Holistic catalytic reactors with fractal structure have attracted growing attention in heterogeneous reactions because of its advantages of improved mass transfer and easy separation. Herein, a simple and cost-efficient design strategy that combined 3D printing and electroless deposition was presented to construct dynamic, holistic stirred reactors with the fractal structure. The conversion factor α of fractal impeller is 65.01 mmol m-2 h-1 with a large volume of reactant (80 mL), which is 1.7 times that of the normal impeller with the same Ag catalysts. Experimental results and simulation analysis demonstrate that the fractal impeller significantly improves the catalytic performance by enhancing mass transfer and spatial dispersion in the reaction. Moreover, the holistic impeller could be reused for ten times without obvious loss of catalytic performance, and easily separated from the reaction system. The structural design of fractal reactors will open the way for a new efficient dynamic heterogeneous catalytic reactor.

Carbon quantum dots (C-QDs) show great potential to replace traditional semiconductive quantum dots as the next generation of fluorescent probes. We demonstrate here a new C-QD production process using lignin, a high-volume but low market-value industrial waste and/or environmental hazards, as the starting carbon source. By adding a small amount of inorganic acid, the rich phenolic components in lignin were successfully converted to C-QDs through a coking formation mechanism similar to what happens on solid acid catalysts in traditional fossil fuel cracking process. The aqueous solution presence of the received lignin C-QDs is beneficial for brain cell imaging applications, attributing to their fast internalization, low toxicity, tunable photoluminescence by appropriate acidity and reaction temperature during hydrothermal synthesis. This method not only provides a low-cost C-QDs production route, but also helps gain extra profit and/or improve environment for many small agricultural business and paper and pulp industry located in rural area.

Acetylene, an important petrochemical feedstock, is the starting chemical to produce many polymer products. Separating C2H2 from its by-product mixtures is still an energy-consuming process and remains challenging. Here, we present a metal-organic framework[Zn2(bpy)(btec)], with a desirable pore geometry and highly stable framework, which demonstrated a high separation performance of C2H2 from simulated mixtures. With the desirable pore dimension and hydrogen bonding sites, Zn2(bpy)(btec) shows by far the both highest C2H2/CO2 and C2H2/CO2 uptake ratios, very high adsorption selectivities and moderately C2H2 uptake of 93.5 cm3•cm−3 under 298 K and 1 atm. Not only straightforwardly produced high purity of C2H4, but also recovered high purity of C2H2 (>98%) in the regeneration process (>92% recovery). More notably, Zn2(bpy)(btec) can be straightforwardly synthesized at a large scale under environmentally friendly conditions, and its good water/chemical stability, thermostability, and cyclic stability highlight the promise of this molecular sieving material for industrial C2H2 separation.

Machine learning (ML) models are valuable research tools for making accurate predictions. However, ML models often unreliably extrapolate outside their training data. We propose an uncertainty quantification method for ML models (and generally for other nonlinear models) with parameters trained by least squares regression. The uncertainty measure is based on the multiparameter delta method from statistics, which gives the standard error of the prediction. The uncertainty measure requires the gradient of the model prediction and the Hessian of the loss function, both with respect to model parameters. Both the gradient and Hessian can be readily obtained from most ML software frameworks by automatic differentiation. We show that the uncertainty measure is larger for input space regions that are not part of the training data. Therefore this method can be used to identify extrapolation and to aid in selecting training data or assessing model reliability.

Research problems in the domains of physical, engineering, biological sciences, often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high-performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.

Molecular simulation has emerged as an important sub-field of chemical engineering, due in no small part to the leadership of Keith Gubbins. A characteristic of the chemical engineering molecular simulation community is the commitment to freely share simulation codes and other key software components required to perform a molecular simulation under open-source licenses and distribution on public repositories such as GitHub. Here we provide an overview of open-source molecular modeling software in Chemical Engineering, with focus on the Molecular Simulation Design Framework (MoSDeF). MoSDeF is an open-source Python software stack that enables facile use of multiple open-source molecular simulation engines, while at the same time ensuring maximum reproducibility.

