Hasil untuk "Chemical engineering"

Marina Bosch

Scott Fogler

W. Mccabe, Julian C. Smith, P. Harriott

Yoo-Sung Ko, J. Kim, Jong An Lee et al.

Sustainable production of chemicals from renewable non-food biomass has become a promising alternative to overcome environmental issues caused by our heavy dependence on fossil resources. Systems metabolic engineering, which integrates traditional metabolic engineering with systems biology, synthetic biology, and evolutionary engineering, is enabling the development of microbial cell factories capable of efficiently producing a myriad of chemicals and materials including biofuels, bulk and fine chemicals, polymers, amino acids, natural products and drugs. In this paper, many tools and strategies of systems metabolic engineering, including in silico genome-scale metabolic simulation, sophisticated enzyme engineering, optimal gene expression modulation, in vivo biosensors, de novo pathway design, and genomic engineering, employed for developing microbial cell factories are reviewed. Also, detailed procedures of systems metabolic engineering used to develop microbial strains producing chemicals and materials are showcased. Finally, future challenges and perspectives in further advancing systems metabolic engineering and establishing biorefineries are discussed.

Giovanni Bovone, Oksana Y. Dudaryeva, B. Marco-Dufort et al.

Hydrogel adhesion inherently relies on engineering the contact surface at soft and hydrated interfaces. Upon contact, adhesion normally occurs through the formation of chemical or physical interactions between the disparate surfaces. The ability to form these adhesion junctions is challenging for hydrogels as the interfaces are wet and deformable and often contain low densities of functional groups. In this Review, we link the design of the binding chemistries or adhesion junctions, whether covalent, dynamic covalent, supramolecular, or physical, to the emergent adhesive properties of soft and hydrated interfaces. Wet adhesion is useful for bonding to or between tissues and implants for a range of biomedical applications. We highlight several recent and emerging adhesive hydrogels for use in biomedicine in the context of efficient junction design. The main focus is on engineering hydrogel adhesion through molecular design of the junctions to tailor the adhesion strength, reversibility, stability, and response to environmental stimuli.

K. McBride, K. Sundmacher

R. Ding, Ying-kang Yao, Binhan Sun et al.

Chemical boundary engineering expands the dimensionality of alloy design. For decades, grain boundary engineering has proven to be one of the most effective approaches for tailoring the mechanical properties of metallic materials, although there are limits to the fineness and types of microstructures achievable, due to the rapid increase in grain size once being exposed to thermal loads (low thermal stability of crystallographic boundaries). Here, we deploy a unique chemical boundary engineering (CBE) approach, augmenting the variety in available alloy design strategies, which enables us to create a material with an ultrafine hierarchically heterogeneous microstructure even after heating to high temperatures. When applied to plain steels with carbon content of only up to 0.2 weight %, this approach yields ultimate strength levels beyond 2.0 GPa in combination with good ductility (>20%). Although demonstrated here for plain carbon steels, the CBE design approach is, in principle, applicable also to other alloys.

Karina Kohl, Luigi Carro

Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.

Rigoberto Advincula, Jihua Chen

Chemical reaction engineering is key to industrial might and sustainable chemistry. This will be enabled using smart, efficient catalysts or catalysis ecosystems. This is possible with advanced artificial intelligence and machine learning (AI/ML) workflows that need to be employed as agentic AI projects. The fundamentals of catalysis need to be emphasized. A strong focus on catalyst design, mechanistic studies, reaction engineering, and scale-up must use ML-driven workflows, along with high-throughput experimentation (HTE) and an autonomous, self-driving laboratory (SDL). Laboratory experience and data-driven approaches are valuable when working together to accelerate this development. Parametrize and create a virtuous circle for data-driven discovery across heterogeneous, homogeneous, and biocatalysts to enable utility in many chemical process industries as agentic AI tasks. This article builds the case for discovery science in catalysis and continuous improvement in chemical reaction engineering with this new ecosystem.

Qiwei Zhang, Yicheng Wang, Jiayuan Wei et al.

