Hasil untuk "Chemical engineering"

Karin Ackermann

Xu Xue, Yan Hu, Sicheng Wang et al.

Bone tissue engineering has emerged as a significant research area that provides promising novel tools for the preparation of biomimetic hydrogels applied in bone-related diseases (e.g., bone defects, cartilage damage, osteoarthritis, etc.). Herein, thermal sensitive polymers (e.g., PNIPAAm, Soluplus, etc.) were introduced into main chains to fabricate biomimetic hydrogels with injectability and compatibility for those bone defect need minimally invasive surgery. Mineral ions (e.g., calcium, copper, zinc, and magnesium), as an indispensable role in maintaining the balance of the organism, were linked with polymer chains to form functional hydrogels for accelerating bone regeneration. In the chemically triggered hydrogel section, advanced hydrogels crosslinked by different molecular agents (e.g., genipin, dopamine, caffeic acid, and tannic acid) possess many advantages, including extensive selectivity, rapid gel-forming capacity and tunable mechanical property. Additionally, photo crosslinking hydrogel with rapid response and mild condition can be triggered by different photoinitiators (e.g., I2959, LAP, eosin Y, riboflavin, etc.) under specific wavelength of light. Moreover, enzyme triggered hydrogels were also utilized in the tissue regeneration due to its rapid gel-forming capacity and excellent biocompatibility. Particularly, some key factors that can determine the therapy effect for bone tissue engineering were also mentioned. Finally, brief summaries and remaining issues on how to properly design clinical-oriented hydrogels were provided in this review.

Yanhao Liu, Wan Zhao, Yingming Zhao et al.

Abstract Bottom-up construction of artificial cells helps elucidate the working mechanism of cells. Signal transduction from extracellular to intracellular artificial cells is essential for autonomous artificial cells. It remains highly challenging to reconstitute G protein-coupled receptor (GPCR) signaling pathways to regulate downstream metabolism in artificial cells. Here, we reconstitute β2-adrenergic receptor, Gs subunit α and adenylate cyclase V into artificial cell membranes to enable signal transduction from extracellular isoproterenol (ISO) to intracellular cAMP (visualization via Epac1-cAMP probes). cAMP production is ISO dose-dependent, with a maximum amplification fold of 22.45 ± 2.14. By encapsulating the glycogenolytic pathway, cAMP activates protein kinase A, triggering phosphorylation of phosphorylase kinase and glycogen phosphorylase to convert glycogen to glucose-1-phosphate (G-1-P). G-1-P is further converted to 6-phosphogluconolactone accompanying with NADPH. ISO stimulation induces G-1-P and NADPH generation, achieving progressive signal amplification. The successful reconstitution of GPCR-mediated signaling pathway in artificial cells paves the way for developing autonomous artificial cells.

Esteban Parra, Sonia Haiduc, Preetha Chatterjee et al.

Peer review is the main mechanism by which the software engineering community assesses the quality of scientific results. However, the rapid growth of paper submissions in software engineering venues has outpaced the availability of qualified reviewers, creating a growing imbalance that risks constraining and negatively impacting the long-term growth of the Software Engineering (SE) research community. Our vision of the Future of the SE research landscape involves a more scalable, inclusive, and resilient peer review process that incorporates additional mechanisms for: 1) attracting and training newcomers to serve as high-quality reviewers, 2) incentivizing more community members to serve as peer reviewers, and 3) cautiously integrating AI tools to support a high-quality review process.

Himon Thakur, Armin Moin

The discussion around AI-Engineering, that is, Software Engineering (SE) for AI-enabled Systems, cannot ignore a crucial class of software systems that are increasingly becoming AI-enhanced: Those used to enable or support the SE process, such as Computer-Aided SE (CASE) tools and Integrated Development Environments (IDEs). In this paper, we study the energy efficiency of these systems. As AI becomes seamlessly available in these tools and, in many cases, is active by default, we are entering a new era with significant implications for energy consumption patterns throughout the Software Development Lifecycle (SDLC). We focus on advanced Machine Learning (ML) capabilities provided by Large Language Models (LLMs). Our proposed approach combines Retrieval-Augmented Generation (RAG) with Prompt Engineering Techniques (PETs) to enhance both the quality and energy efficiency of LLM-based code generation. We present a comprehensive framework that measures real-time energy consumption and inference time across diverse model architectures ranging from 125M to 7B parameters, including GPT-2, CodeLlama, Qwen 2.5, and DeepSeek Coder. These LLMs, chosen for practical reasons, are sufficient to validate the core ideas and provide a proof of concept for more in-depth future analysis.

