Staff publications (MMD)

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Open Access
From raw data to monotonic and trendable features reflecting degradation trends in turbofan engines
(IEEE, 2024-12-08) Fuad, Mohd Fazril Irfan Ahmad; Khan, Samir; Erkoyuncu, John Ahmet
The performance of prognostic models relies heavily on the form and trend of the extracted features. However, the raw data collected from physical systems are inherently noisy, large in volume, and exhibit significant variability, which makes them unsuitable for direct use in prognostics. These characteristics poorly reflect the degradation behavior of physical systems and contribute to the uncertainty of prognostic outcome. Hence, transforming this data into relevant features and carefully selecting them is crucial for meeting the specific needs of prognostic models. This paper aims to address data processing challenges by focusing on extraction and selection of high-quality monotonic features which clearly reflect the degradation and can reduce prognostics uncertainty. The proposed framework comprises three main stages: Data pre-processing, feature extraction, and feature selection. It includes a fitness analysis to evaluate the monotonicity and trendability of features supplemented by visual inspections to identify relevant features. Applied to the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset from the NASA Ames Prognostics Data Repository, the framework reduces noise, improves feature monotonicity and trendability, and facilitates the selection of useful features - essential aspects for effective prognostic methods.
Open Access
Real-time prediction of wear morphology and coefficient of friction using acoustic signals and deep neural networks in a tribological system
(MDPI, 2025-06-01) Tian, Yang; Zheng, Bohao; Khan, Muhammad; Yang, Yifan
Predicting real-time wear depth distribution and the coefficient of friction (COF) in tribological systems is challenging due to the dynamic and complex nature of surface interactions, particularly influenced by surface roughness. Traditional methods, relying on post-test measurements or oversimplified assumptions, fail to capture this dynamic behavior, limiting their utility for real-time monitoring. To address this, we developed a deep neural network (DNN) model by integrating experimental tribological testing and finite element method (FEM) simulations, using acoustic signals for non-invasive, real-time analysis. Experiments with brass pins (UNS C38500) of varying surface roughness (240, 800, and 1200 grit) sliding against a 304 stainless steel disc provided data to validate the FEM model and train the DNN. The DNN model predicted wear morphology with accuracy comparable to FEM simulations but at a lower computational cost, and the COF with relative errors below 10% compared to experimental measurements. This approach enables real-time monitoring of wear and friction, offering significant benefits for predictive maintenance and operational efficiency in industrial applications.
Open Access
Optimization of printing parameters for self-lubricating polymeric materials fabricated via fused deposition modelling
(MDPI, 2025-05-02) Zhang, Peiyang; He, Feiyang; Khan, Muhammad
This study investigated the feasibility of fabricating self-lubrication material using fused deposition modelling (FDM) technology, focusing on the influence of printing parameters on tribological performance. Experiments were conducted using PA and ABS materials, with varying printing speed, infill density, and layer height across four levels. The research established regression equations and fitted curves to describe the relationship between printing parameters and the coefficient of friction (CoF). Validation experiments demonstrated the reliability of the models, with errors within 10%. The results indicate that reducing printing speed and increasing infill density enhance surface quality, with infill density exerting a more significant effect. The influence of layer height on surface quality depends on the printer characteristics, making precise quantification challenging. Additionally, this study confirms that resin-based samples produced via FDM exhibit self-lubricating potential. These findings contribute to the optimization of FDM-printed structures by balancing surface quality and tribological performance.
Open Access
AI-driven maintenance optimisation for natural gas liquid pumps in the oil and gas industry: a digital tool approach
(MDPI, 2025-05-01) Almuraia, Abdulmajeed; He, Feiyang; Khan, Muhammad
Natural Gas Liquid (NGL) pumps are critical assets in oil and gas operations, where unplanned failures can result in substantial production losses. Traditional maintenance approaches, often based on static schedules and expert judgement, are inadequate for optimising both availability and cost. This study proposes a novel Artificial Intelligence (AI)-based methodology and digital tool for optimising NGL pump maintenance using limited historical data and real-time sensor inputs. The approach combines dynamic reliability modelling, component condition assessment, and diagnostic logic within a unified framework. Component-specific maintenance intervals were computed using mean time between failures (MTBFs) estimation and remaining useful life (RUL) prediction based on vibration and leakage data, while fuzzy logic- and rule-based algorithms were employed for condition evaluation and failure diagnoses. The tool was implemented using Microsoft Excel Version 2406 and validated through a case study on pump G221 in a Saudi Aramco facility. The results show that the optimised maintenance routine reduced the total cost by approximately 80% compared to conventional individual scheduling, primarily by consolidating maintenance activities and reducing downtime. Additionally, a structured validation questionnaire completed by 15 industry professionals confirmed the methodology’s technical accuracy, practical usability, and relevance to industrial needs. Over 90% of the experts strongly agreed on the tool’s value in supporting AI-driven maintenance decision-making. The findings demonstrate that the proposed solution offers a practical, cost-effective, and scalable framework for the predictive maintenance of rotating equipment, especially in environments with limited sensory and operational data. It contributes both methodological innovation and validated industrial applicability to the field of maintenance optimisation.
