Hasil untuk "Chemical engineering"

Raoof Bardestani, G. Patience, S. Kaliaguine

Hugo A. Jakobsen

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Maarten R. Dobbelaere, Pieter P. Plehiers, R. Vijver et al.

Abstract Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial and safety implications. Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations. In the last five years, the increasing availability of data and computational resources has led to a resurgence in machine learning-based research. Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases, benchmarks, and representations for chemical applications and new machine learning frameworks. Machine learning has significant advantages over traditional modeling techniques, including flexibility, accuracy, and execution speed. These strengths also come with weaknesses, such as the lack of interpretability of these black-box models. The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns, learn from data, and become more intelligent over time. The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis. Nevertheless, machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.

C. Lunardi, Anderson J. Gomes, Fellipy S. Rocha et al.

Artur M. Schweidtmann, E. Esche, Asja Fischer et al.

Fidele Benimana, Christopher Kucha, Anupam Roy et al.

ABSTRACT The global demand for edible flowers has increased due to their diverse applications in food, nutraceuticals, and the medical field. However, issues of species identification, adulteration, contamination, and quality necessitate the use of advanced methods to authenticate product quality for edible flowers. Conventional methods are expensive, time‐consuming, and require highly skilled personnel and technical expertise. Spectroscopic methods, including Fourier transform infrared, near‐infrared, and Raman spectroscopy, are efficient, fast, and non‐destructive, providing rapid insight into the chemical structure and authenticity of edible flowers. This review systematically summarizes the recent advances in spectroscopic methods for authenticating edible flowers, including the detection of chemical changes and ensuring product integrity. The primary goal is to examine the applications of spectroscopic techniques for assessing quality changes in edible flowers during processing for food applications. Spectroscopic techniques, such as FT‐IR, NIR, and Raman spectroscopy, are rapid, accurate, and non‐destructive alternatives for authenticating the composition and quality of edible flowers. These methods enable the detection of bioactive compounds, differentiation of species, and identification of adulterants with minimal sample processing. Furthermore, chemometric models enhance data analysis, allowing for automated classification and real‐time quality monitoring of edible flowers.

Shibin Li

This study examines the crystallization kinetics of Ni _50−x Mn _39 Sn _11 Fe _x (x = 0, 0.5, 2, 4 at.%) amorphous thin films prepared by DC magnetron sputtering. SEM and XRD confirm their amorphous structure. Non-isothermal DSC results show that the crystallization peak temperature increases from 542.7 K to 568.0 K as Fe content rises, while the apparent activation energy increases from 96.69 to 152.93 kJ mol ^−1 , indicating enhanced resistance to crystallization. Isothermal analysis yields Avrami exponents of 1.15–1.41 (average ≈1.2), corresponding to diffusion-controlled one-dimensional growth. Local activation-energy evaluation further reveals composition-dependent differences in nucleation and growth during various stages. These quantitative kinetic parameters clarify the role of Fe in altering crystallization behavior and support the optimization of annealing conditions for Ni-Mn-Sn-based functional thin films.

Xiang-Jing Kong, Jianrong Li

Abstract Given the current global energy and environmental issues resulting from the fast pace of industrialization, the discovery of new functional materials has become increasingly imperative in order to advance science and technology and address the associated challenges. The boom in metal–organic frameworks (MOFs) and MOF-derived materials in recent years has stimulated profound interest in exploring their structures and applications. The preparation, characterization, and processing of MOF materials are the basis of their full engagement in industrial implementation. With intensive research in these topics, it is time to promote the practical utilization of MOFs on an industrial scale, such as for green chemical engineering, by taking advantage of their superior functions. Many famous MOFs have already demonstrated superiority over traditional materials in solving real-world problems. This review starts with the basic concept of MOF chemistry and ends with a discussion of the industrial production and exploitation of MOFs in several fields. Its goal is to provide a general scope of application to inspire MOF researchers to convert their focus on academic research to one on practical applications. After the obstacles of cost, scale-up preparation, processability, and stability have been overcome, MOFs and MOF-based devices will gradually enter the factory, become a part of our daily lives, and help to create a future based on green production and green living.

Susheel Kumar Nethi, Venugopal Gunda, Nagabhishek Sirpu Natesh et al.

