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.
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.
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.
In this work, the effective ultra-deep catalytic adsorptive desulfurization (CADS) using Ti-silica gel adsorbent at low Ti loading range (< 1%) was investigated. The superior CADS capacity (37.3 mg-S/g-A) and high TOF value (432 h-1) for dibenzothiophene (DBT) were achieved at 0.6% of Ti loading with high dispersion and low Ti coordination. The catalytic oxidation of DBT conformed to the pseudo-first-order kinetic model, and the corresponding rate equation was well described as , where the TiOOR is determined as the intermediate to enable the DBT oxidation to the corresponding sulfone (DBTO2). The effectiveness of CADS using Ti-SG was verified in various real low-sulfur diesels with varied sulfur concentrations and O/S ratios in the dynamic fixed-bed adsorption and multi-cycle regenerations. This work provides insights on using low-cost bifunctional catalytic adsorbents at low Ti loading for effective CADS to realize ultra-deep desulfurization of transportation fuels.
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.
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.
Sequential model-based design of experiments (MBDOE) is used to select operating conditions for new experiments in a batch-reactor that produces bio-based poly(trimethylene) ether glycol (PO3G). These Bayesian A-optimal experiments are designed to obtain improved estimates of the 70 fundamental-model parameter estimates, while accounting for the model structure and for data from eight previous industrial batch-reactor runs. Settings are selected for three decision variables: reactor temperature, initial catalyst level, and initial water concentration. If only one new experiment is conducted, it should be run at high temperature, with relatively high concentrations of catalyst and initial water. When two new runs are conducted, one should use an intermediate catalyst concentration. The effectiveness of the proposed MBDOE approach is tested using Monte-Carlo simulations, revealing that the selected experiments are superior compared to new experiments selected randomly from corners of the permissible design space.
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.
Reactor corrosion and salt deposition problems severely restrict the industrialization of supercritical water oxidation. Transpiring wall reactor can effectively weaken these two problems through a protective water film formed on its internal surface. In this work, the effects of key structural parameters on water film properties of transpiring wall reactor were explored by numerical simulation, and established models were validated by comparing simulation and experimental values. The results show that transpiration water layer, transpiring wall porosity and inner diameter hardly affected organic matter degradation. Increasing transpiration water layer and transpiring wall porosity reduced reactor center temperatures in the middle and lower zones of the reactor. Increasing transpiration water layer, transpiring wall porosity and inner diameter decreased water film temperatures but increased water film coverage rates. Increasing reactor length affected slightly on the volume of the upper supercritical oxidation zone but enlarged the subcritical zone.
We present the development and application of a two-phase stirred reactor model for heavy oil upgrading in the presence of supercritical water (SCW), with coupled phase-specific thermolysis reaction kinetics and multicomponent hydrocarbon water phase equilibrium. We demonstrate the inference of oil and water phase kinetics parameters for a compact lumped reaction kinetics model through the application of this model to two different sets of batch reactor experiments reported in the literature. We infer that, though SCW can suppress the formation of newer polynuclear aromatics (PNA) from distillate range species, it is broadly ineffective in deterring the combination of pre-existing PNA fragments in the oil feed. Quantification of the conversion to distillate liquids before the onset of coke formation helps arrive at a clearer conclusion on whether the use of SCW in the batch reactor leads to better product outcomes for different oil feeds and operating conditions.
Reactive transport codes are today one of the cornerstones of environmental research. They now contain multiphysics with very complex algorithms, including flow, transport, chemical and sometimes heat transport, mechanical and/or biological algorithms. Because of this complexity, some parts of these algorithms still have not been sufficiently studied. Here, we present a comparison of 3 algorithms for activity correction, a specific subset of equilibrium chemistry algorithms. We show that the most used algorithm (the inner fixed-point algorithm) or the most rigorous algorithm (the full Newton) might not be the most efficient, and we propose the outer fixed-point algorithm, which is more robust and faster than other algorithms.