Chemical and biomolecular engineering internships

Supervisor: Dr Fengwang Li

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

Ammonia is an essential feedstock for global food production, chemical manufacturing, and future energy systems – yet its conventional synthesis using the Haber-Bosch process remains heavily fossil-fuel dependent, contributing towards 2-3 % of global CO2 emissions annually (~620 Mt CO2).

This project will explore a novel electrocatalytic reactor recently developed in our lab, where renewable electricity can be harnessed to produce green ammonia directly from reactive nitrogen and water under ambient conditions.

Over the summer, the student is expected to gain a deep understanding of both the fundamental science and applied engineering of this emerging technology. They will develop core laboratory skills including electrode fabrication, nanomaterials synthesis, reactor construction/operation, and product quantification and analysis.

By integrating concepts from electrochemistry, reactor engineering, and materials science, the student will actively contribute towards advancing a sustainable pathway for one of the world’s most important chemicals.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisor: Prof Tim Langrish

Eligibility: 2nd or 3rd year Chemical, Mechanical or Civil Engineering and must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

The key theme of the advanced food engineering pilot plant is the creation of an integrated, university-designed, leading-edge, set of food engineering unit operations for pilot-scale production of innovative food products (1-10 kg/h). We currently have an advanced design for spray and fluidised-bed dryers, and the aim of this project is to operate and optimise a semi-batch solid-liquid extraction system for the extraction of valuable nutrients, in liquid form, from solid fruit and vegetable wastes. This extraction system will feed a solution of valuable soluble solids, with a high antioxidant and nutritional content, into the spray dryer and will be located close to the spray dryer.

The specific aim will be to maximise the concentration of soluble solids extracted from orange peels into water at a range of water flow rates to match the ranges of water flow rates for the downstream spray dryer.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisor: Dr. Aditya Putranto

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

Aligned with the pressing issues on climate change, global warming, resource depletion and biodiversity loss, engineering students should be prepared to be graduate-ready to tackle these challenges. Therefore, engineering curriculum should be designed to articulate and accommodate sustainability aspects in effective ways. In view of the emergence of computing tools, process modelling and simulation is deemed as an appropriate way to embed sustainability aspects in engineering teaching. The modelling tools have capability to simulate the existing processes/activities as well as quantify the resources requirements, energy demands, and pollutant emissions.

Based on simulations, cost effective scenarios to reduce energy demands and improve environmental aspects can be made. It can be used to explore multiple sustainability scenarios. Based on the currently available data that have been collected, this project seeks to evaluate to what extent modelling-and simulation- based study is effective to enhance students’ understanding on sustainability and write a research article subsequently.

Requirement to be on campus: No.  Work will be conducted remotely and communications will be done through Zoom.  

Supervisors: Prof. Yuan Chen, Dr. Fangzhou Liu

Eligibility: Complete year 1 and 2 Chemical Engineering/Chemistry-related courses and must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

This research project focuses on the electrochemical expansion of graphite to achieve controlled interlayer spacing, creating a versatile carbon material with enhanced structural and surface properties. By precisely regulating the electrochemical conditions, the expanded graphite will exhibit tunable interlayer distances, making it a promising substrate for multiple advanced applications, including high-performance battery electrodes and durable carbon supports for hydrogen fuel cell catalysts.

Participants will

• Investigate the relationship between electrochemical parameters and the degree of graphite expansion.
• Characterize the resulting carbon structures using X-ray diffraction, Raman spectroscopy, and nitrogen physisorption surface analysis.
• Evaluate the suitability of the expanded graphite in energy-related applications through preliminary electrochemical and structural assessments

This project provides hands-on experience in materials synthesis, electrochemistry, and energy materials research, while exploring the potential of expanded graphite as a next-generation carbon substrate.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisors: Dr David Wang, Mr Jin Kwei Koh

Eligibility: Research interests: absorption and chemical organic synthesis and must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

Micropollutants are a common concern in industrial effluents, particularly in sectors such as textiles, pharmaceuticals, and food processing, where dyes, organic compounds, and other residues are often released into water streams. Conventional treatment methods, including adsorption, microfiltration, and ion exchange, have been widely applied for pollutant removal. However, these approaches suffer from limitations such as incomplete removal, high operational costs, generation of secondary waste, and reduced efficiency at low pollutant concentrations. In recent years, advanced oxidation processes (AOPs) have emerged as promising alternatives due to their ability to generate highly reactive species capable of degrading complex organic contaminants into harmless by-products.

In this study, methylene blue will be selected as the model micropollutant to investigate degradation performance using AOP. To enhance this process, you will be involved in synthesising, testing, and characterizing a series of catalyst, as catalysts play a crucial role in accelerating reactive species formation, improving efficiency, and ensuring sustainable water treatment applications.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisors:  Dr David Wang, Shakila Akter

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

In our previous research, we successfully synthesized Phosphonium Amino Acid Ionic Liquids (PAAILs) as absorbents with enhanced CO₂ capture capacity. Building on this, our current project investigates polymer–IL hybrid membranes to integrate the CO₂ affinity of ILs with the structural and processing advantages of polymers.

