H. K. Lee, Yih Hong Lee, Charlynn Sher Lin Koh
et al.
Surface-enhanced Raman scattering (SERS) is a molecule-specific spectroscopic technique with diverse applications in (bio)chemistry, clinical diagnosis and toxin sensing. While hotspot engineering has expedited SERS development, it is still challenging to detect molecules with no specific affinity to plasmonic surfaces. With the aim of improving detection performances, we venture beyond hotspot engineering in this tutorial review and focus on emerging material design strategies to capture and confine analytes near SERS-active surfaces as well as various promising hybrid SERS platforms. We outline five major approaches to enhance SERS performance: (1) enlarging Raman scattering cross-sections of non-resonant molecules via chemical coupling reactions; (2) targeted chemical capturing of analytes through surface-grafted agents to localize them on plasmonic surfaces; (3) physically confining liquid analytes on non-wetting SERS-active surfaces and (4) confining gaseous analytes using porous materials over SERS hotspots; (5) synergizing conventional metal-based SERS platforms with functional materials such as graphene, semiconducting materials, and piezoelectric polymers. These approaches can be integrated with engineered hotspots as a multifaceted strategy to further boost SERS sensitivities that are unachievable using hotspot engineering alone. Finally, we highlight current challenges in this research area and suggest new research directions towards efficient SERS designs critical for real-world applications.
E. Pistikopoulos, A. Barbosa‐Póvoa, Jay H. Lee
et al.
Process Systems Engineering (PSE) is the scientific domain within chemical engineering, of describing and analyzing the behavior of a physicochemical system via mathematical modeling, data analytics, design, optimization and control. The webinar will provide a guide towards the evolution of PSE by looking at its history, core competencies, current status and future trends. We will first briefly present some of the key theoretical developments and computational tools in PSE. We will then argue that the versatility and effective employment of PSE methods and tools can offer a systematic platform to address current and future societal, industrial and scientific challenges that require a holistic, systems approach, in energy, the environment, the ‘industry of tomorrow’, and sustainability. We will finally outline the foundations of a Circular Economy Systems Engineering paradigm, that may provide The Generation Next of PSE’s thinking and practice.
S. Golshan, R. Sotudeh-Gharebagh, R. Zarghami
et al.
Abstract With increasing the computational resources, the number of publications about coupled computational fluid dynamics – discrete element method is in the rise in the recent years. This technique is very useful, especially in simulation of fluid-solid flows in process engineering. This paper provides an introduction to CFD-DEM modeling in process engineering systems, including heat and mass transfer and long range forces, and reviews the major researches in simulation of two-phase processes such as drying, coating, granulation, crystallization, chemical reactions (including combustion, gasification and pyrolysis) and mixing. Details of implementing unresolved CFD-DEM in these applications are explained in details and major assumptions and findings are discussed.
Nidhal Selmi, Jean-michel Bruel, Sébastien Mosser
et al.
Decision-making is a core engineering design activity that conveys the engineer's knowledge and translates it into courses of action. Capturing this form of knowledge can reap potential benefits for the engineering teams and enhance development efficiency. Despite its clear value, traditional decision capture often requires a significant amount of effort and still falls short of capturing the necessary context for reuse. Model-based systems engineering (MBSE) can be a promising solution to address these challenges by embedding decisions directly within system models, which can reduce the capture workload while maintaining explicit links to requirements, behaviors, and architectural elements. This article discusses a lightweight framework for integrating decision capture into MBSE workflows by representing decision alternatives as system model slices. Using a simplified industry example from aircraft architecture, we discuss the main challenges associated with decision capture and propose preliminary solutions to address these challenges.
Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions.
Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often. Model-based analyses can estimate dependability, yet in practice they are tricky to build and keep current because models are typically handcrafted. We claim that a simple connectivity-only topological model - just the service-dependency graph plus replica counts - can provide fast, low-risk availability estimates under fail-stop faults. To make this claim practical without hand-built models, we introduce model discovery: an automated step that can run in CI/CD or as an observability-platform capability, synthesizing an explicit, analyzable model from artifacts teams already have (e.g., distributed traces, service-mesh telemetry, configs/manifests) - providing an accessible gateway for teams to begin resilience testing. As a proof by instance on the DeathStarBench Social Network, we extract the dependency graph from Jaeger and estimate availability across two deployment modes and five failure rates. The discovered model closely tracks live fault-injection results; with replication, median error at mid-range failure rates is near zero, while no-replication shows signed biases consistent with excluded mechanisms. These results create two opportunities: first, to triage and reduce the scope of expensive chaos experiments in advance, and second, to generate real-time signals on the system's resilience posture as its topology evolves, preserving live validation for the most critical or ambiguous scenarios.
This report introduces the application of two advanced intelligent models, an adaptively trained neuro-fuzzy inference logic in a hybrid configuration (Hybrid-ANFIS) and multilayer perceptron neural network (MLP-NN) to accurately determine the equilibrium sulfur adsorption in the liquid phase of hydrocarbon/ sulfur compound solution. Models were meticulously developed using a dataset of 107 empirical observations of seven types of sulfur compounds. These models incorporate the influence of input parameters, including initial sulfur level, adsorbent weight, molecular weights of the solvent and solute, densities of the solvent and solute, adsorbent particle diameter, temperature, and the Si/Al ratio of the adsorbent. Notably, the equilibrium sulfur adsorption amount was considered as the sole output variable. To evaluate the performance and precision of the implemented models, graphical representations and quantitative analyses were employed. Moreover, an assessment between the results of implemented models of the existing study and outcomes of previous reports were conducted. The results indicate that both developed models provide precise predictions. However, the Hybrid-ANFIS model demonstrates a strong correlation in predicting the adsorption empirical data, with an average absolute relative deviation of 0.36 % and an overall R2 value and 0.9997. In addition, superiority of the Hybrid-ANFIS model in providing the most reliable and accurate predictions of adsorption experimental data among all types of implemented models was concluded. This study sets a new benchmark in adsorption modeling by providing the most accurate and generalizable predictive framework to date.
Organic pollutants remain a persistent threat to ecosystems and human health. In soils, humification gradually converts these compounds into stable humic substances and attenuates their toxicity, but the transformation can take decades—far too slow to match current pollution loads. In this Perspective, we argue that mature compost offers a pragmatic means to accelerate this process: it delivers partially humified intermediates that can “seed” soil humification and shorten its timescale from decades to seasons. Spectroscopic evidence shows that compost-derived humus is enriched in aromatic backbones and reactive functional groups (–COOH, –OH) that both catalyze further condensation of organic matter and immobilise pollutants through π–π stacking, hydrogen bonding and covalent coupling. By merging these catalytic and sorptive functions, compost amendments provide a scalable, low-cost route to the long-term stabilization of organic contaminants. We outline the key mechanistic questions that now need resolution—particularly the reactivity of specific intermediates in situ—to guide field trials and unlock the full potential of compost-driven accelerated humification as an environmental remediation platform.