assembly. We showed previously that inter-CPE complexes composed of oppositely charged exciton-donor and exciton-acceptor CPEs displayed efficient electronic energy transfer. However, near ionic charge equivalence, complexed CPE chains become net-neutral and thus experience a precipitous drop in aqueous solubility. To increase the stability and to rationally manipulate the phase behavior of inter-CPE complexes, we synthesized a series of highly water-soluble exciton-donor CPEs composed of alternating ionic and polar nonionic fluorene monomers. The nonionic monomer contained oligo(ethyleneglycol) sidechains of variable length. We then formed exciton donor-acceptor complexes and investigated their relative energy transfer efficiencies in the presence of a fixed exciton-acceptor CPE. We find that, even when the polar nonionic sidechains become quite long (nine ethyleneglycol units), the energy transfer efficiency is hardly affected so long as the inter-CPE network retains a net polyelectrolyte charge. However, near the onset of spontaneous phase separation, we observe a clear influence of the length of the oligo(ethyleneglycol) sidechains on the photophysics of the complex. Our results have implications for the use of polyelectrolyte phase separation to produce aqueous light-harvesting soft materials.

Structure and Phase Behavior of Interpolyelectrolyte Complexes of PDADMAC and Hydrophobically Modified PAA (HM‐PAA)

AbstractBy combining oppositely charged polydiallyldimethylammonium chloride (PDADMAC) and sodium polyacrylate (NaPA), interpolyelectrolyte complexes (IPECs) can be formed in aqueous solution. Such IPECs are studied for rather short NaPA and under variation of the Mw of PDADMAC. The focus is on elucidating the effect of having a hydrophobic modification of the NaPA, which is introduced by having 10 mol% of the monomeric units substituted by ones carrying a dodecyl alkyl chain. This modification renders the complexes more hydrophobic, which is seen in the fact that precipitation of the complexes occurs at a lower mixing ratio and the biphasic region is also wider. The structures of the soluble IPECs are studied by a combination of light and neutron scattering (SANS). It is observed that the complexes formed possess typical radii of gyration of ≈30–40 nm, which become somewhat smaller with increasing length of the PDADMAC chain. The SANS data can be described well with the Beaucage model for complexes, where locally small hydrophobic domains of cylindrical shape are formed, whose persistence length decreases with increasing content of NaPA in the complexes. In contrast no such structures are seen for NaPA without the hydrophobic modification. The cylindrical domains are then arranged within larger‐sized clusters of 30–40 nm, which become more compact with reduced length of the PDAMAC chains. The structure of the IPECs is largely determined by the presence of the hydrophobic modification of the NaPA and is further controlled by the length of the hydrophobic modification. Such IPECs of controlled structure, relatively small size, and containing hydrophobic domains are potentially interesting as delivery systems due to having domains of variable polarity.

Applying Massively Parallel Interface for MPFA scheme with advanced linearization for fluid flow in porous media

We apply Massively Parallel Interface for MPFA-O scheme with state-of-the-art Operator-Based Linearization (OBL) approach for multiphase flow in porous media. The implementation of MPFA-O scheme enhances the modelling capabilities for non-K-orthogonal mesh. A fully implicit scheme is applied to guarantee the stability of solutions when a mass-based formulation is involved to keep the flexibility of the framework for general-purpose reservoir simulation. As the MPFA-O introduces more non-zeros elements in the Jacobian matrix than the traditional TPFA, massively parallel computations via Message Passing Interface (MPI) in this work help to guarantee competitive computational efficiency for high-fidelity geological models. Concerning the Jacobian assembly hassle, we apply the OBL approach which introduces operators combining the fluid and rock properties in the conservation equations and discretizes the operators in the physical parameter space. By computing values and derivatives of the operators via a multilinear interpolation, the assembly of Jacobian matrix and residual vector could be drastically simplified. Another benefit of the OBL is that by only evaluating operator values on the predefined nodes in the physical parameter space, the overhead related to complex phase behavior and property evaluation is significantly reduced. In the end, we present several benchmark cases to rigorously demonstrate the accuracy, convergence, and robustness of the framework and two challenging field-scale cases to further prove its computing performance and parallel scalability.