Yang Yue, Zheng Jiang, Yi Wang

The heterogeneity in the organization of software engineering (SE) research historically exists, i.e., funded research model and hands-on model, which makes software engineering become a thriving interdisciplinary field in the last 50 years. However, the funded research model is becoming dominant in SE research recently, indicating such heterogeneity has been seriously and systematically threatened. In this essay, we first explain why the heterogeneity is needed in the organization of SE research, then present the current trend of SE research nowadays, as well as the consequences and potential futures. The choice is at our hands, and we urge our community to seriously consider maintaining the heterogeneity in the organization of software engineering research.

Klara Borowa, Andrzej Zalewski, Lech Madeyski

The software engineering researchers from countries with smaller economies, particularly non-English speaking ones, represent valuable minorities within the software engineering community. As researchers from Poland, we represent such a country. We analyzed the ICSE FOSE (Future of Software Engineering) community survey through reflexive thematic analysis to show our viewpoint on key software community issues. We believe that the main problem is the growing research-industry gap, which particularly impacts smaller communities and small local companies. Based on this analysis and our experiences, we present a set of recommendations for improvements that would enhance software engineering research and industrial collaborations in smaller economies.

Xuezhi Bian, Emily A. Carter

Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory while preserving training and computational efficiency of machine learned interatomic potentials. We demonstrate the framework on Ca2+-CO32- ion pairing in aqueous solution, a key process underlying CO2 mineralization in seawater. As proof of principle, we first show that finetuning a DFT-revPBE-D3(BJ) baseline model with embedded-DFT-SCAN data reproduces the DFT-SCAN free-energy surface within 1 kcal/mol across all solvation states. Extending the framework to embedded MP2 and localized natural-orbital CCSD(T) further refines the free-energy profile, revealing the crucial role of exact electron exchange and correlation in determining ion-pair stability and structure. ECW-TL thus provides a general, data-efficient route for transferring CW accuracy to large-scale simulations of complex aqueous and interfacial chemical processes.

Saheed O. Sanni, Ajibola A. Bayode, Hendrik G. Brink et al.

Over the years, the abuse of antibiotics has increased, leading to their presence in the environment. Therefore, a sustainable method for detecting these substances is crucial. Researchers have explored biomass-based carbon dots (CDs) to detect various contaminants, due to their low cost, environmental friendliness, and support of a circular economy. In our study, we reported the synthesis of CDs using pinecones (PCs) and pinebark (PB) through a sustainable microwave method. We characterized the PCCDs and PBCDs using X-ray diffraction, Raman spectroscopy, Transmission Electron Microscope, and Fourier transform infrared, Ultraviolet-visible, and photoluminescence (PL) spectroscopy. The PCCDs and PBCDs were tested for the detection of amoxicillin (AMX) and tetracycline (TC). The results indicated that the sizes of the PCCDs and PBCDs were 19.2 nm and 18.39 nm, respectively, and confirmed the presence of the 002 plane of the graphitic carbon structure. They exhibited excitation wavelength dependence, good stability, and quantum yields ranging from 6% to 11%. PCCDs and PBCDs demonstrated “turn-off” detection for TC and AMX. The limits of detection (LOD) for TC across a broader concentration range were found to be 0.062 µM for PCCDs and 0.2237 µM for PBCDs. For AMX detection, PBCDs presented an LOD of 0.49 µM.

Preetom Borah, Elif Irem Senyurt, Rohit Berlia et al.