In this project, we will blend PAAILs (1–5 wt%) with polyetherimide (PEI) or polyvinylidene fluoride (PVDF) in N-methyl-2-pyrrolidone (NMP) solution to fabricate coated Al₂O₃ tubular membranes. These polymers are chosen for their compatibility with ILs: PEI, rich in amine groups, promotes chemical interaction with CO₂, while PVDF provides excellent mechanical strength and thermal stability. The coated membranes will be tested using the constant volume and pressure method to evaluate their CO₂ absorption enhancement and interaction mechanisms.  This work aims to advance hybrid membrane technologies for sustainable carbon capture, offering both chemical efficiency and structural durability for practical applications in membrane technology.

Requirement to be on campus: Yes *dependent on government’s health advice.

• Supervisors:  Dr. Gobinath Rajarathnam, Dr. Peter Lok

Eligibility:

• Highly motivated and self-driven learner.
• Enrolled in a Chemical Engineering program.
• Basic understanding of AI and machine learning concepts (preferred).
• Demonstrated strong analytical, research, and problem-solving skills.
• Familiarity with core CHNG units of study including CHNG1108, CHNG2803, CHNG2806.
• In the event more than 1 student is selected, there is option to similarly train AI Study Bots for combination of aforementioned courses
• Must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

This project aims to enhance student learning in core CHNG units of study by training an AI-powered study agent using Cogniti. The AI assistant will support students by:

• Re-explaining core concepts in an interactive manner
• Assisting with quiz preparation
• Explaining answers to tutorials
• Offering personalized study guidance based on student progress

There are already multiple bespoke bots we’ve created – you will be improving our current platform and there’s potential to create new ones too.

The selected engineer will refine existing AI models by curating and structuring relevant course materials, designing response models for key topics, and testing the bot’s accuracy and effectiveness. By the end of the project, the AI agent will be fine-tuned to assist students effectively in preparation for assessments. This internship offers hands-on experience in AI training, educational technology, and chemical engineering education.

Project Timeline (6-8 Weeks):

End-of-Program Output:

• A functional Cogniti-based AI study agent for each CHNG course, integrated into Canvas.
• A functional Cogniti-based AI study agent for each CHNG course, integrated into Canvas.
• A final write-up detailing AI improvements and training methodologies: this will be intended to be used towards a high-quality journal publication, logistics will be discussed at project kick-off.
• A short presentation on the project at the internship’s closing event

Requirement to be on campus: No.  Work can be conducted remotely, with optional on-campus testing sessions. We will be in constant contact in-person and/or via MS Teams, with expected 1-3 weekly meetings (tech industry stand-up style, rapid).

Supervisors: Dr Gobinath Rajarathnam, Dr Peter Lok, Dr Yeow-Tong Chia

Eligibility:

•  Highly motivated and self-driven learner.
• Enrolled in a Chemical Engineering program or related Engineering where similar content covered (e.g. Heat transfer).
• Basic understanding of AI and machine learning concepts (preferred).
• Demonstrated strong analytical, research, and problem-solving skills.
• Familiarity with another field of study, e.g. Law or Commerce, is strongly recommended for this.
• Must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

This project aims to develop and train an interdisciplinary AI-powered design agent using Cogniti. The AI assistant will be

• Expected to design a simple chemical engineering unit operation and/or small plant section.
• Be trained in elements of Standards, Law, Ethics, Philosophy and/or History.

The selected engineer will curate and structure relevant CBE design materials, while designing response models for key topics, and testing the bot’s accuracy and effectiveness. By the end of the project, the AI agent will be fine-tuned to assist engineers effectively in preparation for design informed by other fields (e.g. Law). This internship offers hands-on experience in AI training, leveraging interdisciplinary knowledge for practical design delivery.

Project Timeline (6-8 Weeks):

End-of-Program Output:

• A functional Cogniti-based AI study agent integrated into a sandbox webpage, e.g. Canvas.
• A final write-up detailing AI improvements and training methodologies: this will be intended to be used towards a high-quality journal publication, logistics will be discussed at project kick-off.
• A short presentation on the project at the internship’s closing event.

Requirement to be on campus: No.  Work can be conducted remotely, with optional on-campus testing sessions. We will be in constant contact in-person and/or via MS Teams, with expected 1-3 weekly meetings (tech industry stand-up style, rapid).

Supervisors: Dr Gobinath Rajarathnam, Rafael Franca Dutra

Eligibility:

• Highly motivated and self-driven learner.
• Enrolled in a Chemical Engineering program or related Engineering where similar content covered (e.g. Heat transfer)
• Demonstrated understanding of content curation, and website creation and maintenance (preferred).
• Demonstrated strong analytical and problem-solving skills.
• Familiarity with AI Bots, video teaching materials, etc., is preferred for this project.
• Must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

This project aims to develop a suite of teaching materials aimed at a School-first living “Digital Textbook”:

• Curating video guides for ASPEN modelling.
• Collating existing video demonstrations/guides on Teaching Lab equipment.
• Portfolio showcase of student-developed Augmented-Reality (AR) teaching guides.
• Organising AI Study and Support Agents for various units of study.