Open Access
A reliability-oriented framework for the preservation of historical railway assets under regulatory and material uncertainty
(MDPI, 2025-05-01) Wailes, Thomas; Khan, Muhammad; He, Feiyang
Preserving historical railway assets presents a complex systems challenge, in which uncertainties in material performance, structural degradation, and regulatory requirements directly impact long-term reliability and operational continuity. Traditional maintenance practices often limit the use of modern materials, introducing inefficiencies, increased lifecycle costs, and higher failure risk due to material ageing and environmental exposure. This study proposes a reliability-informed preservation framework that supports the integration of contemporary materials into historical railway infrastructure while accounting for legal, material, and procedural uncertainties. The framework is validated through two industrial case studies, each reflecting different regulatory and operational constraints. The first case demonstrates the successful substitution of timber with certified PVC cladding on a non-listed signal box, achieving improved durability, reduced maintenance intervals, and enhanced system reliability. The second case explores an unsuccessful attempt to replace decayed timber gables with aluminium, in which late-stage planning misalignment, underestimated risks, and uncertainty in approval outcomes led to a significant cost increase and reduced reliability regarding delivery. By systematically applying and evaluating the framework under real-world conditions, this research contributes to engineering asset management by introducing a structured method for mitigating regulatory and material uncertainties.
Open Access
Virtual electroencephalogram acquisition: a review on electroencephalogram generative methods
(MDPI, 2025-05-02) You, Zhishui; Guo, Yuzhu; Zhang, Xiulei; Zhao, Yifan
Driven by the remarkable capabilities of machine learning, brain–computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward.
Open Access
Guest editorial: digitizing food supply chains: a path to ensuring food security
(Emerald, 2025-04-15) Jagtap, Sandeep; Trollman, Hana; Woolley, Elliot
Open Access
Navigating the landscape through digital human resource management: an initiative to achieve sustainable practices
(Elsevier, 2025-06) Virmani, Naveen; Sharma, Shikha; Kumar, Pranav; Luthra, Sunil; Jain, Vranda; Jagtap, Sandeep
The adoption of Digital Human Resource Management (DHRM) is increasing exponentially in the present market landscape; organizations are curious to digitalize human resource practices and enhance organizational performance. The purpose of the presented research is to assess various factors impacting DHRM adoption. The presented study is grounded in social exchange theory and dynamic capability theory. The survey instrument was developed using a literature review. Data from 269 respondents was collected, and Structural Equation Modeling was used to analyze the data. The research investigated the role of DHRM in achieving organizational performance. Furthermore, the role of sustainable Human Resource Practices (SHRP) is examined as a mediating variable. Also, the impact of Employee Engagement is analyzed on organizational performance. Moreover, the role of Management Support is analyzed to assess the relationship between DHRM, SHRP, and Employee Engagement (EENG). The result indicates that external environmental factors significantly impact DHRM practices. Also, SHRP partially mediates the association between DHRM and EENG. DHRM enables professionals to analyze real-time data, allowing managers to make informed decisions. In today's globalized scenario, the emergence of DHRM concept helps to automate and streamline Human Resource (HR) tasks, including performance management, recruitment, training, and appraisal, which eventually help to attain a sustainable competitive advantage. As found in existing literature, the DHRM domain is novel, and more empirical studies must investigate crucial aspects affecting DHRM adoption. Therefore, this study focuses on bridging the identified gaps.
Open Access
Microstructure tailoring of a wire-arc DED processed Ti6242 alloy for high damage tolerance performance
(Elsevier, 2025-05-05) Zakir, Farhana; Syed, Abdul Khadar; Zhang, Xiang; Davis, Alec E.; Sahu, Vivek K.; Caballero, Armando E.; Biswal, Romali; Prangnell, Philip B.; Williams, Stewart W.
