Rezwanul Haque

Lithium-ion batteries are pivotal in modern energy storage, driving advancements in consumer electronics, electric vehicles (EVs), and grid energy storage. This review explores the current state, challenges, and future trajectory of lithium-ion battery technology, emphasizing its role in addressing global energy demands and advancing sustainability. Despite achieving energy densities up to 300 Wh/kg, cycle lives exceeding 2000 cycles, and fast-charging capabilities, lithium-ion batteries face significant challenges, including safety risks, resource scarcity, and environmental impacts. Recycling inefficiencies and the need for sustainable material alternatives further underscore the urgency for innovation. This paper highlights recent breakthroughs in silicon-based anodes, solid-state electrolytes, and advanced cell designs, which promise to push energy densities beyond 400 Wh/kg and extend cycle lives to over 5000 cycles. Additionally, alternative battery technologies, such as solid-state, sodium-ion, and metal-air systems, are explored for their potential to complement or surpass lithium-ion batteries in specific applications. By bridging the gap between academic research and real-world implementation, this review underscores the critical role of lithium-ion batteries in achieving decarbonization, integrating renewable energy, and enhancing grid stability. Collaborative efforts among researchers, industry stakeholders, and policymakers are essential to overcoming these challenges and driving the transition to cleaner, more sustainable energy systems.

The introduction of Generative Artificial Intelligence (GenAI) tools, like ChatGPT, into higher education heralds a transformative era, reshaping instructional methods, enhancing student support systems, and redefining the educational landscape. Recent literature reviews on GenAI highlight a lack of focus on how these tools are being practically implemented in educational settings. Addressing this gap, the present study systematically examines empirical case studies that demonstrate the integration of GenAI into teaching and learning in higher education, offering actionable insights and guidance for academic practice.
We conducted a search of relevant databases and identified 21 empirical studies that met our inclusion criteria. The selected studies cover a diverse range of disciplines, locations, types of participants (from first-year students to postgraduates and academics), and a variety of methodologies. We classified the selected publications based on the pedagogic theory of Laurillard’s Conversational Framework (LCF) and the Substitution, Augmentation, Modification, and Redefinition (SAMR) framework. We also synthesized definitions from selected empirical studies and recent research exploring Technological Pedagogical Content Knowledge (TPACK) in the age of GenAI, providing a comprehensive understanding of GenAI-TPACK factors. Limitations and future research opportunities are also discussed. The paper concludes by providing a GenAI-TPACK diagram to guide educators in effectively incorporating GenAI tools into their teaching practices, ensuring responsible and impactful use in higher education.

The global plastic waste crisis requires innovative solutions, and pyrolysis has emerged as a promising technology. This review critically examines pyrolysis technologies for plastic waste management, focusing on their efficiency, economic potential, and environmental impact. Pyrolysis can convert 60 %–80 % of plastic waste into liquid fuels, with yields of up to 85 % in fast pyrolysis processes conducted at temperatures between 450 °C and 600 °C. The process also reduces greenhouse gas emissions by 40 %, mitigating 3.5 tons of CO2-equivalent per ton of plastic waste processed. Economically, pyrolysis oil can be sold for $600–$900 per ton, while syngas, with a market value of $200–$300 per ton, can generate up to 800 kWh of electricity per ton of waste. Challenges include high energy requirements and the need for more efficient catalysts, which could improve liquid fuel yields by an additional 15 %. Future research should prioritize developing cost-effective and durable catalysts, improving energy efficiency in large-scale reactors, and integrating renewable energy sources to further enhance sustainability. Additionally, supportive policies and market strategies are crucial for enabling large-scale adoption of pyrolysis technologies.

Since the start of 2023, Chat GPT, and the use of generative AI (Gen-AI) more generally, has been the topic of much discussion, advice and debate within engineering education worldwide. Despite a proliferation of guidance, awareness raising and information, there has been little empirical evidence pertaining to the impact of Gen-AI on integrity of assessment and risk of plagiarism, something which has led to confusion and duplication of work.

In this episode we speak to Sasha Nikolic (University of Wollongong), Scott Daniel (University of Technology, Sydney), and Rezwanul Haque (University of the Sunshine Coast) from the Australasian Artificial Intelligence in Engineering Education Centre (AAIEEC) Special Interest Group of the Australasian Association for Engineering Education (AAEE), who, along with other Australian engineering educators, came together to answer questions about how ChatGPT and other Gen-AI tools may affect engineering education assessment methods, and how it might be used to facilitate learning.

ChatGPT, a sophisticated online chatbot, sent shockwaves through many sectors once reports filtered through that it could pass exams. In higher education, it has raised many questions about the authenticity of assessment and challenges in detecting plagiarism. Amongst the resulting frenetic hubbub, hints of potential opportunities in how ChatGPT could support learning and the development of critical thinking have also emerged. In this paper, we examine how ChatGPT may affect assessment in engineering education by exploring ChatGPT responses to existing assessment prompts from ten subjects across seven Australian universities. We explore the strengths and weaknesses of current assessment practice and discuss opportunities on how ChatGPT can be used to facilitate learning. As artificial intelligence is rapidly improving, this analysis sets a benchmark for ChatGPT’s performance as of early 2023 in responding to engineering education assessment prompts. ChatGPT did pass some subjects and excelled with some assessment types. Findings suggest that changes in current practice are needed, as typically with little modification to the input prompts, ChatGPT could generate passable responses to many of the assessments, and it is only going to get better as future versions are trained on larger data sets.

Biodiesel will provide a significant renewable energy source for transportation in the near future. In the present study, principal component analysis (PCA) has been used to understand the relationship between important properties of biodiesel and its chemical composition. Finally, several artificial intelligence-based models were developed to predict specific biodiesel properties based on their chemical composition. The experimental study was conducted in order to generate training data for the artificial neural network (ANN). Available (experimental) data from the literature was also employed for this modeling strategy. The analytical part of this study found a complex multi-dimensional correlation between chemical composition and biodiesel properties. Average numbers of double bonds in the chemical structure (representing the unsaturated component in biodiesel) and the poly-unsaturated component in biodiesel had a great impact on biodiesel properties. The simulation result in this study demonstrated that ANN is a useful tool for investigating the fuel properties from its chemical composition which eventually can replace the time consuming and costly experimental test.