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A*STAR scholar Edward Neo brings together chemistry and machine learning to improve plastic recycling.

© A*STAR Research

Closing the recycling loop with chemistry

30 Sep 2022

A*STAR scholar Edward Neo brings together chemistry and machine learning to improve plastic recycling.

From containers and clothes to furniture and even public transport—plastic is seemingly irreplaceable in our lives. Invented less than 150 years ago, it has become so much a part of the modern era that some scientists have declared this period as ‘The Plastic Age’.

Unfortunately, it is also because of its very omnipresence that plastic waste is piling up at an alarming rate. In fact, according to the Organization for Economic Cooperation and Development’s (OECD) Global Plastics Outlook, worldwide plastic waste generation more than doubled from 2000 to 2019 to reach a whopping 353 million tonnes. Of that amount, just nine percent is recycled.

As the world looks to alleviate the dire consequences of plastic waste with ambitious international treaties and goals, governments and communities all over the world ramp up plastic recycling efforts. This mission is echoed in Singapore too, with many initiatives, like litter clean-up campaigns and plastic recycling management, in place to tackle plastic waste sustainably.

While community programmes and well-placed recycling bins can contribute to the cause, a bigger issue lies in the difficulties of recycling plastic because of the various plastic types and differences in plastic properties. In general, recyclable plastics are ideally non-contaminated and consist of a single plastic type. But plastic recycling is complicated because most plastics are dirtied and used in combination with other materials.

Fascinated by the boundless potential artificial intelligence (AI) technology has to offer, Edward Neo, an A*STAR University of Warwick Engineering Doctorate Partnership (AWP) scholar, explores how AI can be partnered with chemistry to enhance current plastic recycling efforts. He hopes that his work analysing chemical data to determine plastic quality will be able to improve plastic recycling.

Driven by his passion for sustainable waste management, Neo shares how his research can contribute to Singapore’s sustainability goals and his hopes for the future of plastic waste management.

1. What sparked your interest in chemometrics?

As an undergraduate, I picked up programming and was deeply enthralled by the possibilities it has to offer. During my research attachment at A*STAR’s Singapore Institute of Manufacturing Technology (SIMTech), I further honed my programming knowledge when I worked on developing automated waste sorting bins. This project involved the use of AI image recognition technology to power the sorting process. While my job scope was more focused on software development, I had the chance to learn how to develop and implement an AI system. I was curious and wondered if the same AI technology could be applied to chemical data—this eventually led me to chemometrics.

2. Why did you choose to apply for the AWP scholarship?

The AWP scholarship is a doctorate programme that develops leaders in an industrial setting. I applied for the programme as it was geared toward translational research and offered modules that help equip me with the soft skills required to potentially translate research into the market.

3. Could you share with us about your passion for plastic recycling?

As an eco-conscious person, one of my primary motivations is driving sustainability. Initially, I was not clear about the specific research topic of interest. Hence, I spent my undergraduate days exploring different sustainability-related projects.

Eventually, I realised that waste management was an environmental issue that I felt most strongly about. Even in social settings, I often encourage my friends to move away from single-use plastic products. Plastics also happen to be polymeric materials and working on plastic recycling ties in nicely with my chemistry background and interest.

4. What was your greatest takeaway from your most recent project?

Chemometrics is an interdisciplinary research topic that lies at the intersection of chemistry and machine learning. I am thankful to have supervisors from both domains who support my work. As such, I ensure that neither discipline ends up getting lost during research discussions. With that, my greatest takeaway is consistent scientific communication—conveying concepts to people who are not as familiar with the subject area.

5. How has your background in chemistry supported your work in plastic recycling?

As the popular saying goes, “A machine learning model is only as good as the data it is fed.” Indeed, a large part of any AI-driven research is the data rather than the neural network model itself. Since my project primarily deals with chemical data, my background in chemistry has facilitated the collection of good quality chemical data using equipment I am trained to use, such as the infrared and Raman spectrometer. I am also better able to identify potential issues in the data that might be interfering with my AI models.

Additionally, with plastics being the main research subject, my background allows me to understand the properties of different types of plastic concerning its chemical structure and why certain recycling methods may or may not be suitable for different types of plastics.

6. What motivated your recent project on the emissions from mask production?

This research topic was relatively new at the time and as such, there were gaps in knowledge regarding mask emissions. I embarked on this project to quantify the environmental benefits of cloth masks over single-use masks in conjunction with A*STAR’s involvement in producing pioneer batches of cloth masks with embedded filtration layers.

7. Could you describe a current project you are working on?

I am currently studying how accelerated weathering can affect the spectra collected from plastic samples, and whether chemometrics can be used as a tool to distinguish such differences. The data I collect will hopefully provide useful information about the quality of plastic before recycling is done.

8. How does your research align with Singapore’s sustainability goals?

By 2030, Singapore aims to reduce waste sent to landfill by 30 percent, as outlined in the Singapore Green Plan. The effort is in hopes of extending the lifespan of our only offshore landfill—Pulau Semakau. One major contributor is plastic waste, with a six percent recycling rate in 2021. I believe improving the plastic recycling rate could help significantly reduce the amount of waste sent to the landfill.

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This article was made for A*STAR Research by Wildtype Media Group