Due to its toxicity and corrosiveness, it is of enormous significance to efficiently capture and recover sulfur dioxide (SO2) from flue gas and natural gas. Herein, a new type of IL/MIL-0.7 composite was precisely designed to meet this challenge, which exhibits a high adsorption capacity for SO2 (13.17 mmol·g-1) at 298 K and 1 bar while excludes almost completely carbon dioxide (CO2) (0.27 mmol·g-1) and nitrogen (N2) (0.07 mmol·g-1). The high IAST selectivity (at least 11925) of IL/MIL-0.7 for SO2/CO2 can be achieved within the whole test pressure range. In addition, the breakthrough experiment also confirmed the excellent performance of the composite for deep removal of 2000 ppm SO2. Furthermore, the IL/MIL-0.7 composites can maintain excellent performance after four adsorption and desorption circulations and the thermostability can up to ~450 K. Therefore, this stable IL@MOF composite has the potential application as an effective adsorbent for SO2 removal from flue gas.

Oblique collisions of two spherical particles coated with a thin layer of viscous liquid are considered. Experimental measurements are performed using particle tracking velocimetry. Comprehensive experimental data for collisions with an impact angle between 0° - 60° are presented. Collisions are characterised by the Stokes’ number, the coefficient of restitution, and the rotational velocity. The experiments are compared to numerical simulations using the discrete element method (DEM). The translational velocities predicted by the simulations were in good agreement with the experiments at high Stokes’ number, where the models are dominated by the normal components. As the tangential forces become more significant (i.e. at low to medium Stokes’ number, and high collision angle), agreement between the simulations and experiments is poorer. At low Stokes’ number the translational velocities were in good agreement with the experiments, but was poorer at high Stokes’ number.

The direct Z-scheme provide a potential strategy for high efficient CO2 photoreduction, whereas the heterointerface contact resistance is significantly limited the interfacial electron transfer kinetic. Herein, we build the directional charge-transfer channels in a direct Z-scheme system over metal−organic frameworks (MOFs), that is the lattice-guided MOF-on-MOF hybrids, to facilitate CO2 photoreduction. The heteroepitaxial lattice growth along the c-axis of MIL-88B(Fe) via the high-activity (001) facet over the stable (111) facet of UiO-66-NH2. Theoretical calculations and experimental results provide the direct evidence that engineering direct Z-scheme of these MOFs hybrids can induce the electrons migration from UiO-66-NH2 to the holes of MIL-88B(Fe) by directional charge-transfer channels owing to their lattice match. This can dramatically boosts photocatalytic CO2-to-CO selectivity up to nearly 100%, with a rate of 2.26 μmol·g-1·h-1. This work demonstrates that the efficiently selective CO2 photoreduction processes can be achieved by engineering Z-scheme via lattice intergrown of MOF hybrids strategy.

CO2 methanation is one of the vital reactions to utilize CO2 and realize power to gas process. To decrease the CO2 capture cost and alleviate the hot spots during the strong exothermic methanation reaction, here, we report a coupling of CO2 capture process with in situ CO2 methanation process through a ceramic-molten carbonate (MC) dual phase membrane reactor over the Ni-based catalyst. The performance of the membrane reactor was systematically investigated and compared with the traditional fixed-bed reactor. The results show that the performance of the membrane reactor is higher than that of the fixed-bed reactor, since the produced steam through the methanation process can be partially removed through the dual-phase membrane, which promotes the reaction shift to right side. A stability test shows no obvious degradation within 32 h. These results indicate that the membrane reactor is promising for coupling CO2 capture with in situ methanation process.