The development of advanced diagnostic systems to measure and optimize emerging energetic material performance is critical for the defeat of Chemical Warfare Agents (CWA). This study presents an integrated multi-spectroscopic approach to monitor the interaction between a CWA simulant, Diisopropyl Methyl Phosphonate (DIMP), and combusting composite metal particles. A custom benchtop Polygonal Rotating Mirror Infrared Spectrometer (PRiMIRS), equipped with a customizable experimental chamber, is employed to observe DIMP decomposition. Tunable Diode Laser Absorption Spectroscopy (TDLAS) is used to measure path-averaged gas temperature profiles during combustion. In the experiment, the chamber is preheated to evaporate liquid DIMP. Various composite metal powders (Al-8Mg):3Zr, (Al-8Mg):Zr, 2(Al-8Mg):Zr, and 4(Al-8Mg):Zr are placed on a stainless steel mount and ignited using 3Al-2Ni sputter-deposited nanolayered foils. The combusting metal particles mix with the DIMP vapor, initiating chemical and thermal interactions. PRiMIRS captures DIMP spectral evolution, while TDLAS simultaneously monitors gas temperature. A spectral defeat parameter was developed to enable quantitative real-time assessment of the DIMP destruction. It uses infrared light absorption by both from DIMP and its immediate decomposition products Isopropyl Methyl Phosphonate (IMP) and Isopropyl Alcohol (IPA). Fourier Transform Infrared Spectroscopy (FTIR) serves as a secondary verification tool quantifying the decomposition products over extended timeframes, and Transmission Electron Microscopy (TEM) confirms the expected metal oxide dispersion within the reaction space. This study reports variability in DIMP defeat as a function of metal powder stoichiometry, metal powder loading, and path-averaged gas temperature profiles, offering critical insights into optimizing reactive materials for effective CWA neutralization.

Zhenpeng Chen, Chong Wang, Weisong Sun et al.

Large Language Models (LLMs) are increasingly integrated into software applications, giving rise to a broad class of prompt-enabled systems, in which prompts serve as the primary 'programming' interface for guiding system behavior. Building on this trend, a new software paradigm, promptware, has emerged, which treats natural language prompts as first-class software artifacts for interacting with LLMs. Unlike traditional software, which relies on formal programming languages and deterministic runtime environments, promptware is based on ambiguous, unstructured, and context-dependent natural language and operates on LLMs as runtime environments, which are probabilistic and non-deterministic. These fundamental differences introduce unique challenges in prompt development. In practice, prompt development remains largely ad hoc and relies heavily on time-consuming trial-and-error, a challenge we term the promptware crisis. To address this, we propose promptware engineering, a new methodology that adapts established Software Engineering (SE) principles to prompt development. Drawing on decades of success in traditional SE, we envision a systematic framework encompassing prompt requirements engineering, design, implementation, testing, debugging, evolution, deployment, and monitoring. Our framework re-contextualizes emerging prompt-related challenges within the SE lifecycle, providing principled guidance beyond ad-hoc practices. Without the SE discipline, prompt development is likely to remain mired in trial-and-error. This paper outlines a comprehensive roadmap for promptware engineering, identifying key research directions and offering actionable insights to advance the development of prompt-enabled systems.

Krishna Ronanki, Simon Arvidsson, Johan Axell

The rapid emergence of generative AI models like Large Language Models (LLMs) has demonstrated its utility across various activities, including within Requirements Engineering (RE). Ensuring the quality and accuracy of LLM-generated output is critical, with prompt engineering serving as a key technique to guide model responses. However, existing literature provides limited guidance on how prompt engineering can be leveraged, specifically for RE activities. The objective of this study is to explore the applicability of existing prompt engineering guidelines for the effective usage of LLMs within RE. To achieve this goal, we began by conducting a systematic review of primary literature to compile a non-exhaustive list of prompt engineering guidelines. Then, we conducted interviews with RE experts to present the extracted guidelines and gain insights on the advantages and limitations of their application within RE. Our literature review indicates a shortage of prompt engineering guidelines for domain-specific activities, specifically for RE. Our proposed mapping contributes to addressing this shortage. We conclude our study by identifying an important future line of research within this field.