This paper examines the effects of interpass hammer peening and post-process β annealing on the tensile properties, high-cycle fatigue, and fatigue crack growth behaviour of the titanium alloy Ti-6Al-2Sn-4Zr-2Mo-0.1Si (Ti6242), processed via wire-arc directed energy deposition (w-DED, also known as WAAM). A major challenge in additive manufacturing of titanium alloys is the development of a coarse columnar grain structure under standard build conditions, leading to significant anisotropy and variability in mechanical properties. This study demonstrates that interpass peening effectively refines the grain structure by inducing recrystallization, resulting in isotropic properties and increased strength without compromising fatigue crack growth resistance. Additionally, post-deposition annealing above the β-transus temperature (β annealing) significantly reduces the fatigue crack growth rate by an order of magnitude through microstructural refinement. The formation of coarse single-variant lamellar colonies promotes crack path branching and deviation, enhancing fatigue crack growth performance. Combining in-process grain refinement via peening with post-process β annealing further increases the threshold stress intensity factor by 2.5 times. These improvements provide substantial benefits for damage-tolerant design principles.
Open Access
Designing nickel coatings for water erosion performance: optimisation of grain size and thickness
(Elsevier, 2025-06-15) Gaddavalasa, Nithin Chandra; Lodh, Arijit; Cini, Andrea; Saaran, Vinodhen; Mehmanparast, Ali; Starr, Andrew; Castelluccio, Gustavo M.
Metallic coatings are gaining interest as an alternative to classical polymeric layers for erosion damage prevention due to their extended durability and sustainability. However, their implementation requires a thorough understanding of protective potential and reliability. This study explores the use of brush-plated nickel coatings on carbon-fibre reinforced composites to enhance their performance against water erosion. A combination of experimental analysis and computational modeling explores the effect of different coating thickness and properties to withstand water droplet erosion damage. Findings reveal a minimum critical coating thickness around 40 μ m can significantly improve the erosion resistance.
Open Access
High foot traffic power harvesting technologies and challenges: a review and possible sustainable solutions for Al-Haram Mosque
(MDPI, 2025-04-11) Alotibi, Fatimah; Khan, Muhammad
The growing global demand for sustainable energy solutions has led to increased interest in kinetic energy harvesting as a viable alternative to traditional power sources. High-foot-traffic environments, such as public spaces and religious sites, generate significant mechanical energy that often remains untapped. This study explores energy-harvesting technologies applicable to public areas with heavy foot traffic, focusing on Al-Haram Mosque in Saudi Arabia—one of the most densely populated religious sites in the world. The research investigates the potential of piezoelectric, triboelectric, and hybrid systems to convert pedestrian foot traffic into electrical energy, addressing challenges such as efficiency, durability, scalability, and integration with existing infrastructure. Piezoelectric materials, including PVDF and BaTiO3, effectively convert mechanical stress from footsteps into electricity, while triboelectric nanogenerators (TENGs) utilize contact electrification for lightweight, flexible energy capture. In addition, this study examines material innovations such as 3D-printed biomimetic structures, MXene-based composites (MXene is a two-dimensional material made from transition metal carbides, nitrides, and carbonitrides), and hybrid nanogenerators to improve the longevity and scalability of energy-harvesting systems in high-density footfall environments. Proposed applications for Al-Haram Mosque include energy-harvesting mats embedded with piezoelectric and triboelectric elements to power IoT devices, LED lighting, and environmental sensors. While challenges remain in material degradation, scalability, and cost, emerging hybrid systems and advanced composites present a promising pathway toward sustainable, self-powered infrastructure in large-scale, high-foot-traffic settings. These findings offer a transformative approach to energy sustainability, reducing reliance on traditional energy sources and contributing to Saudi Arabia’s Vision 2030 for renewable energy adoption.
