RESUMEN
Bismuth-based catalysts are effective in converting carbon dioxide into formate via electrocatalysis. Precise control of the morphology, size, and facets of bismuth-based catalysts is crucial for achieving high selectivity and activity. In this work, an efficient, large-scale continuous production strategy is developed for achieving a porous nanospheres Bi2O3-FDCA material. First-principles simulations conducted in advance indicate that the Bi2O3 (111)/(200) facets help reduce the overpotential for formate production in electrocatalytic carbon dioxide reduction reaction (ECO2RR). Subsequently, using microfluidic technology and molecular control to precisely adjust the amount of 2, 5-furandicarboxylic acid, nanomaterials rich in (111)/(200) facets are successfully synthesized. Additionally, the morphology of the porous nanospheres significantly increases the adsorption capacity and active sites for carbon dioxide. These synergistic effects allow the porous Bi2O3-FDCA nanospheres to stably operate for 90 h in a flow cell at a current density of ≈250 mA cm- 2, with an average Faradaic efficiency for formate exceeding 90%. The approach of theoretically guided microfluidic technology for the large-scale synthesis of finely structured, efficient bismuth-based materials for ECO2RR may provide valuable references for the chemical engineering of intelligent nanocatalysts.
RESUMEN
Converting CO2 to olefins is an ideal route to achieve carbon neutrality. However, selective hydrogenation to light olefins, especially single-component olefin, while reducing CH4 formation remains a great challenge. Herein, we developed ZnZrOx /SSZ-13 tandem catalyst for the highly selective hydrogenation of CO2 to light olefins. This catalyst shows C2 = -C4 = and propylene selectivity up to 89.4 % and 52 %, respectively, while CH4 is suppressed down to 2 %, and there is no obvious deactivation. It is demonstrated that the isolated moderate Brønsted acid sites (BAS) of SSZ-13 promotes the rapid conversion of intermediate species derived from ZnZrOx , thereby enhancing the kinetic coupling of the reactions and inhibit the formation of alkanes and improve the light olefins selectivity. Besides, the weaker BAS of SSZ-13 promote the conversion of intermediates into aromatics with 4-6 methyl groups, which is conducive to the aromatics cycle. Accordingly, more propene can be obtained by elevating the Si/Al ratio of SSZ-13. This provides an efficient strategy for CO2 hydrogenation to light olefins with high selectivity.
RESUMEN
This study presents the first model-based optimal shutdown procedure of a multistage continuous crystallization process which aims at the maximization of on-spec production and minimization of the shutdown time. The cooling antisolvent crystallization of Aspirin (acetylsalicylic acid) in a three-stage continuous crystallizer was used as a case study. To address the optimal shutdown problem, several single optimization scenarios were considered to assess the impact of the degrees of freedom, discretization schemes, and optimization settings such as the constraints. The proposed optimal shutdown procedures showed that significant amounts of on-spec crystals can be produced both at fixed and variable shutdown times. Most importantly, the optimal shutdown procedures can match the steady-state productivity, based on the shutdown to steady-state productivity ratio (STSPR) which can easily reach 100%. Moreover, the residual shutdown material, considered as waste, can be dramatically reduced by >80% compared to the current standard shutdown procedures. Given the conflicting nature of the maximization of on-spec production and minimization of the shutdown time, multiobjective optimization of the shutdown operation was also addressed to identify the set of Pareto optimal solutions. Finally, a multicriteria decision-aiding method, based on multiattribute utility theory, was proposed to rank the Pareto optimal solutions to support the decision-making and help identify a suitable and feasible single optimal shutdown solution.
