Hydrophobic deep eutectic solvents (DESs) emerge as candidates to extract organic substrates from aqueous solutions. The DES-aqueous liquid-liquid interface plays a vital role in the extraction ability of hydrophobic DES because the non-bulk structure of molecules at the interface could cause thermodynamic and kinetic barriers. One question is how the DES compositions affect the structural features of the DES-aqueous liquid-liquid interface. We investigate the density profile, dipole moment and hydrogen bonds of eight hydrophobic DES-aqueous liquid-liquid interfaces using molecular dynamics simulations. The eight DESs are composed of four organic compounds: decanoic acid, menthol, thymol, and lidocaine. The simulation results show the variations of dipole moment and hydrogen bond structure and dynamics at the liquid-liquid interfaces. Such variations could influence the extraction ability of DES through adjusting the partition and kinetics of organic substrates in the DES-aqueous biphasic systems.
Zeolites with encapsulated transition metal species are extensively applied in the chemical industry as heterogenous catalysts for favorable kinetic pathways. To elucidate the energetic insights into formation of subnano-sized molybdenum trioxide (MoO3) encapsulated/confined in zeolite Y (FAU) from constituent oxides, we performed a systematic experimental thermodynamic study using high temperature oxide melt solution calorimetry as the major tool. Specifically, the formation enthalpy of each MoO3/FAU is less endothermic than corresponding zeolite Y, suggesting enhanced thermodynamic stability. As Si/Al ratio increases, the enthalpies of formation of MoO3/FAU with identical loading (5 Mo-wt%) tend to be less endothermic, ranging from 61.1 ± 1.8 (Si/Al = 2.9) to 32.8 ± 1.4 kJ/mol TO2 (Si/Al = 45.6). Coupled with spectroscopic, structural and morphological characterizations, we revealed intricate energetics of MoO3 – zeolite Y guest – host interactions likely determined by the subtle redox and/or phase evolutions of encapsulated MoO3.
Solution crystallization is an important separation unit operation in active pharmaceutical ingredient (API) production. Solvent is one of the important factors affecting crystal morphology. How to select/design suitable crystallization solvents is still one of the most urgent problems in the crystallization field. In this paper, a framework for crystallization solvent design based on the developed quantitative control model of crystal morphology is proposed. First, molecular dynamics is used to predict the crystal morphology in solvents. Next, nine solvent descriptors are selected. Then, the quantitative relationship between crystal aspect ratio and solvent descriptors is developed. Subsequently, Computer-Aided Molecular Design (CAMD) method is integrated with the developed quantitative control model. The crystallization solvent design problem is expressed as a Mixed-Integer Non-Linear Programming (MINLP) model, which is solved by the decomposition algorithm. Finally, the crystallization solvent design framework is applied to two cases: benzoic acid and ibuprofen, and experimental verification is implemented.
Quantitative structure-property relationship (QSPR) studies based on deep neural networks (DNN) are receiving increasing attention due to their excellent performances. A systematic methodology coupling multiple machine learning technologies is proposed to solve vital problems including applicability domain and prediction uncertainty in DNN-based QSPRs. Key features are rapidly extracted from plentiful but chaotic descriptors by principal component analysis (PCA) and kernel PCA. Then, a detailed applicability domain (AD) is defined by K-means algorithm to avoid unreliable predictions and discover its potential impact on uncertainty. Moreover, prediction uncertainty is analyzed with dropout-embedded DNN by thousands of independent tests to assess the reliability of predictions. The prediction of flashpoint temperature is employed as a case study demonstrating that the model accuracy is remarkably improved comparing with the referenced model. More importantly, the proposed methodology breaks through difficulties in analyzing the uncertainty of DNN-based QSPRs and presents an AD correlated with the uncertainty.
The clustering is critical to understanding the multiscale behavior of fluidization. However, its time-resolved evolution on the particle level is seldom touched. Here, we explore both the time-averaged and time-resolved dynamics of clusters in a quasi-2D fluidized bed. Particle tracking velocimetry is adopted and then clusters are identified by using the Voronoi analysis. The time-averaged results show that the cluster hydrodynamic parameters depend highly on the cluster size and the distance from the wall. The number distribution of the cluster size follows a power law (~nc-2.2)) of the percolation theory except for large clusters (nc>100). The time-resolved analysis shows that the cluster coalescence can be simplified as a collision between two inelastic clusters, during which the net external force is roughly zero, and a snowplow model is proposed to predict its energy loss, ΔE ~ t3/2. The cluster rupture is suggested to be caused by increasing torque.
This study considers the development of suitable models for the estimation of Life Cycle Assessment (LCA) indices of organic chemicals based on their molecular structure. The models developed here follow the well-established Group-Contribution (GC) approach and a variety of regression and non-regression methodologies are recruited to achieve the optimum correlation. These models can then be used, alongside other GC models, to screen for molecules with optimal and/or desirable properties, using appropriate molecular design synthesis algorithms. The LCA indices considered here are the Global Warming Potential (GWP), Cumulative Energy Demand (CED) and EcoIndicator 99 (EI99). The model development uses data from existing LCA databases, where each material is associated with its cradle-to-gate LCA metrics, GWP, CED and EI99. The paper presents the model development results, and applies the proposed LCA models on a typical case study for the design of LL-extraction solvents to separate an n-butanol – water mixture.