Hydrogen energy, as a pivotal secondary energy carrier for the future, plays a core role in achieving global carbon neutrality goals through its green production. Currently, water electrolysis for hydrogen production, particularly alkaline water electrolysis, is regarded as the primary pathway for green hydrogen generation due to its technological maturity and cost‐effectiveness. However, this technology still faces challenges such as low operating current density, high energy consumption, and the difficulty in balancing the activity and stability of nonprecious metal catalysts under high current densities. The design of traditional electrocatalysts has reached a bottleneck, making breakthrough progress difficult. Therefore, this review focuses on internal and external field‐assisted water electrolysis strategies, systematically summarizing the latest research advances in field regulation for enhancing electrocatalytic performance. These strategies provide innovative approaches to addressing the energy efficiency and cost challenges in water electrolysis for hydrogen production, demonstrating the significant potential of field regulation in driving the development of next‐generation, high‐performance, and highly stable water electrolysis technologies.

Sudheesh Parathakkatt, Vaisakh Kizhuveetil, Gokul G. K. et al.

Worm-like micelles (WLMs) are dynamic, self-assembling supramolecular structures that exhibit complex viscoelastic behaviour due to their ability to undergo reversible scission, fusion, branching, and sequence rearrangement. This review provides a comprehensive analysis of recent theoretical advances in modelling WLM rheology, from classical reptation–scission theories to modern stochastic simulations and multi-scale population-balance frameworks. A central challenge addressed is the rheological indistinguishability of competing models under linear conditions, which renders inverse modelling ill-posed and necessitates the integration of experimental data, such as cryogenic transmission electron microscopy (cryo-TEM), small-angle neutron scattering (SANS), and flow birefringence, to constrain theoretical predictions. The article further explores the limitations of conventional models in capturing nonlinear responses, including shear banding and extensional strain hardening, and emphasizes the need for spatially resolved, structurally informed constitutive equations. Emerging tools, including neural networks and hybrid modular frameworks, are identified as promising solutions for bridging microscopic rearrangement dynamics with macroscopic flow behaviour. Ultimately, the development of predictive, physically grounded WLM models will be essential for advancing applications in formulation science, smart materials, and industrial processing.

Ana Paula Yumi Nishimura, Fernando Augusto Pedersen Voll, Nadia Krieger et al.

Kinetic models are important tools for guiding the design and optimization of lipase-catalyzed processes. These processes follow the Ping Pong bi bi mechanism, for which mechanistic kinetic equations can be derived. However, when there are several competing reactions, fully mechanistic models contain a large number of parameters, making it difficult to obtain reliable estimates, so simplified models are necessary. We present a two-step approach to developing semi-mechanistic models of such processes. The first step involves the estimation of the selectivities of the enzyme, using profiles for the reaction species plotted against the degree of reaction, while the second step involves empirical fitting to the same data, but plotted as a function of time. We demonstrate this two-step approach through four case studies based on the literature data for the lipase-catalyzed esterification of fatty acids with trimethylolpropane to produce biolubricants. The semi-mechanistic models were able to describe the data well. Our approach has the advantage of allowing selectivities to be estimated without confounding effects from phenomena such as enzyme denaturation and inhibition. It therefore provides a promising framework for developing models of enzyme-catalyzed processes that obey Ping Pong bi bi kinetics.

Pavlo Kozub, Nataliia Yilmaz, Svitlana Kozub

The study demonstrates the capabilities of a vector-based approach for calculating stoichiometric coefficients in chemical equations, using black powder as an illustrative example. A method is proposed for selecting and constraining intermediate interactions between reactants, as well as for identifying final products. It is shown that even a small number of components can lead to a large number of final and intermediate products. Through concrete calculations, a correlation is established between the number of possible chemical equations and the number of reactants. A methodology is proposed for computing all possible chemical equations within a reaction system for arbitrary component ratios, enabling the derivation of all feasible chemical reactions. Additionally, a method is developed for calculating the chemical composition for a fixed set of reactants, allowing for the evaluation of the set of products resulting from all possible chemical interactions given a specified initial composition.

Tim Wittenborg, Ildar Baimuratov, Ludvig Knöös Franzén et al.

The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-based approaches to managing aerospace engineering knowledge. By advancing these principles, research, and industry can achieve more efficient design processes, enhanced collaboration, and a stronger commitment to sustainable aviation.

Nasir U. Eisty, Jeffrey C. Carver, Johanna Cohoon et al.

