Current Opinion in Chemical Engineering最新文献

Composites of semiconductors forming various heterojunctions have become more and more extensively explored in photocatalysis, but the heterojunction classification seems abstruse. This paper aims to present a strategy for correctly elucidating the heterojunction type. The following guidelines are proposed: (I) the determination of band alignment, (II) the thermodynamic analysis of the interface, and (III) the verification of charge fate and charge transfer kinetics under irradiation. The experimental techniques appropriate for particular steps, including novel approaches, are listed, and their limitations are identified. Following the presented strategy, it is possible to conduct the analysis, which explores the intricate dynamics of photoinduced charges within correctly classified heterojunctions. Such a strategy gives a more reliable picture than often presented, oversimplified study.

Polymer carbon nitrides, such as C₃N₄ and C₃N₅, have considerable promise in photocatalysis because of their unusual thermostability, nontoxicity, and high solar energy usage efficiency. The S-scheme charge transfer mechanism can strengthen the whole photoactivity of a heterojunction by facilitating effective charge separation and maximizing redox capabilities. We outline the evolution from classic C₃N₄ to current C₃N₅, as well as the advanced S-scheme heterojunction technique for further photocatalysis advancement in energy conversion and environmental remediation. Furthermore, an outlook on future challenges and prospects for C₃N₄- and C₃N₅-based S-scheme heterojunction photocatalysts is presented.

Digital applications (DAs) are online/real-time software systems for decision support and control of process operations. They deliver superior process performance by drawing on accurate models derived from prior scientific and engineering knowledge, combined with plant data. This article reviews recent developments on DAs for open-loop process monitoring and closed-loop control in pharmaceutical operations and analyzes the requirements that they pose on the underlying mathematical models. It also discusses the challenges that need to be overcome to allow their deployment at scale in the pharmaceutical sector.

Advanced oxidation processes (AOPs) stand out as promising solutions for wastewater purification. An integration of a photothermal effect in AOPs will bring about a transformative impact on the processes. This innovative approach will boost treatment efficiency and overcome the limitations of the conventional ones. In this review, we thoroughly investigate the progress of applying a photothermal effect on various AOPs technologies for robust wastewater treatment. Our discussion encompasses the origin of the photothermal effect, elucidation of its working principles, and a summary of strategies for engineering the photothermal effect to catalytically remove contaminants. Furthermore, we delve into the diverse applications of photothermal effect in wastewater treatment technologies, spanning photocatalysis, peroxymonosulfate activation, and the Fenton reaction. Finally, we identify some challenges and outline future prospectives for the implementation of photothermal wastewater treatment. This review is anticipated to rejuvenate conventional AOPs, offering more clean, sustainable, and efficient technologies for wastewater treatment.

Hydrodynamic cavitation (HC) is widely acknowledged as a promising green approach for enhancing various production and waste management processes, such as water treatment, sludge pretreatment, lignocellulosic biomass (LCB) pretreatment, emulsification, and food processing. Despite demonstrating superior industrialization potential compared with other emerging technologies such as ultrasound and microwave, the widespread commercial adoption of HC remained limited even after three decades of development. This review aims to assess the current distance from industrialization and promote the advancement of HC by summarizing recent progress in the pilot or full-scale applications, particularly in biodiesel synthesis, water treatment, and the pretreatment of sludge and LCB. Special attention is given to treatment capacity and economic efficiency.

The fabrication of heterojunction has attracted much attention in the photodegradation of organic pollutants and water splitting due to that heterojunction structure markedly improved the separation efficiency of photogenerated carriers. In this review, Z-/S-scheme bismuth inorganics-based graphite carbon nitride heterojunctions are summarized for the enhanced photodegradation of organic pollutants and H₂ production. The main active species, photocatalytic degradation mechanism, and pathways of how the various heterojunctions perform the efficient improvement of photocatalytic efficiency are further addressed. Finally, the challenges and perspectives about this aspect of research are emphasized.

Traditionally, the pharmaceutical industry relied on resource-intensive and empirical methods for process development, optimization, and control. Heuristic approaches to pharmaceutical development and manufacturing have led to an unsustainable number of drug shortages and recalls and to escalating costs for launching new drug products. Optimization and control strategies rooted on process modeling are helping to advance pharmaceutical manufacturing by reducing development times and manufacturing costs, improving productivity and quality control, and enhancing process understanding. This perspective discusses recent developments toward model-based optimization, state estimation, and control of pharmaceutical processes. Ancillary areas such as software tools, equipment and sensor technology, and process modeling are first covered. Then, several recent academic and industrial case studies are discussed to highlight workflows and benefits related to the implementation of model-based optimization, state estimation, and control in (bio)pharmaceutical manufacturing. Finally, strategies for overcoming current challenges in the real-world application of model-based optimization and control are discussed.

Nitrosamine risk assessments are useful to identify processes and/or materials that exhibit legitimate safety concerns to evaluate appropriate actions and controls. Fundamental understanding of nitrosamine toxicity, formation pathways, fate, and purge aid risk quantification. In this work, available digital tools to aid nitrosamine risk assessments are reviewed. From structure–activity relationships through kinetic models to purge factors, these tools can shed light on the various aspects of a nitrosamine risk assessment. While tremendous progress has been made over the last few years, industry, academia, and government still need to focus on supporting science-based decisions and the integration of diverse tools to provide a holistic view of the nitrosamine risk.

Metal–organic frameworks (MOFs) have attracted much attention in photocatalysis due to their unique molecular architecture and diverse composition, where the performance can be effectively enhanced by the construction of heterojunctions. In this review, we summarize the recent progress of MOF-based photocatalysts with traditional heterojunctions (e.g. type I, type II, and p-n type) as well as Z- and S-scheme and even multicomponent heterojunctions, where the advantages and disadvantages of these heterojunctions in their photocatalytic applications toward environmental remediation and energy conversion are critically discussed. The review is concluded with a perspective for the further development of high-performance photocatalysts based on deliberate heterojunction engineering of MOF materials.

The primary objectives of pharmaceutical development encompass identifying the routes, processes, and conditions for producing medicines while establishing a control strategy to ensure acceptable quality attributes throughout the commercial manufacturing lifecycle. However, achieving these goals is challenged by uncertainties surrounding design decisions for the manufacturing process and variations in manufacturing methods resulting in distributions of outcomes during production. In this discussion, we focus on Bayesian approaches to quantify uncertainty and guide decision-making in process development.Bayesian modeling with Markov chain Monte Carlo proves effective in estimating process reliability. Recent advancements in surrogate models (e.g. Gaussian process, decision trees, and neural networks) offer novel means to quantify uncertainty and have shown success in designing experimental plans that reduce the number of required experiments to determine the optimal process design. By leveraging Bayesian approaches, chemical engineers can enhance their ability to navigate complex decision landscapes and optimize processes for improved efficiency and reliability.