Hasil untuk "Chemical engineering"

K. Kobe

Timo Kehrer, Robert Haines, Guido Juckeland et al.

Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.

Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt

The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.

Daniel Mendez, Paris Avgeriou, Marcos Kalinowski et al.

Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.

Francisca Araújo, Solange Magalhães, Bruno Medronho et al.

Chitosan films with potential application in triboelectric nanogenerators (TENGs) represent a promising approach to replace non-biobased materials in these innovative devices. In the present work, chitosan with varying molecular weights (MW) and degrees of deacetylation was dissolved in aqueous acetic acid (AA) at different acid concentrations. It was observed that the MW had a greater influence on the viscosity of the solution compared to either the acid concentration or deacetylation degree. Gel formation occurred in high-MW chitosan solutions prepared with low AA concentration. Films prepared from chitosan solutions, through solvent-casting, were used to prepare TENGs. The power output of the TENGs increased with higher concentrations of AA used in the chitosan dissolution process. Similarly, the residual AA content in the dried films also increased with higher initial AA concentrations. Additionally, hot-pressing of the films significantly improves the TENG power output due to the decrease in morphological defects of the films. It was demonstrated that a good selection of the acid concentration not only facilitates the dissolution of chitosan but also plays a key role in defining the properties of the resulting solutions and films, thereby directly impacting the performance of the TENGs.

Min Ye, Lifen Meng

This study used Houttuynia cordata as the precursor to prepare high fluorescence quantum yield carbon quantum dots (Hc-CQDs) by a simple hydrothermal method. The surface of the Hc-CQDs contained abundant functional groups, such as carboxyl, hydroxyl, and amino groups, which indicated the Hc-CQDs had good water solubility. On the basis of the excellent fluorescence characteristics of Hc-CQDs, a sensor was constructed to achieve high selectivity detection of Cr³⁺, and the detection limit of the Hc-CQDs was 49 μg/L. The sensor also exhibited strong anti-interference ability and excellent reproducibility, which was used for the determination of Cr³⁺ in environmental water samples, and its spiked recovery rate reached over 90%. Therefore, the Hc-CQDs had potential application in the analysis.

Miisa J. Tavaststjerna, Stephen J. Picken, Santiago J. Garcia

Abstract Micropatterned surfaces with both hydrophilic and hydrophobic regions are relevant for a wide range of applications from fuel cells to water harvesting systems. The preferential nucleation of water on hydrophilic regions can also be used to control frost nucleation on chemically patterned surfaces. So far, this concept has been tested on brittle silicon surfaces with only a few different sizes and shapes of hydrophilic regions. In this work, the concept of controlled icing is investigated on five polymeric surfaces with different surface energies modified by micropatterning them with three types of hydrophilic polymer brushes. Frost formation and propagation on the resulting patterned surfaces with regions of varying wettability is monitored and quantified using high‐resolution thermal imaging. The study proves that control over frost nucleation and propagation using regions of varying wettability can be achieved on commodity polymers. In addition to influencing the time and location of ice nucleation, the local patterning affects the freezing propagation mode and rate due to its impact on the continuity and thickness of molecular water layers (MWL). These results show that local control over the state of MWLs is key to controlling both ice nucleation and propagation of freezing events on surfaces.

Zewei Wu, Yi Liu, Sai Chen et al.

Abstract CO2-assisted oxidative dehydrogenation of light alkane is a promising and innovative technology for light olefin production; however, the interference of side reactions and sluggish reactivity of CO2 limit olefin yields. This paper describes an economically viable tandem catalytic system by coupling alkane dehydrogenation and the reverse water gas shift (RWGS) reaction, employing PtSn/SiO2 as ethane dehydrogenation (EDH) sites and nano-CaCO3 as the hydrogen acceptor for sequent RWGS. This tandem catalytic system significantly surpasses commercial CrOx- and Pt-based catalytic systems, and breaks the EDH thermodynamic equilibrium limitation, reaching 142% of the nominal equilibrium ethylene yield of non-oxidative EDH process with 96.7% selectivity under industrially relevant conditions. Experimental characterization and theoretical analysis confirm that CaCO3 mediates the pathway of hydrogen spillover that originates from adjacent PtSn/SiO2, which effectively facilitates the RWGS reaction and thus shifts the EDH toward ethylene. This tandem catalytic strategy assisted by carbonates potentially expands the palette of catalytic systems pertaining to hydrogen transfer mechanisms in CO2-involved hydrogenation or dehydrogenation reactions.

Johan Cederbladh, Antonio Cicchetti

In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that promote specific and general V&V activities earlier to support more effective decision-making. As a result, there are incentives to introduce new technologies to remain competitive with the recently drastic changes in system complexity and heterogeneity. Performing V&V early on in development is a means of reducing risk for later error detection while moving key activities earlier in a process. We present a summary of the literature on early V&V and position existing challenges regarding potential solutions and future investigations. In particular, we reason that the software engineering community can act as a source for inspiration as many emerging technologies in the software domain are showing promise in the wider systems domain, and there already exist well formed methods for early V&V of software behaviour in the software modelling community. We conclude the paper with a road-map for future research and development for both researchers and practitioners to further develop the concepts discussed in the paper.