Because of very high potential barrier for thermionic emission and trap-assisted charge recombination, photocatalytic reaction rate that determined by semiconductor-cocatalyst interfacial electron transfer severely deviates from linearity to the photocatalyst dosage or to the light intensity. This makes it challenging to maximize utilization of practical irradiation by referring the parameters evaluated from method used in conventional catalysis. We here develop a model and predict that photocatalytic reaction rate positively correlates to photocatalyst concentration under weak illumination while the correlation becomes negative under intense irradiation. The theoretical simulation that matches the experimental values can be used to guide maximizing photocatalytic photon utilization under various intensity of irradiation. The strong correlation can rationalize photocatalytic evaluation instead of obtaining a numerically high value by excessively lowering the denominators. To realize efficient utilization of real-time changing sunlight, we propose a reactor configuration that can optimize the amount of photocatalyst participating into the reaction.

Nitrates from agricultural wastewater are harmful to human health and result in eutrophication. Several emerging electrochemical technologies have been developed independently to enable efficient recovery and recycling of nitrate waste; however, it remains unclear whether the implementation of such combined technologies can be economically viable. Herein, we perform a technoeconomic and global warming potential analysis on several hypothetical nitrate capture and conversion (NCC) systems for the recovery of nitrates from agricultural wastewater and conversion of nitrate to ammonia. The energy efficient technologies incorporated include: electrodialysis for nitrate separation, electrocatalysis for ambient ammonia production, and agrophotovoltaics as a clean energy source. Our technoeconomic analysis reveals that despite advancements in nitrate separation and conversion, capital investments for system installation cannot be recovered by the financial benefit of on-site fertilizer production. Our analysis highlights the necessity of government intervention to promote nitrate abatement technologies to ensure environmental compliance and protect public health.

Herein, we propose a novel method to enhance the photoreactivity of an MOF catalyst by grafting isocyanate bonds (−N=C=O) and sulfhydryl-complexed copper (−SCu) onto ZIF-8 (NIF-SCu). The grafting process intercalated interlayer bands between the conduction and valence bands of ZIF-8, thereby providing a “ladder” for facile electron transition. The extreme improvement in the photoreactivity of NIF-SCu could be attributed to the enhancement in light responses in the range of 350–450 nm by −N=C=O groups and the widening of the visible light range of the MOF by −SCu groups. The formation of staggered energy levels in NIF-SCu could also narrow the band gap, lower the resistance, and facilitate the transfer of photogenerated carriers, thereby generating electrons with strong reduction potential in the −SCu conduction band. This study provides a new strategy for improving or even endowing the photoactivity of environmental functional materials with wide bandgaps.

While decomposition techniques in mathematical programming are usually designed for numerical efficiency, coordination problems within enterprise-wide optimization are often limited by organizational rather than numerical considerations. We propose a ‘data-driven’ coordination framework which manages to recover the same optimum as the equivalent centralized formulation while allowing coordinating agents to retain autonomy, privacy, and flexibility over their own objectives, constraints, and variables. This approach updates the coordinated, or shared, variables based on derivative-free optimization (DFO) using only coordinated variables to agent-level optimal subproblem evaluation ‘data’. We compare the performance of our framework using different DFO solvers (CUATRO, Py-BOBYQA, DIRECT-L, GPyOpt) against conventional distributed optimization (ADMM) on three case studies: collaborative learning, facility location, and multi-objective blending. We show that in low-dimensional and nonconvex subproblems, the exploration-exploitation trade-offs of DFO solvers can be leveraged to converge faster and to a better solution than in distributed optimization

This work develops a model predictive control (MPC) scheme using online learning of recurrent neural network (RNN) models for nonlinear systems switched between multiple operating regions following a prescribed switching schedule. Specifically, an RNN model is initially developed offline to model process dynamics using the historical operational data collected in a small region around a certain steady-state. After the system is switched to another operating region under a Lyapunov-based MPC with suitable constraints to ensure satisfaction of the prescribed switching schedule policy, RNN models are updated using real-time process data to improve closed-loop performance. A generalization error bound is derived for the updated RNN models using the notion of regret, and closed-loop stability results are established for the switched nonlinear system under RNN-based MPC. Finally, a chemical process example with the operation schedule that requires switching between two steady-states is used to demonstrate the effectiveness of the proposed RNN-MPC scheme.

