In this study, different calorimetric and analytical techniques were used to evaluate the thermal behaviour of the preparation of 1-nitronaphthalene through the nitration of naphthalene with mixed acid. Differential scanning calorimetry and adiabatic calorimetry revealed that 1-nitronaphthalene lacks thermal decomposition characteristics. The results of reaction calorimetry, high-performance liquid chromatography, and the density function theory method were combined to analyse the reaction mechanism. An expanded Stoessel criticality diagram was developed to evaluate the process safety of the semibatch nitration reaction. The results revealed a higher degree of risk than that obtained when using the traditional Stoessel criticality diagram. Kinetics parameters of the reaction were investigated.

We develop a simulation toolset employing density functional theory (DFT) in conjunction with grand canonical Monte Carlo (GCMC) to study coke formation on Fe-based catalysts during propane dehydrogenation (PDH). As expected, pure Fe surfaces develop stable graphitic coke structures and rapidly deactivate. We find that coke formation is markedly less favorable on Fe3C and FeS surfaces. Fe-Al alloys display varying degrees of coke resistance, depending on their composition, suggesting that they can be optimized for coke resistance under PDH conditions. Electronic structure analyses show that both electron-withdrawing effects (on Fe3C and FeS) and electron-donating effects (on Fe-Al alloys) destabilize adsorbed carbon. On the alloy surfaces, a geometric effect also isolates Fe sites and disrupts the formation of graphitic carbon networks. This work demonstrates the utility of GCMC for studying the formation of disordered phases on catalyst surfaces and provides insights for improving the coke resistance of Fe-based PDH catalysts.

The Bayer process holds an exclusive status for alumina extraction, but a massive amount of caustic “red mud” waste is generated. In this work, three oxalate reagents: potassium hydrogen oxalate (KHC2O4), potassium tetraoxalate (KHC2O4·H2C2O4), and oxalic acid (H2C2O4) were investigated for the Al and Fe extraction process from NIST SRM 600 – Australian Darling range bauxite ore. More than 90% of Al and Fe was extracted into the aqueous phase in less than 2 h with 0.50 M C2O42- for all three reagents. The Fe and Al can be selectively precipitated by hydrolyzing the aqueous phase. By acidifying the Al and Fe free filtrate, 80% of the C2O42- can be precipitated as KHC2O4·H2C2O4. Greater than 90% of the aqueous acid can also be recycled using a cation exchange resin. The proposed closed-loop process is an energy-efficient, cost-effective, environmentally-friendly route for extracting Al and Fe from bauxite ore.

Extrusion-based 3D printing of polymeric biomaterials has emerged as a promising approach for the fabrication of complex tissue engineering constructs. However, the large pore and feature size lead to low cell seeding efficiency and limited control of spatial distribution of cells within the scaffolds. We developed hybrid scaffolds that are composed of 3D printed layers and airbrushed fibrous membranes. Airbrushing time was adjusted to fabricate low (L), medium (M), and high (H) density membranes to effectively control stem cell infiltration. When two distinct populations of stem cells were seeded from top or bottom of the scaffolds, scaffolds composed of LLL membranes showed gradual mixing of the cells with depth whereas LHL membranes led to two distinct regions of cells separated by the H membrane. Our results demonstrate that fibrous membranes incorporated within 3D printed layers enable user-defined and spatially controlled cell compositions within hybrid scaffolds.

Otitis Media (OM) is the most common reason for U.S. children to receive prescribed oral antibiotics, leading to potential to cause antibiotic resistance. To minimize oral antibiotic usage, we developed polyvinylpyrrolidone-coated silver nanoparticles (AgNPs-PVP), which completely eradicated common OM pathogens, i.e., Streptococcus pneumoniae and non-typeable Haemophilus influenzae (NTHi) at 1.04µg/mL and 2.13µg/mL. The greater antimicrobial efficacy against S. pneumoniae was a result of the H2O2-producing ability of S. pneumoniae and the known synergistic interactions between H2O2 and AgNPs. To enable the sustained local delivery of AgNPs-PVP (e.g., via injection through perforated tympanic membranes), a hydrogel formulation of 18%(w/v)P407 was developed. Reverse thermal gelation of the AgNPs-PVP-P407 hydrogel could gel rapidly upon entering the warm auditory bullae and thereby sustained release of antimicrobials. This hydrogel-based local delivery system completely eradicated OM pathogens in vitro without cytotoxicity, and thus represents a promising strategy for treating bacterial OM without relying on conventional antibiotics.

A document by Asterios Pantokratoras. Click on the document to view its contents.

Emulsion electrospinning represents a tunable system for the fabrication of porous scaffolds for controlled, localized drug delivery in tissue engineering applications. This study aimed to elucidate the role of model drug interactions with emulsion chemistry on loading and release rates from fibers with controlled fiber diameter and fiber volume fraction. Nile Red and Rhodamine B were used as model drugs and encapsulation efficiency and release rates were determined from poly(caprolactone) (PCL) electrospun fibers spun either with no surfactant (Span 80), surfactant, or water-in-oil emulsions. Drug loading efficiency and release rates were modulated by both surfactant and aqueous internal phase in the emulsions as a function of drug molecule hydrophobicity. Overall, these results demonstrate the role of intermolecular interactions and drug phase solubility on the release from emulsion electrospun fibers and highlight the need to independently control these parameters when designing fibers for use as tunable drug delivery systems.

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.