Hasil untuk "Chemical engineering"

Yining Hong, Christopher S. Timperley, Christian Kästner

Machine learning (ML) components are increasingly integrated into software products, yet their complexity and inherent uncertainty often lead to unintended and hazardous consequences, both for individuals and society at large. Despite these risks, practitioners seldom adopt proactive approaches to anticipate and mitigate hazards before they occur. Traditional safety engineering approaches, such as Failure Mode and Effects Analysis (FMEA) and System Theoretic Process Analysis (STPA), offer systematic frameworks for early risk identification but are rarely adopted. This position paper advocates for integrating hazard analysis into the development of any ML-powered software product and calls for greater support to make this process accessible to developers. By using large language models (LLMs) to partially automate a modified STPA process with human oversight at critical steps, we expect to address two key challenges: the heavy dependency on highly experienced safety engineering experts, and the time-consuming, labor-intensive nature of traditional hazard analysis, which often impedes its integration into real-world development workflows. We illustrate our approach with a running example, demonstrating that many seemingly unanticipated issues can, in fact, be anticipated.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy, defined as the ability to understand and share others' perspectives and emotions, is essential in software engineering (SE), where developers often collaborate with diverse stakeholders. It is also considered as a vital competency in many professional fields such as medicine, healthcare, nursing, animal science, education, marketing, and project management. Despite its importance, empathy remains under-researched in SE. To further explore this, we conducted a socio-technical grounded theory (STGT) study through in-depth semi-structured interviews with 22 software developers and stakeholders. Our study explored the role of empathy in SE and how SE activities and processes can be improved by considering empathy. Through applying the systematic steps of STGT data analysis and theory development, we developed a theory that explains the role of empathy in SE. Our theory details the contexts in which empathy arises, the conditions that shape it, the causes and consequences of its presence and absence. We also identified contingencies for enhancing empathy or overcoming barriers to its expression. Our findings provide practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

YUE Yan, LI Yu, ZHOU Xianxian et al.

[Purposes] Because of the high charging overpotential and lagging electrochemical reaction kinetics caused by the low electronic conductivity of Li2O2 in Li-O2 batteries, it is important to develop cathode catalysts with high activity. [Methods] By coating nitrogen-doped molybdenum disulfide ultra-thin nanosheets on the surface of nitrogen-doped carbon nanotubes, the N-MoS2/N-CNTs composite was prepared through hydrothermal method combined with ammonia annealing method. The morphology, surface element state, and Li-O2 battery electrochemical performance of N-MoS2/N-CNTs were characterized by X-ray diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy, and electrochemical tests. [Results] The cathode obtains high initial charge/discharge capacity (7909/10015 mAh g-1), low charging overpotential, and high catalytic activity. Moreover, the performance of Li-O2 battery is further improved at large O2 mass transfer area. According to electrochemical reaction engineering, it is proposed that the possible initial discharge reaction interface is electrode/Li2O2 interface, and the charging reaction interface is electrode/electrolyte/Li2O2 interface. Three overpotential theories are used to explain the capacity and rate performance improvement mechanism of N-MoS2/N-CNTs cathode Li-O2 batteries, which is the decrease of electrochemical reaction overpotential (ηR) providing more space for the increase of concentration overpotential (ηC).

The CMS collaboration, A. Tumasyan, W. Adam et al.

Eni Budiyati, Mohammad Sofyan Habibburohman, Nur Ahmad Fauzi et al.

Using methanol, this study examined the hydroxylation process of epoxidized tamanu seed oil (ETSO), with an oxirane number of 3.92 to 4.04 mmol/g, under the catalyzation of sulfuric acid (H2SO4). The objectives of this study were, first, to synthesize polyol from ETSO, and, second, determine how temperature and catalyst concentration play a role in the hydroxylation process. During the experiment, a second-order reaction kinetic model was used for analysis. The hydroxylation process was conducted in a batch reactor for 4 hours under constant temperatures and stirring speed. During the experiment, the samples were taken every 30 minutes. The oxirane number of ETSO and the concentration of polyols were used to the reaction rates. The optimal conditions were found at a temperature of 65°C, with a methanol-to-epoxide mole ratio of 4:1 and a catalyst concentration of 3%. The pre-exponential factor (A) and the calculated activation energy (Ea) were found to be 59,041.74 g.mmol-1.min-1 and 44.69 kJ/mol, respectively. This research, therefore, has successfully identified the optimal conditions for the synthesis of bio-based polyols from tamanu oil.