Solubility of formaldehyde in mixtures of water + methanol + poly(oxymethylene) dimethyl ethers

Poly(oxymethylene)dimethyl ethers (OME) are a highly interesting group of oxygenates that could be used in the future as a sustainable and clean alternative to fossil diesel. Processes to produce them from formaldehyde and methanol have been developed. One remaining problem that may occur in these processes is the formation of a formaldehyde-rich solid, which has to be avoided. Nothing is known about the conditions under which this solid is formed. This gap of knowledge is closed in the present work. Based on previous fundamental work of our group on the solid-liquid equilibrium (SLE) in the system (formaldehyde + water + methanol), we have experimentally studied the SLE in the systems (formaldehyde + water + OME) and (formaldehyde + water + methanol + OME) at temperatures between 273 K and 303 K, for two distinct species: OME2 and OME4. The solid phase consisted basically of pure oligomeric formaldehyde. The system (formaldehyde + water + OME) shows a complex phase behavior due to the superposition of liquid-liquid equilibria (LLE) and SLE, resulting in a solid-liquid-liquid equilibrium (SLLE). As in our previous work, the formation of the solid was described with a physico-chemical model, which considers the reactions between formaldehyde, water, and methanol. Both the equilibrium and the slow kinetics of the process are described well by the model.

Tuning the ionic character of sodium dodecyl sulphate via counter-ion binding: An experimental and computational study

Solutions of surfactants exhibit remarkable features, such as a tunable amphiphilic character, which can further be varied for ionic surfactants through variations in their Coulombic interactions. These properties are very useful in many industrial applications such as in extraction, purification, and formulation processes, as detergents, wetting agents, or emulsifiers. Rather unexpectedly, the addition of tetrabutylammonium chloride ([N4,4,4,4]Cl) to solutions of the ionic surfactant of sodium dodecyl sulphate (SDS) results in the appearance of a phase transition above the lower critical solution temperature (LCST), a property usually associated with non-ionic surfactants. The aim of this study is to provide a detailed nanoscopic scenario on the interaction between SDS micelles and [N4,4,4,4]Cl moieties to better understand the nature of the LCST cloud point and how to confer it to a given ionic surfactant system. A coarse-grained molecular dynamics (CG-MD) computational framework, under the latest MARTINI 3.0 force field, was developed and validated using available literature data. The impact of [N4,4,4,4]Cl concentration in the phase of SDS micellar aqueous solutions was then characterized and compared using experimental results. Specifically, dynamic light scattering (DLS) measurements and small-angle X-ray scattering (SAXS) profiles were obtained at different [N4,4,4,4]+/[DS]- molar ratios (from 0.0 to 1.0) and compared with the CG-MD results. A good agreement between computer simulations and experimental findings was obtained, reinforcing the suitability of GC-MD to simulate complex phase behaviors. When the [N4,4,4,4]+/[DS]- molar ratio is < 0.5, a weak impact of the cation in the micellar distribution was found whereas for ratios > 0.5, the system yielded clusters of enclosed small [DS]- aggregates. Thus, the CG-MD simulations showed the formation of mixed [DS]- and [N4,4,4,4]+ aggregates with [N4,4,4,4]+ cations acting as a bridge between small [DS]- micelles. The CG-MD simulation framework developed in this work captured the role of [N4,4,4,4]+ in the micellar phase transition whilst improving the results obtained with preceding computer models for which the limitations on capturing SDS and [N4,4,4,4]Cl mixtures in aqueous solutions are also shown in detail.

Acid Gas Re-Injection System Design Using Machine Learning

An “energy evolution” is necessary to manifest an environmentally sustainable world while meeting global energy requirements, with natural gas being the most suitable transition fuel. Covering the ever-increasing demand requires exploiting lower value sour gas accumulations, which involves an acid gas treatment issue due to the greenhouse gas nature and toxicity of its constituents. Successful design of the process requires avoiding the formation of acid gas vapor which, in turn, requires time-consuming and complex phase behavior calculations to be repeated over the whole operating range. In this work, we propose classification models from the Machine Learning field, able to rapidly identify the problematic vapor/liquid encounters, as a tool to accelerate phase behavior calculations. To set up this model, a big number of acid gas instances are generated by perturbing pressure, temperature, and acid gas composition and offline solving the stability problem. The generated data are introduced to various classification models, selected based on their ability to provide rapid answers when trained. Results show that by integrating the resulting trained model into the gas reinjection process simulator, the simulation process is substantially accelerated, indicating that the proposed methodology can be readily utilized in all kinds of acid gas flow simulations.