Summary: Pancreatic cancer (PC) exhibits profound metabolic adaptations that support tumor progression, survival, and therapy resistance. Hypoxia-inducible factor-1α (HIF-1α) is a key regulator of these processes, promoting metabolic reprogramming and chemoresistance. Given that mitochondrial metabolites modulate HIF-1α stability, targeting mitochondrial metabolism offers a promising therapeutic strategy. Niclosamide (Nic), a clinically approved anthelmintic, disrupts mitochondrial function but is limited by poor bioavailability. To overcome this, we developed polyanhydride-based Nic nanoparticles (NicNps) to enhance bioavailability and efficacy. NicNps impaired mitochondrial function, suppressed metabolism, downregulated HIF-1α, and inhibited growth of PC cells and orthotopic gemcitabine (Gem)-resistant mouse tumor models. Notably, NicNps combined with Gem overcame therapy resistance by synergistically reducing tumor hypoxia and HIF-1α-driven metabolic reprogramming. These findings highlight NicNps as a mitochondria-targeted, nanoparticle-based therapy that enhances Nic’s bioavailability while suppressing HIF-1α-driven adaptations. NicNps in combination with Gem offer a promising strategy to overcome therapy resistance and improve treatment outcomes in patients with pancreatic cancer.

Artur M. Schweidtmann

Laura Torrente-Murciano, Jennifer B. Dunn, P. Christofides et al.

Alexander Thebelt, Johannes Wiebe, Jan Kronqvist et al.

It is well-documented how artificial intelligence can have (and already is having) a big impact on chemical engineering. But classical machine learning approaches may be weak for many chemical engineering applications. This review discusses how challenging data characteristics arise in chemical engineering applications. We identify four characteristics of data arising in chemical engineering applications that make applying classical artificial intelligence approaches difficult: (1) high variance, low volume data, (2) low variance, high volume data, (3) noisy/corrupt/missing data, and (4) restricted data with physics-based limitations. For each of these four data characteristics, we discuss applications where these data characteristics arise and show how current chemical engineering research is extending the fields of data science and machine learning to incorporate these challenges. Finally, we identify several challenges for future research.

Asmae El Maangar, Clément Fleury, Stéphane Pellet-Rostaing et al.

We show hereby that recycling of NdFeB permanent magnets by selective leaching and precipitation is possible, using an electrolyte as hydrotrope, thus avoiding the need of any specific extractant molecules. We analyse the yield of the extractant-free process and show that the non toxic formulation of Sodium Salicylate and ethylacetate used as diluent and choosing the optimal tie-line in a ternary phase diagram allows extraction using any type of acid in the aqueous phase. Iron is well separated from rare earths and the product can be recovered directly form the fluid used in separation by oxalic acid precipitation.

Isuru A. Udugama, Martin Atkins, C. Bayer et al.

Educators in chemical engineering have a long and rich history of employing digital tools to solve fundamental engineering problems. Today, with the megatrend of digitalisation, there is a growing set of tools that can be used for chemical engineering education. However, identifying which tool is ideally suited to support teaching a given chemical engineering concept can be challenging. To answer this question a survey was distributed to Heads of Departments at IChemE institutions and members of the IChemE committees focused on digitalisation. The survey respondents rated Microsoft Excel (VBA), commercial simulators, and scripting tools as ideal for teaching core subjects such as mass and energy balances, mass transfer and reaction engineering while respondents found 3D Models, and Virtual/Augmented Reality models as being most suited for teaching subjects such as process design, safety and sustainability. Mathematical/programming simplicity, ease of maintenance, and low initial investment costs were identified as key non-technical aspects that will hinder the adoption of a given digital tool. Weighing the benefits of education and non-technical hurdles, the respondents preferred the use of simpler digitalisation platforms such as Excel and scripting languages over the more advanced platforms such as Virtual/Augmented Reality where possible. It

Liangliang Lin, Hue Quoc Pho, Lu Zong et al.

Abstract As an emerging technology that features the integration of microfluidics and non-equilibrium plasmas, microfluidic plasmas not only allow the precise and effective matter and heat transport via the microfluidic system, but also provide an extremely reactive medium full of high energy plasma-generated species. Therefore, they could open new pathways for chemical synthesis or chemical engineering processes that are hardly achievable by conventional methods. In this review, three main microfluidic plasma configurations are reviewed, including plasmas confined within microchannels, plasma jets beyond microchannels and microfluidic plasma arrays. The state-of-the-art diagnostic techniques for characterizing the microfluidic plasma are also examined. A broad range of applications of microfluidic plasmas of particular interest to chemistry and chemical engineering, such as nanomaterials fabrication, surface modification, chemical synthesis, environmental application, and micro total analysis systems, are discussed. The research gaps, bottlenecks and future perspectives of this novel technology are presented.

Soonil Hong, Byoungwook Park, Chandran Balamurugan et al.