The breakdown of the ventricular zone (VZ) with the presence of blood in cerebrospinal fluid (CSF) has been shown to increase shunt catheter obstruction in the treatment of hydrocephalus, but the mechanisms by which this occurs are generally unknown. Using a custom-built incubation chamber, we immunofluorescently assayed cell attachment and morphology on shunt catheters with and without blood after 14 days. Samples exposed to blood showed significantly increased cell attachment (average total cell count 392.0±317.1 versus control of 94.7±44.5, P<0.0001). Analysis of the glial fibrillary acidic protein (GFAP) expression showed similar trends (854.4±450.7 versus control of 174.3±116.5, P<0.0001). An in vitro model was developed to represent the exposure of astrocytes to blood following an increase in BBB permeability. Exposure of astrocytes to blood increases the number of cells and their spread on the shunt.
Accurate chemical kinetics are essential for reactor design and operation. However, despite recent advances in “big data” approaches, availability of kinetic data is often limited in industrial practice. Herein, we present a comparative proof-of-concept study for kinetic parameter estimation from limited data. Cross-validation (CV) is implemented to nonlinear least-squares (LS) fitting and evaluated against Markov chain Monte Carlo (MCMC) and genetic algorithm (GA) routines using synthetic data generated from a simple model reaction. As expected, conventional LS is fastest but least accurate in predicting true kinetics. MCMC and GA are effective for larger data sets but tend to overfit to noise for limited data. Cross-validation least-square (LS-CV) strongly outperforms these methods at much reduced computational cost, especially for significant noise. Our findings suggest that implementation of cross-validation with conventional regression provides an efficient approach to kinetic parameter estimation with high accuracy, robustness against noise, and only minimal increase in complexity.
The coexistence of granular liquid-like phase (cluster) and gas-like phase (void) in fluidization, a spontaneous symmetry-breaking dissipative state, contributes to excellent mixing behavior in multi-phase reactors. In present study, a universal granular state equation to describe phase coexistence far from critical point is developed, where both the inelastic solid-collision and asymmetrical instability is taken into consideration. Catastrophe theory is applied to find the stable boundary of phase coexistence, and verified by cold-flow experiment with different solid pressure. A phase diagram, based on both theoretical analysis and experimental study, is given as a useful guideline of design and operation of efficient multi-phase reactors.
The nitration of chlorobenzene with concentrated mixed acids is a fast and highly exothermic process, which suffers from considerable mass transfer resistance and poor heat transfer rates. The reaction kinetics has not been thoroughly reported before. In this work, a continuous-flow microreactor system and a homogeneous reaction condition were proposed to obtain accurate chlorobenzene nitration kinetics data at high mixed acid concentrations. A general model for predicting the observed reaction rate constants was established. With a new method for estimating the equilibria associated with HNO3 in aqueous sulfuric acid, the rate constants based on nitronium ion and activation energies were obtained. Compared with batch reactors, the continuous-flow microreactor system allows for a sufficient heat transfer efficiency and accurate residence time control, making it possible to study the reaction performance more quickly and sensitively. This work may provide a reliable reference for the kinetic study of similar processes.
Optimal tip sonication settings, namely tip position, input power, and pulse durations, are necessary for temperature sensitive procedures like preparation of viable cell extract. In this paper, the optimum tip immersion depth (20-30% height below the liquid surface) is estimated which ensures maximum mixing thereby enhancing thermal dissipation of local cavitation hotspots. A finite element (FE) heat transfer model is presented, validated experimentally with (R2 > 97%) and used to observe the effect of temperature rise on cell extract performance of E. coli BL21 DE3 star strain and estimate the temperature threshold. Relative yields in the top 10% are observed for solution temperatures maintained below 32°C; this reduces below 50% relative yield at temperatures above 47°C. A generalized workflow for direct simulation using the COMSOL code as well as master plots for estimation of sonication parameters (power input and pulse settings) is also presented.
Clarity as to the role of metal identity and oxidation state in effecting redox and acid-catalyzed turnovers is oftentimes precluded by a high degree of heterogeneity in site speciation, a limitation that can be overcome through the use of well-defined poly-metal clusters hosted by metal organic framework materials- accomplished in the present case using MIL-100(M) for the low temperature oxidation of methane with N2O. Transient kinetic data point to a) methoxy species mediating methane conversion, b) partial and deep oxidation occurring over metal sites distinct in oxidation state, c) chromium clusters amplifying the propensity toward C-C bond formation, and d) the relative velocity of propagation of water and methanol concentration fronts playing a determinative role in maximizing C2 oxygenate selectivity. The study captures the utility of using classes of materials inherently endowed with a high level of definition and uniformity in advancing the elucidation of structure-catalytic property relationships.