Oleksandr Kosenkov, Michael Unterkalmsteiner, Jannik Fischbach et al.

Context: Regulatory acts are a challenging source when eliciting, interpreting, and analyzing requirements. Requirements engineers often need to involve legal experts who, however, may often not be available. This raises the need for approaches to regulatory Requirements Engineering (RE) covering and integrating both legal and engineering perspectives. Problem: Regulatory RE approaches need to capture and reflect both the elementary concepts and relationships from a legal perspective and their seamless transition to concepts used to specify software requirements. No existing approach considers explicating and managing legal domain knowledge and engineering-legal coordination. Method: We conducted focus group sessions with legal researchers to identify the core challenges to establishing a regulatory RE approach. Based on our findings, we developed a candidate solution and conducted a first conceptual validation to assess its feasibility. Results: We introduce the first version of our Artifact Model for Regulatory Requirements Engineering (AM4RRE) and its conceptual foundation. It provides a blueprint for applying legal (modelling) concepts and well-established RE concepts. Our initial results suggest that artifact-centric RE can be applied to managing legal domain knowledge and engineering-legal coordination. Conclusions: The focus groups that served as a basis for building our model and the results from the expert validation both strengthen our confidence that we already provide a valuable basis for systematically integrating legal concepts into RE. This overcomes contemporary challenges to regulatory RE and serves as a basis for exposure to critical discussions in the community before continuing with the development of tool-supported extensions and large-scale empirical evaluations in practice.

Jukka Ruohonen, Kalle Hjerppe

Participatory citizen platforms are innovative solutions to digitally better engage citizens in policy-making and deliberative democracy in general. Although these platforms have been used also in an engineering context, thus far, there is no existing work for connecting the platforms to requirements engineering. The present paper fills this notable gap. In addition to discussing the platforms in conjunction with requirements engineering, the paper elaborates potential advantages and disadvantages, thus paving the way for a future pilot study in a software engineering context. With these engineering tenets, the paper also contributes to the research of large socio-technical software systems in a public sector context, including their implementation and governance.

Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh

Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from many sources, including training data. Existing literature suggests that bugs in training data are highly prevalent, but little research has focused on understanding their impacts on the models used in software engineering tasks. In this paper, we address this research gap through a comprehensive empirical investigation focused on three types of data prevalent in software engineering tasks: code-based, text-based, and metric-based. Using state-of-the-art baselines, we compare the models trained on clean datasets with those trained on datasets with quality issues and without proper preprocessing. By analysing the gradients, weights, and biases from neural networks under training, we identify the symptoms of data quality and preprocessing issues. Our analysis reveals that quality issues in code data cause biased learning and gradient instability, whereas problems in text data lead to overfitting and poor generalisation of models. On the other hand, quality issues in metric data result in exploding gradients and model overfitting, and inadequate preprocessing exacerbates these effects across all three data types. Finally, we demonstrate the validity and generalizability of our findings using six new datasets. Our research provides a better understanding of the impact and symptoms of data bugs in software engineering datasets. Practitioners and researchers can leverage these findings to develop better monitoring systems and data-cleaning methods to help detect and resolve data bugs in deep learning systems.

Francisco Muñoz, Ziyad S. Haidar, Andreu Puigdollers et al.

The demand for novel tissue grafting and regenerative wound care biomaterials is growing as traditional options often fall short in biocompatibility, functional integration with human tissue, associated cost(s), and sustainability. Salmon aquaculture generates significant volumes of waste, offering a sustainable opportunity for biomaterial production, particularly in osteo-conduction/-induction, and de novo clinical/surgical bone regeneration. Henceforth, this study explores re-purposing salmon waste through a standardized pre-treatment process that minimizes the biological <i>waste</i> content, followed by a treatment stage to remove proteins, lipids, and other compounds, resulting in a mineral-rich substrate. Herein, we examined various methods—alkaline hydrolysis, calcination, and NaOH hydrolysis—to better identify and determine the most efficient and effective process for producing bio-functional nano-sized hydroxyapatite. Through comprehensive chemical, physical, and biological assessments, including Raman spectroscopy and X-ray diffraction, we also optimized the extraction process. Our modified and innovative alkaline hydrolysis–calcination method yielded salmon-derived hydroxyapatite with a highly crystalline structure, an optimal Ca/P ratio, and excellent biocompatibility. The attractive nano-scale cellular/tissular properties and favorable molecular characteristics, particularly well-suited for bone repair, are comparable to or even surpass those of synthetic, human, bovine, and porcine hydroxyapatite, positioning it as a promising candidate for use in tissue engineering, wound healing, and regenerative medicine indications.

Kelechi G. Kalu, Taylor R. Schorlemmer, Sophie Chen et al.