RESUMEN
Diagnosis and treatment of patients with cardiovascular and neurologic diseases primarily focus on the heart and brain, respectively. An increasing number of preclinical and clinical studies have confirmed a causal relationship between heart and brain diseases. Cardiogenic dementia is a cognitive impairment caused by heart dysfunction and has received increasing research attention. The prevention and treatment of cardiogenic dementia are essential to improve the quality of life, particularly in the elderly and aging population. This study describes the changes in cognitive function associated with coronary artery disease, myocardial infarction, heart failure, atrial fibrillation and heart valve disease. An updated understanding of the two known pathogenic mechanisms of cardiogenic dementia is presented and discussed. One is a cascade of events caused by cerebral hypoperfusion due to long-term reduction of cardiac output after heart disease, and the other is cognitive impairment regardless of the changes in cerebral blood flow after cardiac injury. Furthermore, potential medications for the prevention and treatment of cardiogenic dementia are reviewed, with particular attention to multicomponent herbal medicines.
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To address the issue of high energy consumption associated with monoethanolamine (MEA) regeneration in the CO2 capture process, solid acid catalysts have been widely investigated due to their performance in accelerating carbamate decomposition. The recently discovered carbon nanotube (CNT) catalyst presents efficient catalytic activity for bicarbonate decomposition. In this paper, bifunctional catalysts SO42-/TiO2-CNT (STC) were prepared, which could simultaneously catalyze carbamate and bicarbonate decomposition, and outstanding catalytic performance has been exhibited. STC significantly increased the CO2 desorption amount by 82.3% and decreased the relative heat duty by 46% compared to the MEA-CO2 solution without catalysts. The excellent stability of STC was confirmed by 15 cyclic absorption-desorption experiments, showing good practical feasibility for decreasing energy consumption in an industrial CO2 capture process. Furthermore, associated with the results of experimental characterization and theoretical calculations, the synergistic catalysis of STC catalysts via proton and charge transfer was proposed. This work demonstrated the potential of STC catalysts in improving the efficiency of amine regeneration processes and reducing energy consumption, contributing to the design of more effective and economical catalysts for carbon capture.
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Identifying the unnecessary H2 consumption existing in diesel hydrotreating process and calculating theoretical minimum H2 consumption are extremely critical for reducing H2 consumption in consideration of carbon reduction and resource utilization improvement. In this work, chemical reactions happened during diesel hydrotreating were categorized into hydrodesulfurization (HDS), hydrodenitrogenation (HDN), saturation of monocyclic aromatic hydrocarbons (MAHs), saturation of polycyclic aromatic hydrocarbons (PAHs), hydrogenation of olefins (HGO) and hydrocracking reactions (HCR). Then, in order to gain insights into where and how much H2 can be reduced, the ideal molecular compositions of the products were analyzed when theoretical minimum H2 was achieved for each type of reactions, which can give a genuine value of average relative molecular weight and average number of moles of H2 consumed per mole of reactants, leading to the establishment of method for calculating theoretical minimum H2 consumption. Additionally, the above method was used to calculate theoretical minimum H2 consumption of five diesel feedstocks with different properties to study the influence of content of S, N and PAHs in the feed on theoretical minimum H2 consumption. This method can provide guidance for experiments of H2 consumption reduction, and also help the refineries to save potential costs of H2.
RESUMEN
Rapid advancements in immersive communications and artificial intelligence have created a pressing demand for high-performance tactile sensing gloves capable of delivering high sensitivity and a wide sensing range. Unfortunately, existing tactile sensing gloves fall short in terms of user comfort and are ill-suited for underwater applications. To address these limitations, we propose a flexible hand gesture recognition glove (GRG) that contains high-performance micropillar tactile sensors (MPTSs) inspired by the flexible tube foot of a starfish. The as-prepared flexible sensors offer a wide working range (5 Pa to 450 kPa), superfast response time (23 ms), reliable repeatability (â¼10000 cycles), and a low limit of detection. Furthermore, these MPTSs are waterproof, which makes them well-suited for underwater applications. By integrating the high-performance MPTSs with a machine learning algorithm, the proposed GRG system achieves intelligent recognition of 16 hand gestures under water, which significantly extends real-time and effective communication capabilities for divers. The GRG system holds tremendous potential for a wide range of applications in the field of underwater communications.