The selected engineer will curate and structure relevant CBE teaching materials, namely those developed under supervision/initiative of Dr Gobinath Rajarathnam for various courses including CHNG1108, CHNG2803, CHNG2806, CHNG5603. By the end of the project, the intern would have created an easily accessible and well-organised, scalable, digital textbook intended for use in Engineering Education both by the School of CBE at USyd and possibly beyond. This internship offers hands-on experience in managing educational content, leveraging access to range of tools from AI to AR and practical delivery.

Project Timeline (6-8 Weeks):

End-of-Program Output:

• A functional integrated into a sandbox webpage, e.g. Canvas, with collated materials.
• A final write-up detailing development methodology and learnings: this will be intended to be used towards a high-quality journal publication, logistics will be discussed at project kick-off.
• A short presentation on the project at the internship’s closing event.

Requirement to be on campus: No.  Work can be conducted remotely, with optional on-campus testing sessions. We will be in constant contact in-person and/or via MS Teams, with expected 1-3 weekly meetings (tech industry stand-up style, rapid).

Supervisor: Dr Gobinath Rajarathnam

Eligibility:

• Highly motivated and self-driven learner.
• Enrolled in a Chemical Engineering program or related Engineering where similar content covered (e.g. techno-economic analysis).
• Basic understanding of AI and machine learning concepts, ESG scoring, UN SDGs mapping (preferred).
• Demonstrated strong analytical, research, and problem-solving skills.
• Familiarity with another field of study, e.g. Social Science or Economics, is helpful for this project.
• Must have already completed at least 72 credit points towards their undergradudate degree at the time of application.

Project Description:

This project aims to explore a range of chemical engineering products including food (e.g. milk powder), drugs (pharmaceuticals), etc, via deep Techno-Socio-Economic analysis (TESA) via Environmental, Social, Governance (ESG) Lens.

• Software (e.g. OpenLCA, SimaPro) or bespoke coding options possible.
• Delving into ESG metrics and scoring methodology, building upon existing knowledge base and proposing amendments/additions.
• Exploring methods to leverage AI in both the above streams.

The selected engineer will combine the above streams to shape the future of CBE design by focussing on the social element (i.e. People). By the end of the project, the results of analysis will assist engineers in various fields to effectively pursue better-informed design decisions which influence communities, cities, countries and entire continents. This internship offers hands-on experience in AI integration, leveraging interdisciplinary knowledge for practical design delivery related to TESA and ESG.

Project Timeline (6-8 Weeks):

End-of-Program Output:

• A functional suite of technical tools and documentation for leveraging AI in TESA-ESG analyses.
• A final write-up detailing specific development methodologies and learning points: this will be intended to be used towards a high-quality journal publication, logistics will be discussed at project kick-off.
• A short presentation on the project at the internship’s closing event

Requirement to be on campus: No.  Work can be conducted remotely, with optional on-campus testing sessions. We will be in constant contact in-person and/or via MS Teams, with expected 1-3 weekly meetings (tech industry stand-up style, rapid).

Supervisors: Dr. Syamak Farajikhah, Prof. Fariba Dehghani, Dr Jacopo Giaretta

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application.

Project Description:

This project aims to develop a cost-effective biosensor platform tailored for biomedical applications, with a strong focus on point-of-care (POC) diagnostics. The proposed technology integrates advanced materials and miniaturised electronics to create a portable, user-friendly device capable of rapid and accurate detection of clinically relevant biomarkers. By leveraging functionalised electrodes and biorecognition molecules, the biosensor will offer high sensitivity and specificity while remaining affordable and scalable. The device is designed for ease of use, requiring minimal sample preparation and no specialised training, making it ideal for deployment in remote, resource-limited, or home settings. It will also enable real-time analysis, reducing reliance on centralised laboratories.

This innovation has the potential to transform healthcare delivery by enabling early diagnosis, personalised treatment, and improved patient outcomes, while also supporting broader accessibility to diagnostic tools. The candidate will acquire various skills in electrochemistry, engineering, additive manufacturing, and various analytical techniques.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisors: Dr Jacopo Giaretta and Dr Aeryne Lee

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 72 credit points towards their undergraduate degree at the time of application

Project Description:

Current heart valve (HV) prosthetics are not monitored remotely after implantation. This causes discomfort and possibly pain in patients when the HV starts deteriorating and losing functionality before failing.

In this project, the intern will develop a smart polymeric HV replacement, which includes conductive patterns on the leaflets (the flaps that open and close to promote unidirectional flow) to monitor their movement. The location of these patterns will be based on fluid-dynamic simulations and the electrical variation will be used to determine the state of the valve.

The intern will join a passionate and multidisciplinary team in a project that bridges engineering and medical practices to create a novel real-world solution for HV disease patients.

The intern will gain invaluable experience and skills in advanced manufacturing (dip coating, 3D printing), sensors, mechanical characterisation, and using a pulse duplicator (a machine which emulates the function of the heart).

Requirement to be on campus: Yes *dependent on government’s health advice.