Microbial processes sour oil, corrode equipment, and degrade hydrocarbons at an annual global cost to the oil and gas industry of nearly $2 billion. However, top-down control of these microbial processes can reduce their damage and enhance oil recovery. Here, we screened microbial communities from five oil wells in the Illinois basin and evaluated nutrient injection strategies to control metabolism and community composition. Molasses and molybdate supplementation stimulated significant gas and organic acid production while completely suppressing corrosive H2S formation in samples from two wells. These changes were accompanied with significant shaping of the microbiome community. Simulations of field operations via a lab-scale mini-coreflood validated that oil well microbiomes can be engineered to also shape oil hydrocarbon composition in situ. These pilot studies validate the potential of economical and sustainable top-down microbiome engineering to control microbes in oil extraction and enhance the economic viability of oil recovery.

In this work, stationary states in nonequilibrium plasmas of chemical reactions that can produce hydrogen are explored, namely the water splitting and water gas shift reactions. For both reactions, the effluent from the reactor at long gas residence times in the plasma was found to be independent of the influent speciation. In other words, feeding the reactor either 0.1 H2O or 0.1H2+0.05O2 by mole produced the same effluent composition, and similarly, feeding the reactor 0.1CO+0.1H2O produced nominally the same effluent as 0.1CO2+0.1H2. For both reactions, the effluent from the plasma was found to be very far from local equilibrium at the total pressure and background temperature of the reactor. An important conclusion of this work is that special attention must be paid to the recombination zone in plasma chemical processes. The recombination zone tends to drive the gas composition from plasma stationary states back towards local equilibrium.

An economical and highly uranium extraction from seawater remains a crucial task for energy sources and environmental safety. Aiming for improving the mass transfer rate of uranium from seawater, a new synthetic strategy was adopted to synthesize 2D-open channel microporous bio-adsorbent for uranium extraction from seawater. Herein, a vapor phase modification approach was adopted to graft divinylbenzene(DVB), and polyacrylonitrile(AN) onto the surfaces of microporous frameworks via a free radical polymerization method. The post-synthetic functionalization was carried out by hydrothermal process, where amidoxime groups are structure-directing agents to trap uranium. Further, amidoxime groups not only enhanced hydrophilicity but also adjusts adsorbents pKa. AO-Fc faces minimum interference of competing ions and achieves a high uranium adsorption capacity of 8.57±0.02 and 409±1 mg/g in seawater and simulated solution. Despite its stable structure, AO-Fc exhibits a long life span and negligible weight loss revealed AO-Fc could be applied as a potential adsorbent for radionuclides

The production of hydrocarbons for the synthesis of readily available energy and multifunctional materials is of great importance in modern society. Zeolites have proven to be a boon for the targeted regulation of specific hydrocarbon as shape-selective catalyst in converting carbon resources. Yet our mechanistic understanding and quantitative description of shape-selectivity of zeolite catalysis remains rather limited, which restricts the upgrade of zeolite catalysts. Herein, we proposed quantitative principle of shape-selectivity for zeolite catalysis using methanol-to-hydrocarbons (MTH) as model. Combining with molecular simulations and infrared imaging, we unveil the competition of thermodynamic stability, preferential diffusion and favored secondary reactions between different hydrocarbons within zeolite framework are the essence of zeolite shape-selective catalysis. Notably, we provide methodology to in silico search for the optimal combination of framework topology and acidity properties of zeolites with operating conditions that potentially outperform commercial MTH catalysts to achieve high selectivity of desired hydrocarbon products.

While protein medications are promising for treatment of cancer and autoimmune diseases, challenges persist in terms of development and injection stability of high-concentration formulations. Here, the extensional flow properties of protein-excipient solutions are examined via dripping-onto-substrate (DoS) extensional rheology, using a model ovalbumin protein (OVA) and biocompatible excipients polysorbate 20 (PS20) and 80 (PS80). Despite similar PS structures, differences in extensional flow are observed based on PS identity in two regimes: at moderate total solution concentrations where surface tension differences drive changes in extensional flow behavior, and at small PS:OVA ratios, which impacts the onset of weakly elastic behavior. Undesirable elasticity is observed in ultra-concentrated formulations, independent of PS identity; higher PS contents are required to observe these effects than with analogous polymeric excipient solutions. These studies reveal novel extensional flow behaviors in protein-excipient solutions, and provide a straightforward methodology for assessing the extensional flow stability of new protein-excipient formulations.