Open Access
Analysis of key challenges to implementation of FEFO in perishable food supply chain
(Elsevier, 2025-06) Kandasamy, Jayakrishna; Vimal, K. E. K.; Singh, Aditya Pratap; Magnani, Aaryan; Gokhale, Ameya; Jagtap, Sandeep
Implementing FEFO practices has become essential for organizations globally to minimize spoilage, enhance inventory turnover, and ensure compliance with health and safety standards. To aid stakeholders in effectively adopting FEFO, it is crucial to identify and address the challenges involved in its implementation. Through an extensive literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology and insights from industry experts, this study identifies thirteen core challenges that hinder FEFO adoption. PRISMA methodology was used to systematically organize the existing literature for the purpose of this study. Using tools like Decision Making Trial and Evaluation Laboratory (DEMATEL) and Total Interpretive Structural Modelling (TISM), the challenges were examined and ranked according to their interdependencies, providing insights into the cause-effect relationships among them. After applying DEMATEL, an alpha threshold value of 0.368 revealed that challenges in effective storage management are the primary barrier in implementing FEFO practices. With level partitioning, this challenge emerged as the most significant, forming the foundation for a roadmap designed to assist stakeholders. The findings from this study offer managers actionable insights for implementing effective FEFO techniques within their organizations. The study's novelty lies in its combination of DEMATEL and TISM methodologies, along with a roadmap that highlights strategic and policy-focused recommendations to support efficient FEFO adoption and the systematic study of challenges preventing effective FEFO adoption. This paper aids implementation of FEFO for better inventory control and management, reduced wastage and greater efficiency. The paper also effectively outlines and analyses the order of importance of challenges in FEFO implementation and their interdependence.
Open Access
Applications of large language models and multimodal large models in autonomous driving: a comprehensive review
(MDPI, 2025-04-01) Li, Jing; Li, Jingyuan; Yang, Guo; Yang, Lie; Chi, Haozhuang; Yang, Lichao
The rapid development of large language models (LLMs) and multimodal large models (MLMs) has introduced transformative opportunities for autonomous driving systems. These advanced models provide robust support for the realization of more intelligent, safer, and efficient autonomous driving. In this paper, we present a systematic review on the integration of LLMs and MLMs in autonomous driving systems. First, we provide an overview of the evolution of LLMs and MLMs, along with a detailed analysis of the architecture of autonomous driving systems. Next, we explore the applications of LLMs and MLMs in key components such as perception, prediction, decision making, planning, multitask processing, and human–machine interaction. Additionally, this paper reviews the core technologies involved in integrating LLMs and MLMs with autonomous driving systems, including multimodal fusion, knowledge distillation, prompt engineering, and supervised fine tuning. Finally, we provide an in-depth analysis of the major challenges faced by autonomous driving systems powered by large models, offering new perspectives for future research. Compared to existing review articles, this paper not only systematically examines the specific applications of LLMs and MLMs in autonomous driving systems but also delves into the key technologies and potential challenges involved in their integration. By comprehensively organizing and analyzing the current literature, this review highlights the application potential of large models in autonomous driving and offers insights and recommendations for improving system safety and efficiency.
Open Access
Exploring circular economy in the United Kingdom based on LinkedIn data from company profiles
(Elsevier, 2025-04-25) Tsironis, Georgios; Cox, Rylan; Jolly, Mark; Salonitis, Konstantinos; Tsagarakis, Konstantinos P.
This work explores the landscape of Circular Economy within the business domain through an innovative approach to topic modelling applied to 1396 LinkedIn company profiles in the UK. We explore thematic structures within a dataset curated through the LinkedIn search engine prompt for companies related to the Circular Economy. Leveraging Latent Dirichlet Allocation models, we identify topics that encapsulate the essence of circular and sustainable business practices. Our findings unveil key thematic clusters, including “Waste Management and Environmental Impact,” highlighting companies at the forefront of waste reduction and eco-conscious industry practices. Another significant cluster, “Sustainable Solutions and Customer-Centric Approach,” delves into businesses seamlessly integrating sustainability across product design and customer interactions. Lastly, “Green Technology and Community Building” sheds light on companies excelling in green technology and actively contributing to environmentally responsible global networks. Topic modelling is employed as a powerful tool for unravelling complex business narratives and fostering a holistic approach to sustainable practices.