Dean Brandner, Sergio Lucia

Optimal operation of chemical processes is vital for energy, resource, and cost savings in chemical engineering. The problem of optimal operation can be tackled with reinforcement learning, but traditional reinforcement learning methods face challenges due to hard constraints related to quality and safety that must be strictly satisfied, and the large amount of required training data. Chemical processes often cannot provide sufficient experimental data, and while detailed dynamic models can be an alternative, their complexity makes it computationally intractable to generate the needed data. Optimal control methods, such as model predictive control, also struggle with the complexity of the underlying dynamic models. Consequently, many chemical processes rely on manually defined operation recipes combined with simple linear controllers, leading to suboptimal performance and limited flexibility. In this work, we propose a novel approach that leverages expert knowledge embedded in operation recipes. By using reinforcement learning to optimize the parameters of these recipes and their underlying linear controllers, we achieve an optimized operation recipe. This method requires significantly less data, handles constraints more effectively, and is more interpretable than traditional reinforcement learning methods due to the structured nature of the recipes. We demonstrate the potential of our approach through simulation results of an industrial batch polymerization reactor, showing that it can approach the performance of optimal controllers while addressing the limitations of existing methods.

Mugeng Liu, Siqi Zhong, Weichen Bi et al.

Large language model-specific inference engines (in short as \emph{LLM inference engines}) have become a fundamental component of modern AI infrastructure, enabling the deployment of LLM-powered applications (LLM apps) across cloud and local devices. Despite their critical role, LLM inference engines are prone to bugs due to the immense resource demands of LLMs and the complexities of cross-platform compatibility. However, a systematic understanding of these bugs remains lacking. To bridge this gap, we present the first empirical study on bugs in LLM inference engines. We mine official repositories of 5 widely adopted LLM inference engines, constructing a comprehensive dataset of 929 real-world bugs. Through a rigorous open coding process, we analyze these bugs to uncover their symptoms, root causes, commonality, fix effort, fix strategies, and temporal evolution. Our findings reveal six bug symptom types and a taxonomy of 28 root causes, shedding light on the key challenges in bug detection and location within LLM inference engines. Based on these insights, we propose a series of actionable implications for researchers, inference engine vendors, and LLM app developers, along with general guidelines for developing LLM inference engines.

Ashis Kumar Mandal, Md Nadim, Chanchal K. Roy et al.

Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques that expedite software development. Finally, we discuss the opportunities and challenges in quantum-driven software engineering and QSE. Our study reveals that quantum machine learning (QML) and quantum optimization have substantial potential to address classical software engineering tasks, though this area is still limited. Current QSE tools and techniques lack robustness and maturity, indicating a need for more focus. One of the main challenges is that quantum computing has yet to reach its full potential.

Xue‐Qiang Zhang, Chen‐Zi Zhao, Jiaqi Huang et al.

Abstract Rechargeable lithium-ion batteries (LIBs) afford a profound impact on our modern daily life. However, LIBs are approaching the theoretical energy density, due to the inherent limitations of intercalation chemistry; thus, they cannot further satisfy the increasing demands of portable electronics, electric vehicles, and grids. Therefore, battery chemistries beyond LIBs are being widely investigated. Next-generation lithium (Li) batteries, which employ Li metal as the anode and intercalation or conversion materials as the cathode, receive the most intensive interest due to their high energy density and excellent potential for commercialization. Moreover, significant progress has been achieved in Li batteries attributed to the increasing fundamental understanding of the materials and reactions, as well as to technological improvement. This review starts by summarizing the electrolytes for next-generation Li batteries. Key challenges and recent progress in lithium-ion, lithium–sulfur, and lithium–oxygen batteries are then reviewed from the perspective of energy and chemical engineering science. Finally, possible directions for further development in Li batteries are presented. Next-generation Li batteries are expected to promote the sustainable development of human civilization.