A Precision Polyanion for High Performance Lithium-Ion Transport in Polymer Blend Electrolytes

A novel precision single ion conductor with an N-((trifluoromethyl)sulfonyl)phenylsulfonimide lithium salt covalently bound to every fifth carbon of a polyethylene backbone, p5PhTFSI-Li, was synthesized via post polymerization modification. The conversion to a trifluoromethanesulfonimide (TFSI) anion from the parent polymer bearing a phenylsulfonate anion was highly efficient (~90%) as determined by 19F-NMR analysis and corroborated through other spectroscopic methods. The bulky and flexible TFSI anion led to a reduced and observable glass transition temperature of ~199 °C and an improved thermal stability up to ~375 °C. Blending of p5PhTFSI-Li with poly(ethylene oxide) at various compositions was performed to investigate electrochemical performance and transference numbers with respect to the lithium electrode using a combination of impedance and polarization methods. At 90 °C, this system displayed high conductivity up to 2.00 X 10-4 S cm-1 and transference numbers were near unity independent of temperature, making this system highly competitive with other polyanion/polyether blend electrolytes. Investigations into the complex miscibility and phase behavior of these blends at various compositions was also probed by a combination of microscopy and differential scanning calorimetry.

Mesoscale structure–function relationships in mitochondrial transcriptional condensates

In live cells, phase separation is thought to organize macromolecules into membraneless structures known as biomolecular condensates. Here, we reconstituted transcription in condensates from purified mitochondrial components using optimized invitro reaction conditions to probe the structure-function relationships of biomolecular condensates. We find that the core components of the mt-transcription machinery form multiphasic, viscoelastic condensates invitro. Strikingly, the rates of condensate-mediated transcription are substantially lower than in solution. The condensate-mediated decrease in transcriptional rates is associated with the formation of vesicle-like structures that are driven by the production and accumulation of RNA during transcription. The generation of RNA alters the global phase behavior and organization of transcription components within condensates. Coarse-grained simulations of mesoscale structures at equilibrium show that the components stably assemble into multiphasic condensates and that the vesicles formed invitro are the result of dynamical arrest. Overall, our findings illustrate the complex phase behavior of transcribing, multicomponent condensates, and they highlight the intimate, bidirectional interplay of structure and function in transcriptional condensates.

The Phase Behavior of a Mixture of the Ionic Liquids [C8mim][AzoO] and [C8mim][PF6

Abstract Mixtures of the ionic liquids 1-methyl-3-octylimidazolium phenylazophenolate, abbreviated as [C8mim][AzoO] and 1-methyl-3-octylimidazolium hexafluorophosphate, abbreviated as [C8mim][PF6], have been known as smart materials with high moldability, electric conductivity, and self-healing properties. However, the structure and the phase behavior at low temperature are not well known in detail, which may change depending on the composition. Differential scanning calorimetry shows that a tiny amount of water is required for the crystallization of neat [C8mim][AzoO]. X-ray diffraction profiles indicate the coexistence of the crystalline phase and the liquid state for the one-component ionic liquid. Polarized optical microscopy indicates that the crystalline phase comprises needle-like microcrystals and coarse crystals. As a result, the phase diagram of the mixture [C8mim][AzoO]n[PF6]1−n is established. The mixture undergoes a complex phase behavior containing glass transition and crystallization, which drastically changes depending on the composition. Noteworthy, it is indicated that [C8mim][PF6] promotes the formation of microcrystals of [C8mim][AzoO], but not coarse crystals. On the other hand, [C8mim][AzoO] facilitates the supercooling of the liquid [C8mim][PF6]. These asymmetric effects enable [C8mim][AzoO]n[PF6]1−n to display moldable but electroconductive features.

Phase Behavior and Miscibility in Two-Component Glycolipid Monolayers.

Glycolipids are known to be involved in the formation of ordered functional domains in biological membranes. Since the structural characterization of such domains is difficult, most studies have so far dealt with lipid mixtures containing only one glycolipid component at a time, although biological membranes usually contain several glycolipid species, which can result in more complex structures and phase behavior. Here, we combine classical isotherm measurements with surface-sensitive grazing-incidence X-ray diffraction to investigate the phase behavior and miscibility in Langmuir monolayers of binary glycolipid mixtures. We find that the phase behavior has a subtle dependence on the saccharide headgroup chemistry. For compatible chemistries, molecular superlattice structures formed by one of the glycolipid species are conserved and can host foreign glycolipids up to a defined stoichiometry. In contrast, for sterically incompatible saccharide chemistries, the superlattice is lost even if both species are able to form such structures in their pure forms. Our results suggest that related phenomena may play important roles also in biological contexts.