A series of pyrazine-interior-embodied MOF-74 composites (py-MOF-74) were successfully synthesized by a post vapor modification method, concomitant with the loading ratio of pyrazine easily controlled in this process. Here, pyrazine molecules perform as a cavity-occupant to block the wide pores of MOF-74, which accentuates the adsorption discrepancy of a pair of gases on MOFs and consequently reinforces the adsorption selectivity (typically for CO2/N2, CO2/CH4). Different from the “physical confinement” of occupants, pyrazine molecule with dual “para-nitrogen” atoms donates one N atom to bond with the open metal ion of MOF-74 for stability, and remains the other N atom available for potential CO2 trapping site. Pyrazine-interior-embodied MOF-74 composites manifest significantly improved CO2/N2 and CO2/CH4 adsorption selectivity. Typically, py-MOF-74c with ultimate pyrazine insertion displays selectivity greatly superior to MOF-74 in the equimolar CO2/CH4 (598 vs. 35) and the simulated CO2/N2 flue gas (471 vs. 49) at 100 kPa and 298 K.
The effects of processing intensity, time and particle surface energy on mixing of binary cohesive blends (size ratio 1:2, fine concentration at 10 %) in high intensity vibration system were investigated via DEM simulations. Results show that both increasing processing intensity from 50 to 100 Gs and reducing surface energy from 50 to 0.5 J/m2 lead to a faster mixing rate. Mixing Bond number (〖Bo〗_m) was introduced to capture the effective mixing rate, Rm; higher 〖Bo〗_m corresponding to lower mixing rate. The coefficient of variation, Cv, formed the basis for the mixing quality and Rm, while the mixing action is quantified by the product of Rm and mixing time (Pr,t). Simulation results show that Cv values drop initially, and then rise with Pr,t. Hence, low Pr,t indicates inadequate mixing intensity, while high Pr,t most likely indicates mixture segregation, and therefore too high or too low Pr,t values should be avoided.
To account for the effect of liquid viscosity, the bubble breakup model considering turbulent eddy collision based on the inertial subrange turbulent spectrum was extended to the entire turbulent spectrum that included the energy-containing, inertial, and energy-dissipation subranges. The computational fluid dynamics-population balance model (CFD-PBM) coupled model was modified to include this extended bubble breakup model for simulations of a bubble column. The effect of turbulent energy spectrum on the bubble breakup and hydrodynamic behaviors was studied in a bubble column under different liquid viscosities. The results showed that when the liquid viscosity was < 80 mPas, the bubble breakup and hydrodynamics were almost independent on the turbulent spectrum. At liquid viscosity > 80 mPas, the bubble breakup rate and gas holdup were significantly under-predicted when the inertial turbulent spectrum was used, and when using the entire turbulent spectrum the predictions were more consistent with experimental data.
Additive manufacturing is increasingly being used to develop innovative packings for absorption and desorption columns. Since distillation has not been in focus so far, this paper aims to fill this gap. The objective is to obtain a miniaturized 3D printed packed column with optimized properties in terms of scalability and reproducibility, which increases process development efficiency. For this purpose, a flexible laboratory scale test rig is presented combining standard laboratory equipment with 3D printed components such as innovative multifunctional trays or the column wall with packing. The test rig offers a particularly wide operating range (F=0.15 Pa0.5…1.0 Pa0.5) for column diameters between 20 mm and 50 mm. First results regarding the time to reach steady-state, operational stability and separation efficiency measurements are presented using a 3D printable version of the Rombopak 9M. Currently, innovative packings are being characterized, which should exhibit a optimized bevavior regarding scalability, reproducibility and separation efficiency.
Bubble breakup plays an important role in gas-liquid flows, but detailed studies are still scarce. In this work, the breakup behavior of a single bubble in a stirred tank was experimentally studied with a high-speed camera, focusing on the effect of gas density, liquid properties, agitation speed and mother bubble size. The bubble breakup time, breakup probability, breakup rate and daughter bubble size distribution were determined. The internal flow phenomenon inside a deformed bubble was studied in detail, which accounted for the effect of gas density or operating pressure. The results showed that with increasing gas density, agitation speed, mother bubble size and decreasing surface tension, the bubble breakup rate and probability of equal-size distribution significantly increased. With increasing liquid viscosity, the bubble breakup rate decreased especially in the high viscosity range. An M-shaped daughter bubble size distribution was observed, which was consistent with our previous bubble breakup model.
Separation of mixed ion, especially Cl- and SO42-, is essential for reduced energy consumption and achievement of the minimal or zero-liquid discharge. Membrane technology has attracted significant attention in this respect owing to its good system coupling and maturity. However, it remains challenging to fabricate highly selective nanofilm with fine-tuning pore and structure that is suitable for the separation of Cl- and SO42-. Herein, we report an asymmetric alicyclic polyamide nanofilm with enhanced interconnectivity pore by manipulating the molecular geometry structure, composed of the porous aromatic polyamide dendrimer layer, and the dense alicyclic polyamide layer with hollow stripes. This resulted membrane shows a 107.14% separation rate of Cl- and SO42-, and a water flux (for Na2SO4) of ~2.2 times that of the pristine polyamide membrane. We estimate this fine-tuning pore approach resulting from alicyclic structure also might be employed in other separation membranes such as gas, solvent or neutral molecules.