Cuixia Liu, Meng Meng, Xian Luo

Based on the innovative mode driven by “data + artificial intelligence”, in this study, three methods, namely Gaussian noise (GAUSS Noise), the Generative Adversarial Network (GAN), and the optimized Generative Adversarial Network (GANPro), are adopted to expand and enhance the collected dataset of element contents and the hardness of the AlCoCrCuFeNi high-entropy alloy. Bayesian optimization with grid search is used to determine the optimal combination of hyperparameters, and two interpretability methods, SHAP and permutation importance, are employed to further explore the relationship between the element features of high-entropy alloys and hardness. The results show that the optimal data augmentation method is Gaussian noise enhancement; its accuracy reaches 97.4% under the addition of medium noise (σ = 0.003), and an optimal performance prediction model based on the existing dataset is finally constructed. Through the interpretability method, it is found that the contributions of Al and Ni are the most prominent. When the Al content exceeds 0.18 mol, it has a positive promoting effect on hardness, while Ni and Cu exhibit a critical effect of promotion–inhibition near 0.175 mol and 0.14 mol, respectively, revealing the nonlinear regulation law of element contents. This study solves the problem of revealing the mutual relationship between the element contents and hardness of high-entropy alloys in the case of a lack of alloy data and provides theoretical guidance for further improving the performance of high-entropy alloys.

Li Chen, Yuan Tian, Nana Zhang et al.

To prevent toxic and harmful gas suffocation accidents in underground metal mine stopes, the Fluent numerical simulation method was employed to investigate the wind field distribution patterns and the diffusion laws of blasting fumes in stopes with and without middle–end roadways under varying effective ranges. The simulation accuracy was validated through laboratory experiments. The results demonstrate that over time, the CO concentration in the blasting area decreases, while in other regions of the stope, it initially increases before declining. The presence or absence of a middle roadway does not significantly alter the migration and diffusion behavior of blasting fumes in the stope. When the effective range is ER–1, the simulation error is only 8 s. As the effective range increases, the time required to reduce the CO concentration to 24 ppm on the respiratory plane, across the entire space, and at the monitoring point follows a linearly increasing trend. Meanwhile, the maximum wind speed at the working face exhibits a linearly decreasing trend, whereas the peak CO concentration shows a linearly increasing trend. Under the ER–1 effective range, the CO concentration can be reduced to a safe threshold more rapidly. The experimental and simulation results exhibit an error margin within 16.97%, confirming the accuracy of the numerical simulation.

Jin Xuan, Jinfeng Liu

Seyed Reza Nabavi, Zhiyuan Wang, Gade Pandu Rangaiah

This chapter assesses the sensitivity of multi-criteria decision-making (MCDM) methods to modifications within the decision or objective matrix (DOM) in the context of chemical engineering optimization applications. Employing eight common or recent MCDM methods and three weighting methods, this study evaluates the impact of three specific DOM alterations: linear transformation of an objective (LTO), reciprocal objective reformulation (ROR), and the removal of alternatives (RA). Our comprehensive analysis reveals that the weights generated by entropy method are more sensitive to the examined modifications compared to the criteria importance through intercriteria correlation (CRITIC) and standard deviation (StDev) methods. ROR is found to have the largest effect on the ranking of alternatives. Moreover, certain methods, gray relational analysis (GRA) without any weights, multi-attributive border approximation area comparison (MABAC), combinative distance-based assessment (CODAS), and simple additive weighting (SAW) with entropy or CRITIC weights, and CODAS, SAW, and technique for order of preference by similarity to ideal solution (TOPSIS) with StDev weight are more robust to DOM modifications. This investigation not only corroborates the findings from the previous study, but also offers insights into the stability and reliability of MCDM methods in the context of chemical engineering.

