Hasil untuk "Chemical engineering"

R. Pintelon, J. Schoukens

J. E. Mark

J. Shackelford, W. Alexander

Zu-wei Ma, M. Kotaki, R. Inai et al.

Sangtae Kim, S. Karrila

M. Carrión-Vázquez, A. Oberhauser, Susan B. Fowler et al.

H. Miras, Jun Yan, D. Long et al.

Jeong Wook Lee, D. Na, J. Park et al.

Baolin Guo, P. Ma

Yang Zhang, E. Butelli, Cathie Martin

Y. Gong, Zheng Liu, A. Lupini et al.

Zhuyan Zhang, Zhaopeng Deng, L. Huo et al.

Alexandra Mazak-Huemer, Christian Huemer, Michael Vierhauser et al.

With the increasing significance of Research, Technology, and Innovation (RTI) policies in recent years, the demand for detailed information about the performance of these sectors has surged. Many of the current tools are limited in their application purpose. To address these issues, we introduce a requirements engineering process to identify stakeholders and elicitate requirements to derive a system architecture, for a web-based interactive and open-access RTI system monitoring tool. Based on several core modules, we introduce a multi-tier software architecture of how such a tool is generally implemented from the perspective of software engineers. A cornerstone of this architecture is the user-facing dashboard module. We describe in detail the requirements for this module and additionally illustrate these requirements with the real example of the Austrian RTI Monitor.

Jannatul Bushra, Md Habibor Rahman, Mohammed Shafae et al.

Reverse engineering can be used to derive a 3D model of an existing physical part when such a model is not readily available. For parts that will be fabricated with subtractive and formative manufacturing processes, existing reverse engineering techniques can be readily applied, but parts produced with additive manufacturing can present new challenges due to the high level of process-induced distortions and unique part attributes. This paper introduces an integrated 3D scanning and process simulation data-driven framework to minimize distortions of reverse-engineered additively manufactured components. This framework employs iterative finite element simulations to predict geometric distortions to minimize errors between the predicted and measured geometrical deviations of the key dimensional characteristics of the part. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion additive manufacturing. This paper presents a remanufacturing process that combines reverse engineering and additive manufacturing, leveraging geometric feature-based part compensation through process simulation. Our approach can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during reverse engineering. We evaluate the merits of STL-based and CAD-based approaches by quantifying the errors induced at the different steps of the proposed approach and analyzing the influence of varying part geometries. Using the proposed CAD-based method, the average absolute percent error between simulation-predicted distorted dimensions and actual measured dimensions of the manufactured parts was 0.087%, with better accuracy than the STL-based method.

Liu Hong, Hong Qian

The general theory of a complex system of nonlinear chemical reactions is a primary language of chemistry that includes chemical engineering and cellular biochemistry. Its significance as an analytical framework, however, has not been fully appreciated outside the community of physical chemists. In this review, we discuss the latest advances in the kinetics and Gibbsian thermodynamics of chemical reactions in a spatially homogeneous aqueous solution with a multiscale perspective on complex systems. From the microscopic level of single reaction events which are purely stochastic in continuous time, one at a time among a set of molecules, to the macroscopic chemical reaction systems in bulk in terms of deterministic rate equations, the mathematical descriptions of kinetic models for chemical reactions at different levels are presented in detail, with rigorous mathematical justifications presented. In parallel with the kinetics of chemical reactions, the irreversible thermodynamics of open systems and the stochastic thermodynamics along reactions trajectories are reviewed thoroughly. As a novel feature, the mathematical theory of large deviations is shown to play a pivotal role in the thermodynamics of chemical reactions in equilibrium and in irreversible processes. This review is expected to stimulate interests in and help defining multiscale phenomena and nonequilibrium thermodynamics in many research fields on population dynamics of interacting species using chemical reactions as an analytic paradigm.

Allysson Allex Araújo, Marcos Kalinowski, Matheus Paixao et al.

[Background] Emotional Intelligence (EI) can impact Software Engineering (SE) outcomes through improved team communication, conflict resolution, and stress management. SE workers face increasing pressure to develop both technical and interpersonal skills, as modern software development emphasizes collaborative work and complex team interactions. Despite EI's documented importance in professional practice, SE education continues to prioritize technical knowledge over emotional and social competencies. [Objective] This paper analyzes SE students' self-perceptions of their EI after a two-month cooperative learning project, using Mayer and Salovey's four-ability model to examine how students handle emotions in collaborative development. [Method] We conducted a case study with 29 SE students organized into four squads within a project-based learning course, collecting data through questionnaires and focus groups that included brainwriting and sharing circles, then analyzing the data using descriptive statistics and open coding. [Results] Students demonstrated stronger abilities in managing their own emotions compared to interpreting others' emotional states. Despite limited formal EI training, they developed informal strategies for emotional management, including structured planning and peer support networks, which they connected to improved productivity and conflict resolution. [Conclusion] This study shows how SE students perceive EI in a collaborative learning context and provides evidence-based insights into the important role of emotional competencies in SE education.