Open Access
Mapping research frontiers in gender and sustainability in agricultural development: a bibliometric review
(Springer, 2025-01-01) Kumari, Anshu; Tiwari, Manish; Mor, Rahul; Jagtap, Sandeep
Gender and sustainability are crucial in agriculture, which remains a significant source of global employment. However, urbanization, industrialization, and technological advancements have reshaped the sector, impacting labor dynamics and gender roles. Traditional agricultural labor faces challenges due to low wages, physically demanding tasks, and unfavorable working conditions. Addressing gender disparities and promoting inclusive work environments is essential for achieving sustainability. According to the ILO (International Labour Office) decent work encompasses productivity and equal employment opportunities for both genders. This study aims to review the literature on gender, sustainability and agricultural development using a bibliometric analysis of Scopus-indexed articles. The findings identify five main research domains: gender dynamics and roles, agriculture and climate change, sustainability and development, human and labor dynamics, and environmental and technological aspects. Additionally, four key scientific communities led the research: Gender studies, agricultural economics, environmental management, and rural sociology. Emerging research trends focus on gender roles in sustainable farming, environmental innovation, and labor governance in agriculture. Spain, the United Kingdom, United States, and Canada lead in knowledge production, contributing significantly to these research domains. This review highlights the importance of interdisciplinary approaches to address the complex issues of gender and sustainability in agriculture. It also specifies a target for expectations research, highlighting that the ILO’s definition of appropriate employment can guide efforts to improve gender equity and labor conditions, ultimately supporting sustainable development in the agricultural sector.
Open Access
Cyclic thermal treatment parameters of bagasse particle reinforced epoxy bio-composites for sustainable applications
(Springer, 2025-03-13) Oladele, Isiaka Oluwole; Falana, Samuel Olumide; Ilesanmi; Akinbamiyorin, Michael; Onuh, Linus Nnabuike; Taiwo, Anuoluwapo Samuel; Adelani, Samson Oluwagbenga; Olajesu, Olanrewaju Favor
The demand for sustainable, high-performance materials has led to increased interest in bio-based composites. However, optimizing the mechanical properties of such materials for engineering applications remains a challenge. This study addresses this gap by developing and characterizing an epoxy-based biocomposite reinforced with sugarcane bagasse particles, focusing on the influence of cyclic thermal treatment on its properties. The bagasse particles were chemically treated with 1 M NaOH to remove impurities, improve interfacial bonding with the epoxy matrix, and enhance the overall composite performance. The treated particles j were pulverized to 470 µm and incorporated into the epoxy matrix (0–20 wt%) using the hand layup method. The composites were divided into untreated and thermally treated groups, with the latter subjected to cyclic thermal treatment (100 °C for 3 h over 7 days). Mechanical, wear, and water absorption properties were evaluated, while fractured surface morphologies were analyzed using SEM. Results revealed that cyclic thermal treatment significantly enhanced the composites’ performance, with the 15 wt% heat-treated composite showing optimal properties: density of 1.102 g/cm3, flexural strength of 29.13 MPa, ultimate tensile strength of 103.50 MPa, impact strength of 3.49 kJ/m2, hardness of 64.70 HS, and wear indices of 0.034 mg. These findings demonstrate that alkali treatment and cyclic thermal treatment synergistically enhance the performance of bio-composites, making them suitable for diverse applications, including automotive, aerospace, and other engineering fields.
Open Access
What drives Generation Z to choose green apparel? Unraveling the impact of environmental knowledge, altruism and perceived innovativeness
(Taylor and Francis, 2025-01-01) Vishnoi, Sushant Kumar; Mathur, Smriti; Agarwal, Vaishali; Virmani, Naveen; Jagtap, Sandeep
This study proposes to determine the influence of ‘Environmental Knowledge’ (EK), ‘Altruism’ (Atr), ‘Consumer Confidence’ (CC) and constructs of ‘Theory of Planned Behaviour’ (TPB) like Attitude” (Atd), ‘Subjective Norm’ (Sub) and ‘Perceived behavioural control’ (Pbhc) on consumers’ intention to purchase ‘Green Apparel Products’ (GAPI). Moreover, the moderating effect of ‘Perceived Innovativeness’ (PInn) on the relationship between ‘Attitude’ (Atd), ‘Subjective Norm’ (Sub), ‘Perceived behavioural control’ (Pbhc), ‘EK’, ‘Atr’ and ‘CC’ was studied. To test the research model and hypothesis, a survey of 349 Generation Z consumers (18–26 years) was conducted. Cronbach’s alpha and a ‘Confirmatory Factor Analysis’ (CFA) were used to determine the scale’s reliability and validity. ‘Structural Equation Modelling’ (SEM) validated the given model and hypotheses. In this research, six hypotheses were tested, and it was found that three hypotheses showed a direct relationship. Specifically, the result of SEM showed that ‘Atd’, ‘Sub’ and ‘CC’ were positively related to GAPI. Also, six hypotheses were formulated testing the moderating role of ‘PInn’. The results established that ‘PInn’ moderated the relationship between ‘Atd’, ‘Sub’, ‘CC’ and ‘GAPI’ significantly. This research provides a novel framework to explore the relationship between the ‘EK’, ‘Atr’ and ‘CC’ and Generation Z consumer’s ‘GAPI’.