CHEN Meng-han, Amel Mohamed, XU Ying-shu, YANG Zi-fan, JIA Hong-bing*

"Rubber blending was an effective way to develop new rubber materials that could achieve better properties than those of single components. The properties of blend could be precisely controlled by using different types of rubbers. However, most blends tended to phase separation, which led to deterioration in mechanical properties. Graphene oxide (GO) could be used as a novel compatibilizer to improve compatibility between rubbers［1-2］. 　In this work, GO was added to the blends of carboxylated nitrile butadiene rubber (XNBR) and styrene-butadiene rub-ber (SBR). The XNBR/SBR blend with different blend ratios were designed, and the effect of GO on the mechanical properties of GO/XNBR/SBR blended systems was analyzed in detail by a combination of molecular dynamics (MD) simulations and experiments. The formulation of rubber compounds was XNBR/SBR 100 phr (in mass, the same below), GO 0 or 3.0 phr, antideteriorant 4010 NA 2.0 phr, zinc oxide 2.0 phr, stearic acid 2.4 phr, accelerator CZ 2.2 phr and sulfur 1.5 phr. The mass ratios of XNBR/SBR were 25/75, 50/50 and 75/25, respectively. 　Through MD simulation, the number of hydrogen bonds of GO/XNBR/SBR blends was shown in Fig 1. The results showed that plenty of hydrogen bonds existed in GO/XNBR/SBR blends, and both the total number of hydrogen bonds and the number of interfacial hydrogen bonds increased with increasing XNBR content, indicating that the interfacial interaction of GO/XNBR/SBR blends was enhanced. After adding 75 phr of XNBR, the number of hydrogen bonds was the highest［3］. ■ Fig 1 Number of hydrogen bonds of GO/XNBR/SBR blends 　Tensile strength of XNBR/SBR and GO/XNBR/SBR blends were shown in Fig 2. It could be seen that the tensile strength of XNBR/SBR blend increased gradually with the growing of XNBR content, mainly due to the higher strength of XNBR compared to SBR. The higher the proportion of XNBR rubber was, the better the mechanical properties of the blends were. Compared to that of XNBR/SBR blend, the tensile strength of GO/XNBR/SBR blend increased by 86% when adding XNBR of 75 phr. The strong interfacial interactions, such as hydrogen bonds, may lead to a remarkable increase in the mechanical properties of the blend. ■ Fig 2 Tensile strength of XNBR/SBR and GO/XNBR/SBR blends"

Juan Hidalgo, Luis Hidalgo, Carlos Serrano et al.

Corrosion is a critical industrial problem. To solve this problem, the present research analyzed the influence of corrosive media on the efficiency of a guayusa inhibitor. Therefore, guayusa extract was obtained, and five groups of ASTMS A36 steel test tubes were prepared, each with variable extract concentrations (200 ppm, 400 ppm, 600 ppm, 800 ppm, and 1000 ppm) that were exposed to different corrosive media (5% NaCl, 5% NaCl + acetic acid, 1% HNO₃, and 10% HNO₃). The results obtained were compared to determine the percentage efficiency of the inhibitor in each of the corrosive media. This study provides a detailed understanding of how the corrosive environment influences the effectiveness of a guayusa inhibitor, which is used as a green inhibitor for the first time, allowing its viability and performance to be assessed under various conditions.

Jingchao Zhang, Yingping Bu, Zhuoyan Li et al.

Oxygen evolution reaction (OER) is a critical half-reaction in electrochemical overall water splitting and metal–air battery fields; however, the exploitation of the high activity of non-noble metal electrocatalysts to promote the intrinsic slow kinetics of OER is a vital and urgent research topic. Herein, Fe-doped Ni₃S₂ arrays were derived from MOF precursors and directly grown on nickel foam via the traditional solvothermal way. The arrays integrated into nickel foam can be used as self-supported electrodes directly without any adhesive. Due to the synergistic effect of Fe and Ni elements in the Ni₃S₂ structure, the optimized Fe_2.3%-Ni₃S₂/NF electrode delivers excellent OER activity in an alkaline medium. The optimized electrode only requires a small overpotential of 233 mV to reach the current density of 10 mA cm⁻², and the catalytic activity of the electrode can surpass several related electrodes reported in the literature. In addition, the long-term stability of the Fe_2.3%-Ni₃S₂/NF electrode showed no significant attenuation after 12 h of testing at a current density of 50 mA cm⁻². The introduction of Fe ions could modulate the electrical conductivity and morphology of the Ni₃S₂ structure and thus provide a high electrochemically active area, fast reaction sites, and charge transfer rate for OER activity.

Elena Laura Mazzoldi, Giulia Gaudenzi, Paola Serena Ginestra et al.