In this study, the dissociation constants of the eight amines, namely, N-(2-aminoethyl)-1,3-propanediamine, 2-methylpentamethylene diamine, N, n-dimethyldipropylene-triamine, 3,3’-Diamino-n-methyldipropylamine, Bis[2-(n, n-dimethylamino) ethyl]ether, 2-[2-(Dimethylamino) ethoxy] Ethanol, 2-(dibutylamino) Ethanol and N-propylethanolamine were determined from 298.15 K to 313.15 K. Using the van’t Hoff equation, thermodynamic properties such as the standard state changes of enthalpy, entropy and Gibbs free energy were calculated. Using computational chemistry calculations, the amino group protonated first was predicted. Furthermore, computer free group contribution methods such as the original Perrin-Dempsey-Serjeant (PDS), the modified PDS and the Qian-Sun-Sun-Gao (QSSG) model were used to estimate the dissociation constants of the studied amines. In these methods, the QSSG provided the most accurate results as the database used in this method was updated with additional the functional groups as well as information about group positions. Finally, an artificial neural network was used to predict the pKa values.

The utilization of lignin remains a great challenge due to its complex non-repetitive structure and the lack of efficient catalyst. Herein, a single-atom catalyst Ni@N-C was designed via a facile chelation-anchored strategy. Ni atoms were immobilized on the N-doped carbon carrier by a two-stage pyrolysis of a mixture of D-glucosamine hydrochloride, nickel acetate and melamine. D-glucosamine hydrochloride as a chelating agent prevented the aggregation of Ni2+, and melamine provided enough N to anchor Ni by forming Ni-N4 structure. Ni@N-C gave a 31.2% yield of aromatic compounds from lignin hydrogenolysis, which was twice higher than that achieved by Ni cluster catalyst. Based on the experimental and DFT calculation results, the higher activity of Ni@N-C was attributed to its lower H2 dissociation energy and the reduced energy barriers of the transition states. The strategy described opens an efficient green avenue for preparing single-atom catalyst that possesses outstanding activity in lignin depolymerization.

Modern chemical processes need to be operated around different operating conditions to optimize plant economy, in response to dynamic supply chains. As such, the process control system needs to handle a wide range of operating conditions whilst optimizing system performance and ensuring stability during transitions. This article presents a reference-flexible nonlinear model predictive control approach using contraction based constraints. Firstly, a contraction condition that ensures convergence to any feasible state trajectories or setpoints is constructed. This condition is then imposed as a constraint on the optimization problem for model predictive control with a general (typically economic) cost function, utilizing Riemannian weighted graphs and shortest path techniques. The result is a reference flexible and fast optimal controller that can trade-off between the rate of target trajectory convergence and economic benefit (away from the desired process objective). The proposed approach is illustrated by a simulation study on a CSTR control problem.

This work considers a seeded fesoterodine fumarate (FF) cooling crystallization and presents the methodology and implementation of a real-time machine learning modeling-based predictive controller to handle batch-to-batch (B2B) parametric drift. Specifically, an autoencoder recurrent neural network-based model predictive controller (AERNN-MPC) is developed to optimize product yield, crystal size, and energy consumption while accounting for the physical constraints on cooling jacket temperature. Deviations in the kinetic parameters are considered in the closed-loop simulations to account for the B2B parametric drift, and two error-triggered online update mechanisms are proposed to address issues pertaining to the availability of real-time crystal property measurements and are incorporated into the AERNN-MPC to improve the model prediction accuracy. Closed-loop simulation results demonstrate that the proposed AERNN-MPC with online update, irrespective of the accessibility to real-time crystal property data, achieves a desired closed-loop performance in terms of maximizing product yield and minimizing energy consumption.