The study investigates effect of shape on gas bypassing and mixing of gas-fluidized Geldart A particles. A shallow Fluidized Bed (FB), configured at bench-scale, was used with Digital Image Analysis (DIA) for the investigation. The extent of scatter of tracer particles throughout the bed was assessed from DIA images of defluidized powder. A novel method, employed Jupyter notebook software, which directly determined Mixing Index from the images. Remarkably, Platelet-shaped China clay powder displayed the best mixing characteristics (Mixing Index: 0.79) with no significant bypassing. Angular shaped Quartz displayed moderate mixing (Mixing Index: 0.67), but high bypassing (Bypassing Index: 0.75). Contrary to conventional assumptions, spherical diatomite exhibited poor mixing (Mixing Index: 0.61) with the highest bypassing (Bypassing Index: 0.82). Platelet particles performed well even with fines removal or larger particles addition. Most likely, particle shape significantly influenced the number of available particle contact points, tracer migration, and tracer on particle binding.

Optimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL-based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this work proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions.

This work explores technical challenges and potential methodologies for understanding electrochemical Reactive CO2 Capture (RCC) mechanisms. RCC offers potential energy cost advantages by directly converting captured CO 2 into fuels and chemicals, unlike traditional carbon capture and utilization (CCU) processes that require sequential capture, concentration, and compression. However, direct conversion of captured CO 2 introduces complexity due to additional equilibrium buffer reactions, making it challenging to identify active species for reduction in electrochemical studies. This work discusses methods to integrate transport, thermodynamics, and kinetics concepts to identify active carbon sources in RCC. Vapor-Liquid Equilibrium (VLE) and transport models are validated against experimental results obtained in a gastight rotating cylinder electrode reactor and are shown as useful tools for studying RCC in heterogeneous electrocatalysts across different capture agents, solvents, and temperatures. This work establishes an experimental framework for advancing research in electrochemical RCC.

Accurate detection and analysis of bubble size and shape in bubbly flow are critical to understanding mass and heat transfer processes. Convolutional neural networks have limitations in different bubble images due to their dependence on large amounts of labeled data. A new foundational Segment Anything Model (SAM) recently attracts lots of attention for its zero-shot segmentation performance. Herein, we developed a novel image processing method named bubSAM, which achieves efficient and accurate bubble segmentation and shape reconstruction based on SAM. The segmentation performance of bubSAM is 30% higher than that of SAM, and its accuracy reaches 90% under different bubbly flow conditions. The accuracy of bubble shape reconstruction algorithm (BSR) in bubSAM is about 30% higher than that of typical ellipse fitting method, thus better restoring the geometric shape of bubbles. BubSAM can provide great support for understanding gas-liquid multiphase flow and design of industrial multiphase reactors.

The study explores perfume encapsulation and release from micellar solutions, emphasizing the impact of perfume-surfactant interactions and micelle geometry. Examining two mixed-micelle systems with branched-tail (ST2S/CAPB) and linear-tail (SLE1S/CAPB) surfactants, the research systematically investigates the effects of time, perfume concentration, and dilution on micelle geometry and headspace release. Utilizing SANS, GC-MS, and statistical analyses, the study reveals stable systems over time, with increased perfume content causing micelle growth and dilution leading to micelle shrinkage. Both systems exhibit consistent trends in micelle geometry, except the branched-tail system forming a lamellar phase with added perfume. GC-MS headspace concentration correlates with micelle geometry, influenced by tail group structure. The findings contribute insights into the intricate dynamics of perfume-micelle interactions, crucial for formulations in material science and actives delivery.

Modulating lattice strain in intermetallic compounds could effectively alter their electronic structure and binding energy, thus impacting catalytic activity. Strain is usually induced through lattice mismatch, achieved by constructing core-shell nanostructures or metal-substrate interfaces with complex reciprocity and distractors. However, in situ induced strain without interface-construction or lattice mismatch presents challenges. In this study, we precisely manipulate consecutive compressive strain from -0.5% to -0.8% in CoPt 3Pd intermetallic compound by inducing interior atomic radius mismatch. Precise strain control results in a negative shift of d-band center, dynamic charge distribution, and facilitates water dissociation, leading to the enhanced electrocatalytic activity. The CoPt 3Pd catalyst with -0.5% compressive strain exhibits exceptional hydrogen evolution activity, with an overpotential of 169 mV at 1 A cm -2. Our approach offers a straightforward method to manipulate compressive strain on intermetallic compound by atomic size mismatch, with broad implications for catalytic processes.