IntroductionTissue engineering has advanced significantly in recent years, owing primarily to additive manufacturing technology and the combination of biomaterials and cells known as 3D cell printing or Bioprinting. Nonetheless, various obstacles remain developing adequate 3D printed structures for biomedical applications, including bioinks optimization to meet biocompatibility and printability standards. Hydrogels are among the most intriguing bioinks because they mimic the natural extracellular matrix found in connective tissues and can create a highly hydrated environment that promotes cell attachment and proliferation; however, their mechanical properties are weak and difficult to control, making it difficult to print a proper 3D structure.MethodsIn this research, hydrogels based on Alginate and Gelatin are tested to evaluate the metabolic activity, going beyond the qualitative evaluation of cell viability. The easy-to-make hydrogel has been chosen due to the osmotic requirements of the cells for their metabolism, and the possibility to combine temperature and chemical crosslinking. Different compositions (%w/v) are tested (8% gel-7% alg, 4% gel-4% alg, 4% gel-2% alg), in order to obtain a 3D structure up to 10.3 ± 1.4 mm.ResultsThe goal of this paper is to validate the obtained cell-laden 3D structures in terms of cell metabolic activity up to 7 days, further highlighting the difference between printed and not printed cell-laden hydrogels. To this end, MS5 cells viability is determined by implementing the live/dead staining with the analysis of the cellular metabolic activity through ATP assay, enhancing the evaluation of the actual cells activity over cells number.DiscussionThe results of the two tests are not always comparable, indicating that they are not interchangeable but provide complementary pieces of information.

Christian Schäfer, Jakub Fojt, Eric Lindgren et al.

Altering chemical reactivity and material structure in confined optical environments is on the rise, and yet, a conclusive understanding of the microscopic mechanisms remains elusive. This originates mostly from the fact that accurately predicting vibrational and reactive dynamics for soluted ensembles of realistic molecules is no small endeavor, and adding (collective) strong light-matter interaction does not simplify matters. Here, we establish a framework based on a combination of machine learning (ML) models, trained using density-functional theory calculations, and molecular dynamics to accelerate such simulations. We then apply this approach to evaluate strong coupling, changes in reaction rate constant, and their influence on enthalpy and entropy for the deprotection reaction of 1-phenyl-2-trimethylsilylacetylene, which has been studied previously both experimentally and using ab initio simulations. While we find qualitative agreement with critical experimental observations, especially with regard to the changes in kinetics, we also find differences in comparison with previous theoretical predictions. The features for which the ML-accelerated and ab initio simulations agree show the experimentally estimated kinetic behavior. Conflicting features indicate that a contribution of dynamic electronic polarization to the reaction process is more relevant then currently believed. Our work demonstrates the practical use of ML for polaritonic chemistry, discusses limitations of common approximations and paves the way for a more holistic description of polaritonic chemistry.

Lvyang Yang, Jiankang Zhang, Huaiqiang Li et al.

The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality, engineering drawings are often blurred and distorted due to improper scanning, storage, and transmission, which may jeopardize the effectiveness of existing approaches. This paper focuses on restoring and recognizing low-quality engineering drawings, where an end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. The framework uses K-means clustering to classify different engineering drawing patches into simple and complex texture patches based on their gray level co-occurrence matrix statistics. Computer vision operations and a modified Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model are then used to improve the quality of the two types of patches, respectively. A modified Faster Region-based Convolutional Neural Network (Faster R-CNN) model is used to recognize the quality-enhanced graphical symbols. Additionally, a multi-stage task-driven collaborative learning strategy is proposed to train the modified ESRGAN and Faster R-CNN models to improve the resolution of engineering drawings in the direction that facilitates graphical symbol recognition, rather than human visual perception. A synthetic data generation method is also proposed to construct quality-degraded samples for training the framework. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%, demonstrating its superiority over previous approaches. Moreover, the framework is integrated into a widely-used power system software application to showcase its practicality.

Yanggeun Joo, Eunji Hwang, Heemyoung Hong et al.

Abstract Memory devices are an essential part of modern electronics. Efforts to move beyond the traditional “read” and “write” of digital information in volatile and non‐volatile memory devices are leading to the rapid growth of neuromorphic technology. There is a growing demand for memory devices with continuous memory states with various retention times and greater integration density with more energy‐efficient mechanisms. Two types of memory devices (i.e., non‐volatile digital memory and neuro‐synaptic devices) have been extensively investigated with emerging materials. Among numerous materials for such memory devices, in this review, the authors focus on 2D ferroelectric materials for promising memory and synaptic devices. Three types of memory devices based on 2D ferroelectric materials are classified and discussed here: 1) ferroelectric gating of semiconducting channels, 2) active ferroelectric channels, and 3) ferroelectric tunnel junction devices. It is known that atomically thin geometry competes with ferroelectricity, which can degrade the quality of the devices based on atomically thin ferroelectric materials. Various efforts to resolve the fundamental issue with emerging 2D ferroelectric materials and how they can be used as a critical element for memory and synaptic devices are surveyed.