Understanding the phase and solvation behavior of fluorinated ionic liquids

• Effect of perfluoroalkyl chains on the properties and phase equilibria of FILs. • VLE of FILs show the need of optimal fluorine density to enhance gas absorption. • LLE of FILs reveal high surfactant behavior in water and improved PFCs solubility. • SLE of FILs uncover formation of eutectic mixtures with complex phase behavior. • FILs show huge relevance in GHGs capturing processes and biomedical applications. Fluorinated ionic liquids (FILs) are defined as molecules having fluorinated tags equal to or longer than 4 carbon atoms in the anion and/or cation structures. They present nanosegregated domains making them 3-in-1 solvents with exceptional properties, including an attractive solubility power. This work is an important contribution towards understanding the current research on the phase behavior of FILs, which may be used as task-specific materials for industrial applications. An overview of the main works published in the last two decades is presented, concerning gas solubility in FILs, the application of membranes to improve the gas absorption in FILs, and the use of modeling approaches to ease the application of FILs in gas capture and separation processes, with emphasis on the relationship between the structural properties and their performance. Contributions concerning the liquid-liquid and solid-liquid equilibria behavior of FILs, including the liquid-liquid equilibria (LLE) of FILs in water and perfluoroalkanes, and the solid-liquid equilibria (SLE) of solid FILs in water and mixtures of FILs are also presented. Regarding the absorption of gases in FILs, a careful analysis of the published works reveals that: (1) an optimal density of fluorine atoms in FILs structure is required to positively impact the absorption of different gases, (2) the functionalization of membranes can be also a useful method to improve the performance in separation processes, and (3) modeling tools can ease the screening of the features that promote the absorption of gases by FILs. The study of FILs LLE showed a rich phase behavior with water and perfluoroalkanes and the enhanced surfactant power of FILs, which is highly dependent on the length of the hydrogenated and fluorinated side chains. Finally, studying the SLE of FILs mixtures allowed the formation of deep eutectic systems that enlarges the applicability of FILs.

Disorder Dynamics in Battery Nanoparticles During Phase Transitions Revealed by Operando Single‐Particle Diffraction

Structural and ion-ordering phase transitions limit the viability of sodium-ion intercalation materials in grid scale battery storage by reducing their lifetime. However, the combination of phenomena in nanoparticulate electrodes creates complex behavior that is difficult to investigate, especially on the single nanoparticle scale under operating conditions. In this work, operando single-particle x-ray diffraction (oSP-XRD) is used to observe single-particle rotation, interlayer spacing, and layer misorientation in a functional sodium-ion battery. oSP-XRD is applied to Na$_{2/3}$[Ni$_{1/3}$Mn$_{2/3}$]O$_{2}$, an archetypal P2-type sodium-ion positive electrode material with the notorious P2-O2 phase transition induced by sodium (de)intercalation. It is found that during sodium extraction, the misorientation of crystalline layers inside individual particles increases before the layers suddenly align just prior to the P2-O2 transition. The increase in the long-range order coincides with an additional voltage plateau signifying a phase transition prior to the P2-O2 transition. To explain the layer alignment, a model for the phase evolution is proposed that includes a transition from localized to correlated Jahn-Teller distortions. The model is anticipated to guide further characterization and engineering of sodium-ion intercalation materials with P2-O2 type transitions. oSP-XRD therefore opens a powerful avenue for revealing complex phase behavior in heterogeneous nanoparticulate systems.

A gradient-based deep neural network model for simulating multiphase flow in porous media

Simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO2 storage sites, where CO2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and CO2 saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet

It is challenging to predict the production performance of unconventional reservoirs because of the sediment heterogeneity, intricate flow channels, and complex fluid phase behavior. The traditional oil production prediction methods (e.g., decline curve analysis and reservoir simulation modeling forecasting) are subjective. This paper presents a machine learning-based time series forecasting method, which considers the existing data as time series and extracts the salient characteristics of historical data to predict values of a future time sequence. We used time series forecasting because of the historical fluctuations in production well and reservoir operations. Three algorithms were studied and compared to address the limitations of traditional production forecasting: Auto-Regressive Integrated Moving Averages (ARIMA), Long-Short-Term Memory (LSTM) network, and Prophet. This study starts with the representative oil production data from a well located in an unconventional reservoir in the Denver-Julesburg (DJ) Basin. 70% of the data was used for model training, whereas the remaining 30% of data was used to evaluate the performance of the above-mentioned methods. Then, the decline curve analysis and reservoir simulation modeling forecasting were applied for comparison. The advantages of the machine-learning models include a simple workflow, no prior assumption about the reservoir type, fast prediction, and reliable performance prediction for a typical fluctuating declining curve. More importantly, the ‘Prophet’ model captures production fluctuation caused by winter impact, which can attract the operator's attention and prevent potential failures. This has rarely been explored and discussed by previous studies. The application of ARIMA, LSTM, and Prophet methods to 65 wells in the DJ Basin show that ARIMA and LSTM perform better than Prophet—probably because not all oil production data include seasonal influences. Furthermore, the wells in the nearby pads can be studied using the same parameter values in ARIMA and LSTM for predicting oil prediction in a transferred learning framework. Specifically, we observed that ARIMA is robust in predicting the oil production rate of wells across the DJ Basin.

