The droplet generation mechanism in the step T-junction remains unknown, especially for the transition stage from dripping to jetting . In this work, the droplet generation mechanism was systematically investigated in a novel modified step T-junction. We found that under different fluid regimes, different factors take action. In dripping regime, the interfacial tension dominated the formation mechanism when the surfactant concentration was controlled below micelle concentration (CMC). In jetting regime, our experimental results showed that the influence of the surfactant concentration on the size of generated droplets was rather negligible while the phase ratio indeed determined such a parameter. In the dripping-jetting transition stage, an abnormal increase of droplet size was observed despite the increase of continuous phase flow. To the best for our knowledge, it is the first study to report generation mechanism in modified step T-junction from dripping to jetting regimes.
This article focuses on the catalytic hydrodenitrogenation (HDN) mechanism of indole under hydrothermal conditions. Both gaseous hydrogen and liquid hydrogen donor formic acid (FA) can improve indole conversion and total yield of denitrogenated products. Ru/C showed the highest activity among the catalysts for indole conversion in all temperature conditions with the existence of H2 and 91.17 % indole was converted at 400 °C and 60 min. Based on reaction kinetic experiments, a kinetic model was developed to describe the hydrothermal HDN reaction of indole over the home-made Ni80Ru20/γ-Al2O3 catalyst, which clearly captured all data trends and fitted the temporal variation of all major liquid products. High activation energy for formation of O-containing substance o-cresol from both mathematical fitting and density functional theory (DFT) calculation indicated a rare occurrence of reaction between pyrrole ring-opening product methyl aniline and H2O, consistent with experimental observation that only a trace of o-cresol was detected.
This study presents the chromatographic purification of immunoglobulin G (IgG) from human plasma using a two-column process integrating the peptide-based adsorbents LigaGuardTM, which captures non-Ig plasma proteins in flow-through mode, and LigaTrapTM, which isolates IgG in bind-and-elute. Buffer composition and column loading were optimized for both adsorbents. Two process configurations were evaluated. In the first design, plasma was fed to a LigaGuardTM column to capture plasma proteins, the effluent was loaded on the LigaTrapTM column, and the bound IgG was eluted with 63.8% global recovery and 99.7% purity; in comparison, Protein G agarose afforded ~67% recovery and 97.2% purity. In the alternative design, the LigaGuardTM column was utilized to polish the LigaTrapTM elution stream, affording 82.3% global recovery and 98.8% purity. Collectively, these results demonstrate the potential of a fully chromatographic process for purifying polyclonal IgG from plasma feedstocks.
Nanomaterial drug delivery systems have become one of the most important targeted therapy technologies. Although great efforts have been made to study the self-assembled mesoscopic structure of nanoparticles and understand drug loading and release mechanisms, the interaction between nanoparticles and cell membranes has not yet been clearly studied. Moreover, the research of experimental methods in this field has been greatly restricted due to its special time-space scale, so it is necessary to apply computer simulations to visualize the cell internalization of the nanoparticle. This review covers modelling methods and the current status and viewpoints of research on the influencing factors of the nanoparticle-biomembrane interaction mechanism. In particular, we discussed in detail the positive and negative effects of various nanoparticle properties. This article may assist researchers in rationally optimize the nanoparticle structure to improve therapeutic efficiency.
The advanced use of a pH-responsive biomaterial-based injectable liquid implant for effective chemotherapeutic delivery in glioblastoma multiforme brain (GBM) tumour treatment is presented. As an implant, we proposed a water-in-oil-in-water multiple emulsion with encapsulated doxorubicin. The effectiveness of the proposed therapy was evaluated by comparing the cancer cell viability achieved in classical therapy (chemotherapeutic solution). The experimental study included doxorubicin release rates and consumption for two emulsions differing in drop sizes and structures in the presence of GBM-cells (LN229, U87 MG), and a cell viability. The results showed that the multiple emulsion implant was significantly more effective than classical therapy when considering the reduction in cancer cell viability: 85% for the emulsion-implant, and only 43% for the classical therapy. A diffusion-reaction model was adapted to predict doxorubicin release kinetics and elimination by glioblastoma cells. CFD simulations confirmed that the drug release kinetics depends on multiple emulsion structures and drop sizes.
Efficient and economical separation of 1,3-butadiene (C4H6) from C4 hydrocarbons is imperative yet challenging in industrial separation processes. Herein, a guest-induced flexible Mn-bpdc MOF has been employed to separate C4H6 from C4 hydrocarbons, including n-butene (n-C4H8), iso-butene (iso-C4H8), n-butane (n-C4H10) and iso-butane (iso-C4H10). Significantly, C4H6 can instantaneously induce gate-opening of Mn-bpdc MOF at 0.13 bar and 298 K, thus significant amounts of C4H6 can be adsorbed, while other C4 hydrocarbons cannot induce the gate-opening even at 1 bar. The uptake selectivities of Mn-bpdc MOF for C4H6/n-C4H8 and C4H6/iso-C4H8 are up to 40.0 and 45.0 at 298 K and 1 bar, respectively, both surpassing all the reported adsorbents. In addition, breakthrough experiments verified that C4H6/n-C4H8, C4H6/iso-C4H8, C4H6/n-C4H10 and C4H6/iso-C4H10 mixture can be efficiently separated. More importantly, Mn-bpdc possesses excellent water stability and outstanding regeneration ability for C4H6 separation, making it a new benchmark for C4H6 purification.
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.
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.