Fluorescence-based high-throughput screening approaches facilitate the discovery of enzymes and microorganisms for polyethylene terephthalate (PET) depolymerization and recycling. However, the traditional method of activating the fluorescence signal by cleaving the ester bond on small molecule probes has limited ability in detecting enzymatic activity towards polymeric substrates. This study proposes a novel fluorescence-based screening strategy that detects the release of sulfhydryl groups during the depolymerization of pseudo-PET polythioesters by polyesterases. The strategy successfully identifies the polyester-depolymerizing activity of leaf-branch compost cutinase (LCC ICCG), while porcine liver esterase (PLE) only hydrolyses small molecular substrates. When combined with a droplet microfluidic system, the strategy enables high-throughput screening of LCC ICCG. The study also demonstrates that screening for polyester-depolymerizing bacteria can be performed via a microplate reader platform. The new screening approach offers an efficient method for identifying enzymes and microbial resources for depolymerizing polyester-like plastics.

Polymer crystal hydrates (PCHs) are crystalline solids that form between a polymer and water. To date, only four distinct PCHs have been discovered – one of polyoxa- cyclobutane (POCB) and water, and three different polymorphs of polyethyleneimine (PEI) and water. These PCHs were first reported decades ago andhave fascinating structures and peculiar properties that make them potentially useful for a wide range of applications including refrigeration, proton conduction membranes, and desalination. This perspective revisits what is known about these compounds, categorizes their sim- ilarities and differences with other known compounds, and offers a perspective into future efforts to discover new PCHs to address technological needs for society.

Industrial chemical processes require sulfur-resistant catalysts, which reduce catalyst replacement costs and simplify process operations. Herein, a high-entropy-stabilized strategy was put forward for sulfur-resistant catalysis. A high entropy (Zn0.2Mg0.2Cu0.2Mn0.2Co0.2Al2O4) showed stable performance in CO oxidation with SO2, while unitary oxide and binary spinel oxide were all deactivated. The mechanism study showed that the adsorption of SO2 onto Zn0.2Mg0.2Cu0.2Mn0.2Co0.2Al2O4 was challenging. Moreover, Zn0.2Mg0.2Cu0.2Mn0.2Co0.2Al2O4 has a high degree of disorder, with five metal elements co-temporarily living in one cell location as cations. Thermodynamic equilibrium allows the sacrificial cations to capture the trace SO2 anchor on the Zn0.2Mg0.2Cu0.2Mn0.2Co0.2Al2O4 surface in time to protect the catalytically active cation. This work reveals the significance of high-entropy structures in sulfur resistance and offers a novel design strategy for sulfur-resistant catalysts.

There are few mass transfer studies to nanocatalysts (1 nm ≤ dp ≤ 100 nm). We have experimentally investigated the electrocatalytic reduction of hexacyanoferrate (III) to hexacyanoferrate (II) on gold nanospheres. The surface flux is insensitive to particle sizes of dp≥ 30 nm. For particle sizes of dp < 30 nm, the flux increases sharply with decreasing particle size. However, the measured fluxes are one to three orders of magnitude smaller than predicted by a purely diffusion-limited model. Using mathematical modeling, we evaluated six mechanisms potentially affecting mass transfer to a nanoparticle. Flux concentration due to the curvature effect and electromigration become important below 30 nm. Stabilizing layers on the particle also greatly influence the flux through electrosteric effects. Brownian advection, enhanced surface reactivity, and particle aggregation play negligible roles. Tuning the charge and the tortuosity of the stabilizer layer to potentiate the flux may be useful in nanosuspensions.

Dynamic product transitions are essential for achieving high product quality and reducing production costs. However, optimizing dynamic product transitions is a challenging task due to the complex dynamics of the process and the uncertainty in the measurements. In this work, a data-driven Bayesian approach for optimal dynamic product transitions is proposed. The approach is based on a dynamic optimization problem that is solved using a Bayesian optimization algorithm. One of the advantages of this approach for process optimization tasks is that it does not require a first-principles dynamic mathematical model. The approach is applied to three case studies. The results show that the proposed approach finds optimal transition trajectories meeting product composition requirements using smooth control actions. The approach is also able to cope with noisy measurements, which is an important feature in real-world applications. The proposed approach has several advantages including being data-driven, able to cope with noisy measurements.

Aminoxyl-mediated electrocatalysis offers a sustainable approach for the oxidation of alcohols to carboxylic acids. However, the high dosage of aminoxyl radicals and the limiting current density hinder the practicality of industrial application. Herein, a nickel-based electrocatalyst (Ni-tpdc) was synthesized on a three-dimensional porous graphite felt substrate. Taking advantage of the synergistic effect of the electrochemically activated Ni-tpdc/GF electrocatalyst with 4-acetamido-TEMPO, the yield of 4-pyridinecarboxylic acid was up to 99% with only 1 mol% ACT. A series of in situ measurements showed that NiOOH enhanced the activity of the intrinsic electrocatalyst. Additionally, the synergistic system was extended to the oxidation of various heterocyclic alcohols. The scale-up synthesis of chiral acid (LEV-CO 2H) was achieved in an improved electrolyzer with a yield of 95.4%, enantiomeric retention of 99.2%, and space-time yield of 34.12 kg/(m 3·h). The practicality and effectiveness of this synergistic electrocatalytic strategy in practical electrosynthesis was further demonstrated.

