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.

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.

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.

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.

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 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.

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.

To validate the experimental results of Part-1, we conducted a two-phase flow simulation of imbibition of a wetting liquid through 2D microstructures made of ellipses of varying aspect ratios. The flow simulation in the particulate microstructures, characterized by low (ellipse) aspect ratio, produced somewhat even micro-fronts, thus replicating the sharp fronts at the visual (macroscopic) scale observed in Part-1. Whereas simulations in the fibrous microstructures produced highly uneven micro-fronts, suggesting the formation of semi-sharp or diffuse visual fronts. Increasing the porosity from 50% to 70% resulted in solid-phase clustering and led to further increase in the unevenness of micro-fronts, pointing to purely diffuse visual fronts. The evolution of the saturation plots along the flow direction, obtained from area-averaging of fluid-distribution plots, pointed to diffusing of sharp fronts with time. The predictions matched our previous experimental observations, i.e., the particulate media create sharp fronts while the fibrous media create semi-sharp/diffuse fronts.

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 existing methods of flexibility index are mainly based on mixed-integer linear or nonlinear programming methods, making it difficult to readily deal with complex mathematical models. In this article, a novel solution strategy is proposed for finding a reliable upper bound of the flexibility index where the process model is implemented in a black box that can be directly executed by a commercial simulator, and also avoiding the need for calculating derivatives. Then, the flexibility index problem is formulated as a sequence of univariate derivative-free optimization (DFO) models. An external DFO solver based on trust-region methods can be called to solve this model. Finally, after calculating the critical point of the model parameters, the vertex enumeration method and two gradient approximation methods are proposed to evaluate the impact of process parameters and to evaluate the flexibility index. A reaction model is studied to show the efficiency of the proposed algorithm.

Multiple functional and hard-to-quantify sensorial product attributes that can be satisfied by a large number of cosmetic ingredients are required in the design of cosmetics. To overcome this challenge, a new optimization-based approach for expediting cosmetic formulation is presented. It exploits the use of a hierarchy of models in an iterative manner to refine the search for creating the highest-quality cosmetic product. First, a systematic procedure is proposed for optimization problem formulation, where the cosmetic formulation problem is defined, design variables are specified, and a set of models for sensorial perception and desired product properties are identified. Then, a solution strategy that involves iterative model adoption and two numerical techniques (i.e., generalized disjunctive programming reformulation and model substitution) is applied to improve the efficiency of solving the optimization problem and to find better solutions. The applicability of the proposed procedure and solution strategy is illustrated with a perfume formulation example.