Gianluigi Rozza, Oliver Schütze, Nicholas Fantuzzi

This Special Issue comprises the first collection of papers submitted by the Editorial Board Members (EBMs) of the journal <i>Mathematical and Computational Applications</i> (MCA), as well as outstanding scholars working in the core research fields of MCA [...]

Sarah Spiekermann, Till Winkler

Digital ethics is being discussed worldwide as a necessity to create more reliable IT systems. This discussion, fueled by the fear of uncontrollable artificial intelligence (AI) has moved many institutions and scientists to demand a value-based system engineering. This article presents how organizations can build responsible and ethically founded systems with the 'Value-based Engineering' (VBE) approach that was standardized in the IEEE 7000TM standard. VBE is a transparent, clearly-structured, step-by-step methodology combining innovation management, risk management, system and software engineering in one process framework. It embeds a robust value ontology and terminology. It has been tested in various case studies. This article introduces readers to the most important steps and contributions of the approach.

Chong Wang, Jian-Hao Lu, An-Bang Wang et al.

The shuttling effect of soluble lithium polysulfides (LiPSs) and the sluggish conversion kinetics of polysulfides into insoluble Li₂S₂/Li₂S severely hinders the practical application of Li-S batteries. Advanced catalysts can capture and accelerate the liquid–solid conversion of polysulfides. Herein, we try to make use of bismuth tantalum oxide with oxygen vacancies as an electrocatalyst to catalyze the conversion of LiPSs by reducing the sulfur reduction reaction (SRR) nucleation energy barrier. Oxygen vacancies in Bi₄TaO₇ nanoparticles alter the electron band structure to improve instinct electronic conductivity and catalytic activity. In addition, the defective surface could provide unsaturated bonds around the vacancies to enhance the chemisorption capability with LiPSs. Hence, a multidimensional carbon (super P/CNT/Graphene) standing sulfur cathode is prepared by coating oxygen vacancies Bi₄TaO_7−x nanoparticles, in which the multidimensional carbon (MC) with micropores structure can host sulfur and provide a fast electron/ion pathway, while the outer-coated oxygen vacancies with Bi₄TaO_7−x with improved electronic conductivity and strong affinities for polysulfides can work as an adsorptive and conductive protective layer to achieve the physical restriction and chemical immobilization of lithium polysulfides as well as speed up their catalytic conversion. Benefiting from the synergistic effects of different components, the S/C@Bi₃TaO_7−x coin cell cathode shows superior cycling and rate performance. Even under a high level of sulfur loading of 9.6 mg cm⁻², a relatively high initial areal capacity of 10.20 mAh cm⁻² and a specific energy density of 300 Wh kg⁻¹ are achieved with a low electrolyte/sulfur ratio of 3.3 µL mg⁻¹. Combined with experimental results and theoretical calculations, the mechanism by which the Bi₄TaO₇ with oxygen vacancies promotes the kinetics of polysulfide conversion reactions has been revealed. The design of the multiple confined cathode structure provides physical and chemical adsorption, fast charge transfer, and catalytic conversion for polysulfides.

Sulaiman Oladipo Lawal, Hiroki Nagasawa, Toshinori Tsuru et al.

This work investigated the long-term hydrothermal performance of composite carbon-SiO₂-ZrO₂ membranes. A carbon-SiO₂-ZrO₂ composite was formed from the inert pyrolysis of SiO₂-ZrO₂-polybenzoxazine resin. The carbon-SiO₂-ZrO₂ composites prepared at 550 and 750 °C had different surface and microstructural properties. A carbon-SiO₂-ZrO₂ membrane fabricated at 750 °C exhibited H₂ selectivity over CO₂, N₂, and CH₄ of 27, 139, and 1026, respectively, that were higher than those of a membrane fabricated at 550 °C (5, 12, and 11, respectively). In addition to maintaining high H₂ permeance and selectivity, the carbon-SiO₂-ZrO₂ membrane fabricated at 750 °C also showed better stability under hydrothermal conditions at steam partial pressures of 90 (30 mol%) and 150 kPa (50 mol%) compared with the membrane fabricated at 500 °C. This was attributed to the complete pyrolytic and ceramic transformation of the microstructure after pyrolysis at 750 °C. This work thus demonstrates the promise of carbon-SiO₂-ZrO₂ membranes for H₂ separation under severe hydrothermal conditions.