CO2 as a greenhouse gas causes a series of issues, and catalytic utilization of CO2 to fuels is a favorable strategy. Herein, we report the discovery in CO2 hydrogenation reaction where C5+ yield can be evidently improved by encapsulating ZnFe2O4 inside ZSM-5, in which the micro-environments of core-shell components can be tuned. For the ZnFe2O4, the K promoter makes the Fe-C structure more electron deficient than the Na, which contributes to the formation of long-chain olefins. ZSM-5 with K or Ce modification presents enhanced adsorption ability of alkene, then promoting aromatization and isomerization reactions of alkenes. Compared with Ce, K-ZSM-5 contributes to isomerization rather than aromatization, forming more isoparaffins. In this work, regulating the microenvironment of capsule catalysts provides a new idea for the design of efficient tandem catalysts, and expands the ability of hybrid catalysts against other catalysts, thus presenting an excellent catalytic efficiency for CO2 upgrading.

NETmix is a network of mixing chambers interconnected by channels, in which the most efficient mixing occurs under chaotic flow regimes. Chaos is highly influenced by the topology, particularly by the ratio chamber diameter/channel width, D/d. In this work, micromixing in NETmix is characterized through Lagrangian Mixing Simulation (LMS), which enables to establish the relation between the degree of mixing and the topology, D/d. The degree of mixing is then related with the Eulerian chaoticity from hydrodynamics and spectral analysis. The best mixing is obtained at D/d = 6.65 and is associated to a thin 8 shaped strange attractor. The spectral analysis shows a principal oscillation frequency at St = 0.13. Since the quasi-periodic regime is alternated with chaotic behaviour, NETmix has an intermittent bifurcation. For the NETmix system, orderly flows in the quasi-periodic regime have higher mixing intensity than less orderly flows in the weak or strong chaotic regimes.

Biomass supply chain performance is heavily affected by uncertainties stemming from supply, demand, or unexpected disruptions. Unlike petrochemical plants that use crude oil, biorefineries often have to deal with the uneven spatial-temporal distribution of feedstock supply. The modular production strategy provides more flexibility in chemical manufacturing by allowing fast capacity expansion and unit movement. However, modeling and optimizing modular biomass supply chain under uncertainties becomes challenging due to high-dimensionality and the existence of discrete decisions. This work optimizes the multiperiod biomass supply chain using the rolling horizon planning and two-stage stochastic programming framework. We then applied generalized Benders decomposition to reduce the computational complexity of the stochastic mixed integer nonlinear programming (MINLP) supply chain optimization. Furthermore, the solution of the stochastic programming could be used to quantitatively describe the life-cycle assessment uncertainties of the biomass supply chain performance, demonstrating seasonality and random variability.

A 1D multifluid population balance model approach is presented as a compromise between computational effort and accuracy. The approach is used to test process scenarios, perform sensitivity analysis, and provide a reliable scale-up and optimization tool. The study focuses on a mini-plant batch bubble column, where the scale-up behavior in terms of bubble column height, gas flux, and composition of the liquid phase is investigated. Although simplifications were made, the model requires calibration to experimental data using different calibration methods. An optimal calibration procedure is found that minimizes experimental effort while maximizing scalability. The model was tested on various liquid-phase compositions, and it was found to reproduce experimental data accurately. However, the model cannot reproduce flow regime changes and does not perform well outside the calibrated concentration. The study shows that the applied 1D multifluid populations balance approach is a valuable and reliable tool in multiphase reactor scale-up and optimization.

In this article, we extend a previously developed globally optimal enumeration methodology for the synthesis of Heat Exchanger Networks to the simultaneous synthesis of the network and the basic design of Heat Exchangers. Our procedure guarantees global optimality, unlike previous approaches, such as Pinch Technology, metaheuristics, or mathematical programming that do not guarantee it and sometimes do not even guarantee local optimality. The procedure is not iterative, and does not present any convergence issues. To enumerate HEN structures, we use linear methods and for the HEX design we use Set Trimming followed by sorting. In addition, because some network structures are incompatible with single shell exchangers, we use multiple shell exchangers in series. The comparison of the results of the proposed approach with two solution alternatives from the literature in two different problems indicates that considerable cost reductions may be obtained.

To overcome the limitations of empirical synthesis and expedite the discovery of new polymers, this work aims to develop a data-driven strategy for profoundly aiding in the design and screening of novel polyester materials. Initially, we collected 695 polyesters with their associated glass transition temperatures (Tgs) to develop a quantitative structure-property relationship (QSPR) model. The model underwent rigorous validation (external validation, internal validation, Y-random and application domain analysis) to demonstrate its robust predictive capabilities and high stability. Subsequently, by employing an in-silico retrosynthesis strategy, over 95000 virtual polyesters were designed, largely expanding the available space for polyester materials. External assessments highlight the good extrapolation ability of the QSPR model. Furthermore, we experimentally synthesized diverse virtual polyesters with Tgs covering a sufficient large temperature range. It is believed that this data-driven approach can drive future product development of polymer industry.