In the evolving landscape of scientific and scholarly research, effective collaboration between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) is pivotal for advancing innovation and ensuring the integrity of computational methodologies. This paper presents ten strategic guidelines aimed at fostering productive partnerships between these two distinct yet complementary communities. The guidelines emphasize the importance of recognizing and respecting the cultural and operational differences between RSEs and SERs, proactively initiating and nurturing collaborations, and engaging within each other's professional environments. They advocate for identifying shared challenges, maintaining openness to emerging problems, ensuring mutual benefits, and serving as advocates for one another. Additionally, the guidelines highlight the necessity of vigilance in monitoring collaboration dynamics, securing institutional support, and defining clear, shared objectives. By adhering to these principles, RSEs and SERs can build synergistic relationships that enhance the quality and impact of research outcomes.

Oz Levy, Ilya Dikman, Natan Levy et al.

This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.

Heng Zhang, Jibin Zhou, Feiyang Xu et al.

The development of chemical engineering technology is a multi-stage process that encompasses laboratory research, scaling up, and industrial deployment. This process demands interdisciplinary col laboration and typically incurs significant time and economic costs. To tackle these challenges, we have developed a system based on ChemELLM in this work. This system enables users to interact freely with the chem ical engineering model, establishing a new paradigm for AI-driven in novation and accelerating technological advancements in the chemical sector.If you would like to experience our system, please visit our official website at: https://chemindustry.iflytek.com/chat.

Kim-Marie A. Dam, Harry B. Gristick, Yancheng E. Li et al.

Summary: HIV-1 broadly neutralizing antibodies (bNAbs) targeting the CD4-binding site (CD4bs) contain rare features that pose challenges to elicit these bNAbs through vaccination. The IOMA class of CD4bs bNAbs includes fewer rare features and somatic hypermutations (SHMs) to achieve broad neutralization, thus presenting a potentially accessible pathway for vaccine-induced bNAb development. Here, we created a library of IOMA variants in which each SHM was individually reverted to the inferred germline counterpart to investigate the roles of SHMs in conferring IOMA’s neutralization potency and breadth. Impacts on neutralization for each variant were evaluated, and this information was used to design minimally mutated IOMA-class variants (IOMAmin) that incorporated the fewest SHMs required for achieving IOMA’s neutralization breadth. A cryoelectron microscopy (cryo-EM) structure of an IOMAmin variant bound to Env was used to further interpret characteristics of IOMA variants to elucidate how IOMA’s structural features correlate with its neutralization mechanism, informing the design of IOMA-targeting immunogens.

V. Kumar, Deborah Carberry, Christian Beenfeldt et al.

Abstract With the advent of digitalization and industry 4.0, education in chemical and biochemical engineering has undergone significant revamping over the last two decades. However, undergraduate students sometimes do lack industrial exposure and are unable to visualise the complexity of actual process plants. Thereby, students might graduate without adequate professional hands-on experience. Similarly, in the process industry, operator training-simulators are widely used for the training of new and skilled operators. However, conventional training-simulators often fail to simulate reality and do not provide the user with the opportunity to experience unexpected and hazardous scenarios. In these regards, virtual reality appears to be a promising technology that can cater to the needs of both academia and industry. This paper discusses the opportunities and challenges for the incorporation of virtual reality into chemical and biochemical engineering education with an emphasis on the fundamental areas of technology, pedagogy and socio-economics. The paper emphasises the need for augmenting virtual reality interfaces with mathematical models to develop advanced immersive learning applications. Further, the paper stresses upon the need for novel educational impact assessment methodologies for the evaluation of virtual-reality-based learning. Finally, an ongoing case study application is presented to briefly discuss the social and economic implications, and to identify the bottlenecks involved in the adoption of virtual reality tools across chemical and biochemical engineering education.

Adrian Bajraktari, Michelle Binder, Andreas Vogelsang

Modern science is relying on software more than ever. The behavior and outcomes of this software shape the scientific and public discourse on important topics like climate change, economic growth, or the spread of infections. Most researchers creating software for scientific purposes are not trained in Software Engineering. As a consequence, research software is often developed ad hoc without following stringent processes. With this paper, we want to characterize research software as a new application domain that needs attention from the Requirements Engineering community. We conducted an exploratory study based on 8 interviews with 12 researchers who develop software. We describe how researchers elicit, document, and analyze requirements for research software and what processes they follow. From this, we derive specific challenges and describe a vision of Requirements Engineering for research software.