The degree of rate control quantitatively identifies the kinetically relevant (sometimes known as rate-limiting) steps of a complex reaction network. This concept relies on derivatives which are commonly implemented numerically, e.g. with finite differences. Numerical derivatives are tedious to implement, and can be problematic, and unstable or unreliable. In this work, we demonstrate the use of automatic differentiation in the evaluation of the degree of rate control. Automatic differentiation libraries are increasingly available through modern machine learning frameworks. Compared to the finite differences, automatic differentiation provides solutions with higher accuracy with lower computational cost. Furthermore, we illustrate a hybrid local-global sensitivity analysis method, the distributed evaluation of local sensitivity analysis (DELSA), to assess the importance of kinetic parameters over an uncertain space. This method also benefits from automatic differentiation to obtain high-quality results efficiently.

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.

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.

Decoupling and understanding the various mass, charge and heat transport phenomena involved in the electrocatalytic transformation of small molecules (i.e. CO2, CO, H2, N2, NH3, O2, CH4) is challenging but it can be readily achieved using dimensionless quantities (i.e. Reynolds, Sherwood, Schmidt, Damköhler, Nusselt, Prandtl, and Peclet Numbers) to simplify the characterization of systems with multiple interacting physical phenomena. Herein we report the development of a gastight rotating cylinder electrode cell with well-defined mass transport characteristics that can be applied to experimentally decouple mass transfer effects from intrinsic kinetics in electrocatalytic systems. The gastight rotating cylinder electrode cell enables the dimensionless analysis of electrocatalytic systems and should enable the rigorous research and development of electrocatalytic technologies.

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.

Synthesis of adipic acid (AA) through the oxidation of cyclohexanol and cyclohexanone (K/A oil) with nitric acid was conducted in a capillary microreactor system. Effects of the temperature, the nitric acid concentration, the volumetric flow rate ratio of nitric acid to K/A oil, and the capillary length on the selectivity and the product yield were investigated systematically to achieve optimal reaction conditions. Notably, a high yield of adipic acid (i.e., 90%) was achieved just in 6 seconds at 85℃ with the use of 55 wt% nitric acid. Gas components produced in this oxidation process and its total volumetric flow rate were determined under various operating conditions, which was beneficial for reaction mechanism characterization and process optimization. Finally, a kinetic model was established, which was of crucial theoretical significance and practical value for optimizing the reactor design and better understanding such fast and highly exothermic multiphase processes with abundant gas production.

Adsorption of CO2 from post-combustion flue gas is one of the leading candidates for globally-impactful carbon capture systems. This work highlights opportunities and limitations of sub-ambient CO2 capture processes utilizing a multi-stage separation process. A hybrid process design using a combination of pressure-driven separation of CO2 from flue gas followed by CO2-rich product liquefaction to produce high purity (>99%) CO2 at pipeline conditions is considered. The economic viability of applying pressure swing adsorption (PSA) processes using fiber sorbent contactors with internal heat management were found to be most influenced by the productivity of the adsorption system. Three exemplar fiber sorbents (MIL-101(Cr), UiO-66, and zeolite 13X) were considered for application in the sub-ambient process of PSA unit. MIL-101(Cr) and UiO-66 fiber composites were estimated to have costs of capture as low as $61/tonne CO2.

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 semi-resolved CFD-DEM method has emerged as a prominent tool for modelling particle-fluid interactions in granular materials with high particle size ratios. However, challenges arise from conflicting requirements regarding the CFD grid size, which must adequately resolve fluid flow in the pore space while maintaining a physically meaningful porosity field. This study addresses these challenges by introducing a two-grid mapping approach. Initially, the porosity field associated with fine particles is estimated using a coarse CFD grid, which is then mapped to a dynamically refined grid. To ensure conservation of total solid volume, a volume compensation procedure is implemented. The proposed method has been rigorously validated using benchmark cases, showing its high computational efficiency and accurate handling of complex porosity calculations near the surface of coarse particles. Moreover, the previously unreported impact of the empirical drag correlation on fluid-particle force calculations for both coarse and fine particles has been revealed.

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.

The flow characteristics of the blade unit of a tridimensional rotational flow sieve tray was investigated experimentally in this study. First, the flow patterns are defined under different liquid arrangement methods. They are bilateral film flow, continuous perforated flow, and dispersion-mixing flow in overflow distribution and film and jet flow and jet and mixed flow in spray distribution. Second, the time and frequency domain analysis of the differential pressure pulsation signal in the blade unit is carried out. The influence of perforation and mixing intensity on the flow pattern transition is clarified. Third, the rotational flow ratio of the gas-liquid phase is measured. The influence of the operating conditions on the distribution of the rotational and perforated flow for the gas-liquid phase is investigated. Finally, a prediction model for the rotational flow ratio is proposed. The prediction results agree well with the experimental data.

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.