Davannendran Chandran, Revathi Raviadaran, Taib Iskandar Mohamad et al.

This paper aims to determine the effect of black soldier fly larvae (BSFL) oil-based biodiesel (B100) towards metal corrosion and elastomer degradation. Copper (Cu) and nitrile butadiene rubber (NBR) were exposed to BSFL-B100, industrial diesel (D2) and palm oil biodiesel (P-B100) for 1200 h at 25 °C. Corrosion rate, elastomer volume, tensile and hardness change, as well as surface morphology and total acid number (TAN) were determined. Cu had highest corrosion rates in BSFL-B100 at 0.00195 mm/yr, followed by in P-B100 at 0.00163 mm/yr and D2 at 0.00096 mm/yr. NBR exposed to BSFL-B100 had highest volume change by 31.4 % followed with in P-B100 at 29.0 % and finally in D2 at 19.4 %. BSFL-B100 exhibited significantly higher TAN increases than P-B100 and D2 after exposure to both Cu (188 %, 118 % and 84 %) and NBR (233 %, 139 % and 95 %) indicating greater fuel degradation in BSFL-B100, thus adversely affecting Cu corrosion and NBR degradation. Despite key fuel properties of prepared BSFL-B100 were within the limits specified in American Society for Testing and Materials (ASTM) D6751–23 and exhibited materials degradation comparable to P-B100, it however experienced higher fuel degradation measured in terms of TAN than P-B100 under similar experimental conditions. This could be associated to higher polyunsaturated fatty acids present in BSFL-B100 than P-B100 which is susceptible to oxidation which could adversely affect materials degradation.

An Su, Yanlin Luo, Chengwei Zhang et al.

Abstract The stability and therapeutic efficacy of antibody-drug conjugates (ADCs) are critically determined by the chemical linkers that connect the antibody to the cytotoxic payload, which is a key factor influencing drug release, plasma stability, and off-target toxicity. However, the current linker design space remains highly constrained, with most approved ADCs relying on a narrow set of established motifs. This limitation highlights an urgent need for computational tools capable of generating structurally diverse and synthetically accessible linkers. In this study, we introduce Linker-GPT, a Transformer-based deep learning framework leveraging self-attention mechanisms to generate novel ADC linkers with high structural diversity and synthetic feasibility. The model integrates transfer learning from large-scale molecular datasets and reinforcement learning (RL) to iteratively refine molecular properties such as drug-likeness and synthetic accessibility. During transfer learning, a pre-trained model was fine-tuned on a curated linker dataset, yielding molecules with high validity (0.894), novelty (0.997), and uniqueness (0.814 at 1k generation). RL further optimized the model to prioritize synthesizability and drug-like properties, resulting in 98.7% of generated molecules meeting target thresholds for QED (> 0.6), LogP (< 5), and synthetic accessibility score (SAS < 4). Linker-GPT demonstrates strong potential as a computational platform for accelerating the discovery and optimization of novel ADC linkers, offering a scalable solution for early-stage linker design. While these results are currently computational, they provide a foundation for future experimental validation and optimization.

Chao Jiang, Xiao Yang, Xingmei He et al.

Abstract Thermal rectification is a noteworthy phenomenon of asymmetric material, which enables the directional transfer of thermal energy. But the design and construction of such asymmetric thermal conductive materials with complex structures are full of challenges. Here, an additive manufacturing method is proposed to fabricate asymmetric porous composites from layer-by-layer cast emulsions, stabilized with Janus particles (JPs), for thermal rectification. The emulsions are remarkably stable, allowing each layer to be manipulated independently without interference, resulting in a porous structure with significant asymmetry. The thermal rectification of JPs-stabilized asymmetric porous composites (JAPCs) is investigated through both experiments and simulations. It is found that their thermal rectification ratios can be adjusted by altering the contrast between the two layers of the asymmetric porous composites, with a maximum value of 38%. This emulsion casting additive manufacturing method is suitable for large-scale production. A simple model demonstrates the potential of JAPCs to regulate thermal energy in ambient conditions with fluctuating temperatures. It is explored to achieve multilayer alternating porous composites, which cannot be achieved with gradient asymmetric approaches. This method provides a practical way to design and fabricate complicated porous structures with potential applications in additive manufacturing.

Haochen Li, Jonathan Leung, Zhiqi Shen

Large Language Models (LLMs) have shown prominent performance in various downstream tasks and prompt engineering plays a pivotal role in optimizing LLMs' performance. This paper, not only as an overview of current prompt engineering methods, but also aims to highlight the limitation of designing prompts based on an anthropomorphic assumption that expects LLMs to think like humans. From our review of 50 representative studies, we demonstrate that a goal-oriented prompt formulation, which guides LLMs to follow established human logical thinking, significantly improves the performance of LLMs. Furthermore, We introduce a novel taxonomy that categorizes goal-oriented prompting methods into five interconnected stages and we demonstrate the broad applicability of our framework. With four future directions proposed, we hope to further emphasize the power and potential of goal-oriented prompt engineering in all fields.