Efforts to commercialize organic solar cells (OSCs) by developing roll-to-roll compatible modules have encountered challenges in optimizing printing processes to attain laboratory-level performance in fully printable OSC architectures. In this study, we present efficient OSC modules fabricated solely through printing methods. We systematically evaluated the impact of processing solvents on the morphology of crucial layers, such as the hole transport, photoactive, and electron transport layers, applied using the doctor blade coating method, with a particular focus on processability. Notably, deposition of charge transport layer using printing techniques is still a challenging task, mainly due to the hydrophobic characteristic of the organic photoactive layer. To overcome this issue, we investigated the solvent effect of a well-studied cathode interlayer, bathocuproine (BCP). We were able to form a uniform thin BCP film (∼10 nm) on a non-fullerene based organic photoactive layer using the doctor bladed coating method. Our results showed that the use of volatile alcohols in the BCP processing required a delicate balance between wettability and vaporization, which contrasted with the results for spin-coated films. These findings provide important insights into improving the efficiency of printing techniques for depositing charge transport layers. The fully printed OSC modules, featuring uniform and continuous BCP layer formation, achieved an impressive power conversion efficiency of 10.8% with a total area of 10.0 cm2 and a geometrical fill factor of 86.5%.

Antonija Tomic, Marin Kovacic, Hrvoje Kusic et al.

Although heterogeneous photocatalysis has shown promising results in degradation of contaminants of emerging concern (CECs), the mechanistic implications related to structural diversity of chemicals, affecting oxidative (by HO•) or reductive (by O₂•⁻) degradation pathways are still scarce. In this study, the degradation extents and rates of selected organics in the absence and presence of common scavengers for reactive oxygen species (ROS) generated during photocatalytic treatment were determined. The obtained values were then brought into correlation as <i>K</i> coefficients (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>M</mi><mrow><mi>HO</mi><mo>•</mo></mrow></msub></mrow></semantics></math></inline-formula>/<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>M</mi><mrow><msub><mi mathvariant="normal">O</mi><mn>2</mn></msub><msup><mo>•</mo><mo>−</mo></msup></mrow></msub></mrow></semantics></math></inline-formula>), denoting the ratio of organics degraded by two occurring mechanisms: oxidation and reduction via HO• and O₂•⁻. The compounds possessing <i>K</i> >> 1 favor oxidative degradation over HO•, and vice versa for reductive degradation (i.e., if <i>K</i> << 1 compounds undergo reductive reactions driven by O₂•⁻). Such empirical values were brought into correlation with structural features of CECs, represented by molecular descriptors, employing a quantitative structure activity/property relationship (QSA/PR) modeling. The functional stability and predictive power of the resulting QSA/PR model was confirmed by internal and external cross-validation. The most influential descriptors were found to be the size of the molecule and presence/absence of particular molecular fragments such as C − O and C − Cl bonds; the latter favors HO•-driven reaction, while the former the reductive pathway. The developed QSA/PR models can be considered robust predictive tools for evaluating distribution between degradation mechanisms occurring in photocatalytic treatment.

Chaudry Sajed Saraj, Subhash C. Singh, Gopal Verma et al.

Transition–metal-doped electrocatalysts are considered as low-cost alternatives of Pt and RuO2 electrocatalysts for large scale electrochemical generations of hydrogen and oxygen, respectively. Although, chemical synthesis, typically adopted to produce these electrocatalysts, is scalable but hazardous by-products and chemical wastes create growing environmental concerns. Here, we developed a single step, single pot, and environmentally friendly physical approach of electric field-assisted pulsed laser ablation in liquid for the synthesis of colloidal solution of pure CuMoO4 (CMO) electrocatalysts. The entire process took few minutes and did not involve or generate any chemical. A pulsed picosecond laser was used to ablate MoS2 target at the solid-liquid interface to generate spatially confined plasma plume. Two parallel electrodes (copper sheets) were mounted around the plasma plume to modulate the plasma parameters, control the reactions at the plasma-liquid interface, and simultaneously inject copper ions from the electrode to the laser-produced plasma (LPP) for the generation of CMO. nanoparticles. Surprisingly, we observed that by varying the applied electric field, we can efficiently control the size, shape, crystallinity, morphology, and composition of as produced CMO nanocomposites and enhance their hydrogen evolution reaction (HER) performance. The characterization results proves that the introduction of applied electric field during the laser ablation process significantly change the morphology of as-prepared nanomaterials, and the shape of these nanomaterials were spherical, spindle and cuboid for MoS2, CuO and CMO respectively. Among all the fabricated electrocatalysts, CMO-60 is the best HER performer in alkaline medium, while MoS2 and CuO nanoparticles were the worse. For CMO-60 sample, only 440 mV overpotential required to reach the current density of 10 mA/cm2 and as well as posess good stability. We found that electrocatalysts produced at a higher electric field have higher contents of copper and oxygen leading to a superior HER activity. The developed approach can be applied for the synthesis of other electrocatalysts for a range of chemical reactions.