Open Access
ROSE+ : A robustness-optimized security scheme against cascading failures in multipath TCP under LDDoS attack streams
(IEEE, 2024-12-17) Nie, Jinquan; Ji, Lejun; Jiang, Yirui; Ma, Young; Cao, Yuanlong
Multipath TCP leverages parallel data transmission across multiple paths to improve transmission rates, reliability, and resource utilization. However, Multipath TCP faces severe network security and communication reliability challenges when exposed to low-rate distributed denial-of-service (LDDoS) attacks. In this paper, we propose a robustness optimization security scheme against cascading failures in Multipath TCP (ROSE+) to tackle the challenges posed by Low-rate Distributed Denial of Service (LDDoS) attacks on network security and communication reliability. The scheme integrates the intricate network load-capacity cascading failures model and leverages the unique characteristics of multipath TCP to facilitate the redistribution of load traffic at ineffectiveness nodes, thereby alleviating the cascading failures induced by LDDoS attack streams. Additionally, we optimize the robustness of communication transmission systems by devising a load-capacity cascading failures model. The experimental results demonstrate that the scheme reduces the probability of cascading failures by 20.07%. This research provides new ideas and methods to improve the robustness and destruction resistance of multipath TCP transmission.
Open Access
Multisensory design in memory research: the £1 coin case in the digital era
(IOS Press, 2025-03-31) Ji, Yijing; Lin, Qianqian; Liu, Zhenghong; Tran, Trung Hieu; Williams, Leon; Simon, Jude; Fan, Yilin
This study explores the effects of multisensory memory on memory for everyday objects, with a particular focus on memory for £1 coins. The study delves into the intersection of sensory anthropology, sensory history, and sensory sociology to examine how multisensory experiences affect memory persistence. The study used a dual-task paradigm and cross-modal stimuli to investigate the effectiveness of different sensory combinations in enhancing memory. Post-epidemic era, unlike offline experiences, this experiment utilised an online survey and a variety of media formats including text, images, video, audio and physical objects. The results showed that multisensory interactions significantly improved short-term memory recall over single-sensory modalities, while visual elements such as colours and shapes had a lasting effect on long-term memory. The study also highlights the potential of multisensory engagement in educational environments and museum experiences, gathering reliable data for future projects in which computers simulate human behaviour.
Open Access
In-situ monitoring the structural pathway of a Ti-based alloy from metallic liquid to metallic glass
(Elsevier, 2025-04-25) Georgarakis, Konstantinos; Stiehler, Martin E.; Hennet, Louis; Guo, Yaofeng; Antonowicz, Jerzy; Louzguine-Luzgin, Dmitri V.; Jolly, Mark R.; Andrieux, Jérôme; Vaughan, Gavin B. M.; Greer, A. Lindsay
A metallic glass is formed when a molten metallic alloy is cooled rapidly enough that crystallisation is avoided. However, the way the atomic structure of the liquid converts to that of the glass is generally unknown. The main challenge is the sufficiently fast experimental acquisition of structural data in the undercooled liquid regime necessitated by the high cooling rates needed to avoid crystallisation. In the present study, using aerodynamic levitation, the Ni-free Ti-based alloy Ti40Zr10Cu34Pd14Sn2 was vitrified in-situ in a high-energy synchrotron X-ray beam while diffraction data were acquired during cooling from above the liquidus temperature Tliq to well below the glass-transition temperature Tg. The structure in the undercooled liquid regime shows an accelerated evolution. Both the local order in the short (SRO) and medium range (MRO) increases rapidly as the undercooled liquid approaches Tg, below which the amorphous structure “freezes”. Nevertheless, distinct differences between the evolution of SRO and MRO were observed. The structural rearrangements in the undercooled liquid are found to be correlated with a rapid increase in viscosity of the metallic liquid upon cooling. The new findings shed light on the evolution of the atomic structure of metallic liquids during vitrification and the structural origins of the sluggish kinetics that suppress nucleation and growth of crystalline phases.

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