The primary theory of software engineering is that an organization's Policies and Processes influence the quality of its Products. We call this the PPP Theory. Although empirical software engineering research has grown common, it is unclear whether researchers are trying to evaluate the PPP Theory. To assess this, we analyzed half (33) of the empirical works published over the last two years in three prominent software engineering conferences. In this sample, 70% focus on policies/processes or products, not both. Only 33% provided measurements relating policy/process and products. We make four recommendations: (1) Use PPP Theory in study design; (2) Study feedback relationships; (3) Diversify the studied feedforward relationships; and (4) Disentangle policy and process. Let us remember that research results are in the context of, and with respect to, the relationship between software products, processes, and policies.

Yaohou Fan, Chetan Arora, Christoph Treude

Stop words, which are considered non-predictive, are often eliminated in natural language processing tasks. However, the definition of uninformative vocabulary is vague, so most algorithms use general knowledge-based stop lists to remove stop words. There is an ongoing debate among academics about the usefulness of stop word elimination, especially in domain-specific settings. In this work, we investigate the usefulness of stop word removal in a software engineering context. To do this, we replicate and experiment with three software engineering research tools from related work. Additionally, we construct a corpus of software engineering domain-related text from 10,000 Stack Overflow questions and identify 200 domain-specific stop words using traditional information-theoretic methods. Our results show that the use of domain-specific stop words significantly improved the performance of research tools compared to the use of a general stop list and that 17 out of 19 evaluation measures showed better performance. Online appendix: https://zenodo.org/record/7865748

Heng Zhao, Xiao Wang, Xingxing Wu et al.

Biomass valorization by photoreforming approach provides a promising and alternative strategy to generate value-added chemicals and fuels. In this work, we demonstrate the selective production of lactic acid from glucose photoreforming over pristine graphitic carbon nitride (g-C3N4) photocatalyst. Control experiments screen the best condition for the highest yield of lactic acid, including modulating pH, catalyst loading, and reaction time. 100% glucose conversion is achieved along with almost 100% lactic acid yield under the optimized condition. Density functional theory (DFT) calculations reveal that the rate-determining step (RDS) of the overall reaction on g-C3N4 is the conversion of pyruvaldehyde, where an electron transfer takes place. This present work provides experimental insights and theoretical understanding for selective lactic acid production from biomass photoreforming.

Lei Zhao, Shirong Sun, Jinxin Lin et al.

Highlights The N/S co-doped lignin-derived porous carbon (NSLPCs) with ultra-high heteroatom doping was prepared through a novel supramolecule-mediated pyrolysis strategy. Covalently bonded graphitic carbon/amorphous carbon intermediates induce the formation of high heteroatom doping. The high heteroatom doping of NSLPC could provide abundant defective active sites for the adsorption of K+.

Robert T. Woodward, Alexander Bismarck

editorial

Per Fors, Thomas Taro Lennerfors, Jonathan Woodward

This paper aims to outline an approach for case-based chemistry and chemical engineering education for sustainability. Education for Sustainability is assumed to offer a holistic approach to equip students with the knowledge, skills, values, and attitudes needed to contribute to a more sustainable society in their future careers. While Case-Based Education traditionally focuses on disciplinary learning in simulated settings, it can also effectively teach essential sustainability-related skills like integrated problem-solving, critical thinking, and systems thinking. The approach we propose is “case hacking”, which should be understood as utilizing existing business cases while incorporating supplementary resources to align the assignment with intended learning objectives. This expansion of the cases involves, among other things, introducing additional questions and assignments, perspectives from stakeholders previously unexplored in the original case, and the integration of recent research articles from relevant fields. We advocate for the use of case hacking when educators want to harness the educational benefits of Case-Based Education while emphasizing the complexity of sustainability-related challenges faced by industrial companies today. As an illustrative example, we demonstrate the process of hacking a case related to Green Chemistry in the pharmaceutical industry, highlighting specific challenges for chemistry and chemical engineering education. We hope this example will inspire educators in these disciplinary contexts to engage with the case hacking approach as they navigate the complex terrain of sustainability.

Adrian Gheata, Alessandra Spada, Manon Wittwer et al.

Inorganic nanoparticles (NPs) have emerged as promising tools in biomedical applications, owing to their inherent physicochemical properties and their ease of functionalization. In all potential applications, the surface functionalization strategy is a key step to ensure that NPs are able to overcome the barriers encountered in physiological media, while introducing specific reactive moieties to enable post-functionalization. Silanization appears as a versatile NP-coating strategy, due to the biocompatibility and stability of silica, thus justifying the need for robust and well controlled silanization protocols. Herein, we describe a procedure for the silica coating of harmonic metal oxide NPs (LiNbO₃, LNO) using a water-in-oil microemulsion (W/O ME) approach. Through optimized ME conditions, the silanization of LNO NPs was achieved by the condensation of silica precursors (TEOS, APTES derivatives) on the oxide surface, resulting in the formation of coated NPs displaying carboxyl (<b>LNO@COOH</b>) or azide (<b>LNO@N₃</b>) reactive moieties. <b>LNO@COOH</b> NPs were further conjugated to an unnatural azido-containing small peptide to obtain silica-coated LNO NPs (<b>LNO@Talys</b>), displaying both azide and carboxyl moieties, which are well suited for biomedical applications due to the orthogonality of their surface functional groups, their colloidal stability in aqueous medium, and their anti-fouling properties.