Investigating the Potential of Ethyl Cellulose and a Porosity-Increasing Agent as a Carrier System for the Formulation of Amorphous Solid Dispersions.

In the present work, an insoluble polymer, i.e., ethyl cellulose (EC), was combined with the water-soluble polyvinylpyrrolidone (PVP) as a carrier system for the formulation of amorphous solid dispersions. The rationale was that by conjoining these two different types of carriers a more gradual drug release could be created with less risk for precipitation. Our initial hypothesis was that upon contact with the dissolution medium, PVP would be released, creating a porous EC matrix through which the model drug indomethacin could diffuse. On the basis of observations of EC as a coating material, the effect of the molecular weight of PVP, and the ratio of EC/PVP on the miscibility of the polymer blend, the solid state of the solid dispersion and the drug release from these solid dispersions were investigated. X-ray powder diffraction, modulated differential scanning calorimetry, and solid-state nuclear magnetic resonance were used to unravel the miscibility and solid-state properties of these blends and solid dispersions. Solid-state nuclear magnetic resonance appeared to be a crucial technique for this aspect as modulated differential scanning calorimetry was not sufficient to grasp the complex phase behavior of these systems. Both EC/PVP K12 and EC/PVP K25 blends were miscible over the entire composition range, and addition of indomethacin did not alter this. Concerning the drug release, it was initially thought that more PVP would lead to faster drug release with a higher probability that all of the drug molecules would be able to diffuse out of the EC network as more pores would be created. However, this view on the release mechanism appeared to be too simplistic as an optimum was observed for both blends. On the basis of this work, it could be concluded that drug release from this complex ternary system was affected not only by the ratio of EC/PVP and the molecular weight of PVP but also by interactions between the three components, the wettability of the formulations, and the viscosity layer that was created around the particles.

Thermochromic Phase Transitions of Long Odd-Chained Inorganic-Organic Layered Perovskite-Type Hybrids [(CnH2n+1NH3)2PbI4], n = 11, 13, and 15.

We investigate the last members of a series of inorganic-organic hybrid materials of the general formula [(CnH2n+1NH3)2PbI4] (abbreviated CnPbI). The self-assembly of the inorganic and organic components has a perovskite-like structure as the two-dimensional (2D) inorganic layers have four corners of the lead(II) iodide octahedra being shared out. The inorganic layers are separated by bilayers of alkylammonium chains, in this case with n = 11, 13, and 15. These materials exhibit complex phase behavior in the temperature range from -20 to + 81 °C. Differential scanning calorimetry and single-crystal X-ray diffraction enabled the phase transition temperatures and enthalpies to be determined and the structural changes that occur at the phase transition temperature. The number of phases is dependent on the chain length: for n = 11 and 15, there are three phases, and for n = 13, there are four phases. Regardless of the number of phases, all three compounds have identical lowest-temperature phases (all stable below room temperature), which have inorganic layers that are staggered, alkylammonium chains that are planar and nonplanar, and yellow crystals. The room-temperature phases for the three compounds differ, but all are orange. C11PbI has staggered layers, and C13PbI and C15PbI have identical room-temperature phases with eclipsed layers. C13PbI and C15PbI also show an additional phase between the lowest-temperature and room-temperature phases.

Materials structure\u2013property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes

Properties can be tailored by tuning composition in high-order composition spaces. For spaces with complex phase behavior, modeling the properties as a function of composition and phase distribution remains a formidable challenge. We present materials structure–property factorization (MSPF) as an approach to automate modeling of such data and identify synergistic phase interactions. MSPF is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks (DRNets) to matrix factorization-based modeling of the representative properties of each phase in a dataset. MSPF is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation, which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity relationships. Comparing the measured photoactivity to a learned model for non-interacting phases, synergistic phase interactions are identified to guide further photoactivity optimization and understanding. MSPF identifies synergistic interactions of a BiVO4-like phase with both Cu2V2O7-like and CuV2O6-like phases, creating avenues for understanding complex photoelectrocatalysts.