Rubin Gulaboski

Understanding energetics and electron behavior has been pivotal in elucidating numerous fundamental phenomena, including electricity, corrosion, respiration, energy generation in biological systems, intermolecular interactions within living organisms, organic synthesis, drug development, enzyme functions, and the design of biosensors, among others. As 2024 records the centennial anniversary of the completion of the first polarograph by Nobel laureate Jaroslav Heyrovský (awarded the Nobel Prize in Chemistry in 1959), it presents an opportune moment to pay tribute to several eminent electrochemists who have made significant contributions to the field of voltammetric techniques. Following our recent acknowledgment of the outstanding women who have made substantial contributions to voltammetry in a prior publication, this article aims to briefly highlight the major achievements of several distinguished male figures in the field (Jaroslav Heyrovský, Allen J. Bard, Christian Amatore, Richard Compton, Jean-Michel Savéant, Fraser Armstrong, Fritz Scholz, Joseph Wang, Milivoj Lovrić, Valentin Mirčeski, Alan M. Bond). Given that many of these remarkable personalities have contributed both as authors and referees for the Macedonian Journal of Chemistry and Chemical Engineering, this tribute serves as a fitting acknowledgment of their remarkable accomplishments on the occasion of the journal's 50th anniversary.

Madhu Surana, Dhruti Sundar Pattanayak, V.K. Singh et al.

Graphitic carbon nitride (g-C3N4) has garnered significant attention due to its low cost, ease of preparation, high chemical stability, and non-toxicity. Nevertheless, pristine g-C3N4 faces challenges in simultaneously achieving a broad absorption range, high stability, efficient charge separation, and strong redox capability, which hampers its practical applications. Recently, g-C3N4-based Z-scheme photocatalysts have emerged as research hotspots owing to their robust redox ability, effective charge carrier separation, and capacity to harness visible light for degradation of tetracyclines (TCs) in waters. This review delves into the fundamental photocatalysis, and application of g-C3N4-based Z-scheme photocatalysts for the degradation of TCs pollutants. The review concludes with final remarks and a concise discussion on the prospects of g-C3N4-based Z-scheme photocatalysts.

Li Wei, Lei Zhao, Xun Zhu et al.

In this study, polylactic acid/graphene oxide/Dopamine (PLA/GO/DA) porous nanofiber membrane was prepared by electrospinning. L _16 (4 ^3 ) orthogonal experiment was designed to investigate the effects of reaction temperature, reaction time, and DA concentration on the adsorption performance of DA oxidized and self-polymerized on the fiber. Based on the characterization of scanning electron microscopy and the determination of the adsorption performance of the fiber membrane to methylene blue (MB) dye, data visualization analysis, variance analysis, and F-test were conducted to determine the optimal process parameters: reaction temperature of 45 °C, reaction time of 30 h, and DA concentration of 2 mg ml ^−1 . PLA/GO/PDA(Polydopamine) nanofiber was prepared and characterized under the optimal process parameters. The results showed that the average diameter of the PDA-loaded nanofiber increased from 737 nm to 996 nm, and a layer of PDA with a thickness of about 129 nm was loaded on the outer surface of the fiber, making the contact angle of the fiber membrane with 0° and becoming a hydrophilic material. In adsorption performance testing of MB, the PLA/GO/PDA nanofiber membrane prepared based on the PLA/GO/DA fiber membrane with an adsorption rate of 98.81 % in 24 h was superior to the PLA/GO/PDA nanofiber membrane prepared based on the PLA/GO fiber membrane.

Huiyang Hu, Prabhakar Busa, Yue Zhao et al.

Externally triggered drug delivery systems empower patients or healthcare providers to utilize external stimuli to initiate drug release from implanted systems. This approach holds significant potential for clinical disease management, offering appealing features like enhanced patient adherence through the elimination of needles and medication reminders. Additionally, it facilitates personalized medicine by granting patients control over the timing, dosage, and duration of drug release. Moreover, it enables precise drug delivery to targeted locations where external stimuli are applied. Advances in materials science, nanotechnology, chemistry, and biology have been pivotal in driving the development of these systems. This review presents an overview of the progress in research on drug release systems responsive to external stimuli, such as light, ultrasound, magnetic fields, and temperature. It discusses the construction strategies of externally triggered drug delivery systems, the mechanisms governing triggered drug release, and their applications in disease management.