Winner - South Africa

Sebasa Theresa Ramahlare

Sebasa is from Mohodi ga Manthata, a village in the Limpopo province, South Africa. She has a BSc in Chemical Engineering from the University of Witwatersrand and is currently working at the Council for Scientific and Industrial Research (CSIR) in the Pretoria Gauteng province. She is on a 3-year graduate-in-training programme in the Advanced Polymer and Composites (APC) group in the Chemicals cluster, currently working on a project titled 'Biodegradable films: Potential Environmentally Friendly Alternative for Agricultural Mulching'. 

Sebasa hopes to pursue my postgraduate MSc in Chemical Engineering in 2025.

Biodegradable Mulch Biofilms Potential Environmentally Friendly Alternative for Agricultural Mulching

Plastic mulching materials are used in agriculture, providing advantages to crop production. Conventional plastic mulch presents challenges with disposal, increasing pollution, and breakdown into microplastics, leading to ecological problems. The current materials are synthetic and nondegradable, raising environmental concerns. This work was conducted to develop biobased plastic materials from biopolymers and biomass waste residues. Local biomass was used to modify biodegradable polymers to tailor biodegradability to suit the mulching of different lifecycles.

The physical and chemical properties of the materials were studied. The optimized mulch plastics were taken for laboratory and agricultural field trials to test the value proposition and practicality of use. The results revealed good properties comparable to conventional nonbiodegradable mulch plastics, with the added advantage of 100% biodegradability. These offer a sustainable alternative to traditional plastic mulch, which reduces plastic pollution.

2nd Place - FEMS

Oliver Marian Preuß

Oliver studied Materials Science at the Technical University of Darmstadt, Germany. After investigating the mechanical properties of diamond coatings during his Bachelor's thesis, he did his Master's thesis in the field of dislocations in ceramics in 2021, under the supervision of Dr Xufei Fang. Continuing this topic, he started a PhD on dislocation-based toughening in ceramics co-supervised by Prof Dr Jürgen Rödel and Dr Xufei Fang.

Oliver attended 3 international (incl. USA and Japan) and 5 national conferences and was the winner of the 2024 FEMS Master Thesis Award. His first paper achieved 'Editor’s Choice' by the American Ceramic Society and he also won three prizes in the ceramographic competition of the American Ceramic Society, including the Roland B Snow award for best-of-show. In his free-time, he practices luthiery and builds his own guitars.

Dislocation-Based Toughening and Damage-Tolerance in Oxide Ceramics

The growing research interest in dislocation tuned functionality in ceramics is evident, with the most recent proofs of concept for enhanced ferroelectric properties, electrical conductivity, and superconductivity via dislocations. In this study, we focus on dislocation-tuned mechanical properties and demonstrate that, by engineering high dislocation densities (up to 1014 per m2) into KNbO3 at room temperature, the fracture toughness can be increased by a factor of 2.8 compared to the reference.

A strong interaction between the dislocations and the ferroelectric domains was found, whose effect on the toughness is ruled out by a high-temperature indentation experiment. By an improved deformation technique, the dislocation density in MgO can reach over 1015 per m2, which leads to full crack suppression.

3rd Place - Malaysia

Jerome Liew

Jerome Liew is a PhD candidate at Universiti Malaya (UM) studying Doctor of Philosophy in Physical Science. He is zealous in his research in energy storage devices and has participated in numerous exhibitions, competitions, and conferences with awards. He won top 3 in Faculty of Science 3-Minute Thesis at UM twice consecutively, and actively took part in International Invention, Innovation & Technology Exhibition (ITEX’23 & ITEX’24).

He and his group won a Gold Award and a special award in ;Technology Reversing Climate Change' in ITEX’23. Recently, he received a scholarship to attend Green Chemistry Summer School in Venice, Italy. He aspires to make technological advancements in the field of energy storage devices and desires to bring breakthroughs in electrified vehicles in the advent of Electric Vehicles.

MXene: Pursuing Fast-Charging Battery

The presentation exploits the properties of MXene in its incorporation with silicon-based anodes. Various MXene synthesis methods were examined for the best MXene without the direct usage of hydrofluoric acid. Silicon anode faces the issue of massive volumetric changes during charging and discharging that cause its pulverisation, limiting its practical applications.

By electrostatic assembly of MXene and silicon and the coating of the composite thereof, the exorbitant aspect ratio of MXene provides extra active sites for redox reaction, and the 3D matrix formed by MXene establishes electronic transport pathways and promotes carrier transfer, increasing rate performance. The matrix also provides mechanical integrity to the composite, confining silicon nanoparticles from pulverising, and improving the longevity of silicon anodes. This concurrently addresses the restacking phenomenon of MXene due to the intercalation of silicon nanoparticles, demonstrating the synergistic effects of the composite.

UK

Emilia Russell

 Emilia Russell is a 2^nd year PhD student in the Department of Engineering at Durham University, currently investigating the impacts of mechanical strain on the electrical properties of 2-dimensional semiconductors. In her free time, she enjoys sailing and organising seminars from industry speakers with the Women’s Engineering Society at Durham.

Why strained semiconductors are like crinkle-cut crisps

When applying strain to 2-dimensional transition metal dichalcogenides, it has been observed that the bandgap changes. The bandgap is the potential difference between the highest energy bounded (immobile) electrons and the delocalised (mobile) electrons.

Crinkle cut crisps taste better because they hold more salt, crinkle cut semiconductors insulate better because they hold more electrons.

This effect could be exploited in the next generation of synthetic skin. It could also be used to design flexible acetylene detectors to monitor food expiration, reducing food waste. To date, the relationship between mechanical strain and electronic properties has predominantly been shown through computational modelling. This research focuses on overcoming the issues that have hindered the realisation of these devices, such that the effects of strain on other semiconductor properties can be investigated. In turn, this will lead to a better understanding of the electrostatics and charge transport of these novel 2-D materials.

Hong Kong

Sihao Yu

Sihao is a 2nd year PhD student in the Department of Earth Sciences at the University of Hong Kong, under the supervision of Prof Louis Wong. He earned his BEng (2020) in Civil Engineering from Southeast University, China, followed by an MPhil (2023) in Geotechnical Engineering from Tongji University, China. His current research focuses on the application of artificial intelligence in geotechnical engineering practice, particularly advanced computer vision techniques.

In his spare time, Sihao enjoys outdoor activities like hiking and ball games

A deep learning-based framework for automatic rock core logging

Rock core logging plays a crucial role in obtaining geological and geotechnical information such as lithology and Rock Quality Designation (RQD). However, traditional rock core logging procedures are often tedious and time-consuming, especially for highly fractured rock cores. Have you ever imagined that such domain-professional work could be aided by artificial intelligence? Prof Louis Wong and his research team at HKU have developed an autonomous framework for performing core logging that mimics human processes.

This presentation will showcase ongoing research to further improve automatic lithology classification and RQD estimation. A deep learning-based framework has been trained to classify rock cores into different categories. Moreover, this framework can also segment cores at a high accuracy, allowing the subsequent automatic RQD calculation. Upon further verification, the plan is to update the framework and integrate it into a publicly accessible website for widespread use.