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Non-invasive Bioelectronic Devices for Diabetes Management
No full textDiabetes is an ancient disease that has been longstanding in human history for thousands of years. It was named and described of symptoms in the 2nd century AD during the Greco-Roman era. Diabetes is characterized by impaired utilization of glucose, which is the primary cellular energy source. Poor management of diabetes can result in various complications and even death. Centuries of research and exploration have enriched the understanding of diabetes, but the exact cause has not yet been fully uncovered. To date, diabetes patients require lifelong management through careful monitoring of blood glucose levels and preventing diabetic complications. Biosensing techniques are vital for monitoring in diabetes management, the representative one is glucose sensing. However, current commercially available glucose meters are still invasive, painful, and can damage skin tissues, posing risks of infection. The pursuit of non-invasive sensing technologies for glucose is crucial to enhance the comfort of long-term diabetes management. Additionally, sensing techniques for other biomarkers have been applied in diagnosing diabetic complications, but these tests are only available at centralized facilities such as hospitals and laboratories. My doctoral research focuses on exploring non-invasive biosensing technologies for applications in diabetes management. This research encompasses three main projects covering four different biomarkers related to various aspects of diabetes. The first project is the optical sensing technique for glucose. Utilizing glucose absorbance of near-infrared (NIR) light, the optical sensing approach can quantitatively detect glucose concentrations. Optical sensing technique is inherently non-invasive but faces challenges from water absorption of NIR light. In this work, the spectroscopic analysis identified four glucose absorption peaks counter with water interference: 1605, 1706, 2145 and 2275 nm. Furthermore, a miniaturized glucose optical sensor was fabricated using 1600-1700 nm light, which was decided by the most prominent peaks at 1605 and 1706 nm. The device successfully detects glucose in aqueous solutions within the physiological range of 50-400 mg/dL, attaining a limit of detection (LOD) as low as 10 mg/dL. This work provides a foundational design for future non-invasive glucose sensing, by offering functional light wavelengths and a simplified optical detection system.The second project focuses on an inflammatory biomarker, Tumor Necrosis Factor-alpha (TNF-alpha). This biomarker has been confirmed by clinical research for its significance in monitoring diabetic complications and its association with insulin resistance, one cause for type 2 diabetes. Inspired by home-use blood glucose meters and the widely used COVID saliva test kits during the pandemic, the second project proposes a conductometric sensor measuring TNF-alpha levels in saliva. This sensor enables rapid detection, easily accessible resistance signals and conversion to TNF-alpha concentration. The fabricated TNF-alpha sensor has a broad detection range covering levels from 10 to 3000 femtomolar (fM) with a low LOD of 10 fM. Additionally, a Bluetooth-integrated microcontroller successfully measured the resistance of a TNF-alpha sensor and observed the readings on a smart phone. This approach of TNF alpha sensing and reading forms an initial prototype for transforming conventional time consuming and laboratory-based tests. With further research on TNF-alpha and diabetes, this self monitoring method holds the potential to be used together with blood glucose meters in future. It can evaluate diabetic complications, assessing insulin resistance, and optimizing diabetes treatment plans from another focus beyond limitation to monitoring glucose levels only.The third project is detecting biomarkers related to kidneys, because kidney disease is one major diabetic complication. About 40% of diabetes patients develop kidney disease, termed diabetic nephropathy. For assessment of kidney function, conductometric sensors for creatinine and cystatin C (cysC) were developed. The sensors successfully detected creatinine and cysC in both phosphate buffer saline (PBS) and artificial saliva in nanomolar (nM) range. The detection limit for both creatinine and cysC was determined as 0.01 nM, which is more than 500× and 1000× times lower than critical concentrations to diagnosis kidney disease by using these two biomarkers, respectively. Moreover, creatinine and cysC sensors were both fabricated following one protocol, with the only difference at antibody immobilization step. A battery-free miniaturized thin-film device for reading sensors signals was fabricated as a proof-of-concept of the ease of use. The outcome and analysis of the third project provide a potential route for assessing kidney function in home and a scalable fabrication process of conductometric sensors for different biomarkers.The outcomes of these three projects are all associated with non-invasive sensing techniques for biomarkers related to diabetes management. As searching for a definitive cure for diabetes would be a long-lasting process, these research works would contribute to the transformation of biosensing in diabetes to be a painless, rapid and easily accessible process.</p
Investigations Into Illicit Synthesis of Fentanyl
No full textFentanyl is a powerful synthetic opioid that is an effective and widely used analgesic in medicine. Over the last decade however, it has also had a devastating impact in some countries as an illicit drug. Fentanyl is approximately 80 to 100 times more potent as an analgesic than morphine, and due to its widespread availability and low cost, it has fuelled the opioid crisis in North America, leading to record numbers of overdoses and fatalities, with an estimated 76,226 deaths in the US for 2022 (National Center for Health Statistics 2024). Efforts to combat its impact have included law enforcement measures and public health campaigns to raise awareness. Despite these efforts, the crisis continues to strain healthcare and emergency services and cause misery for millions, with an estimated impact of $1.5 trillion on the US economy in 2020 alone (US Congress Joint Economic Committee 2022). This highlights the need for more comprehensive strategies, including greater research into how illicit fentanyl is manufactured.
Over the last decade, five alternative synthesis methods have been published in scientific journals and on drug enthusiast websites detailing optimised processes for producing fentanyl. As governments have sought to stem the flood of the drug into the market, key precursors were placed under tight regulations and controls. Sadly, this only resulted in adaptation from organised crime groups, who now employ these alternative methods to maintain production. This thesis largely focuses on four of these principal synthetic approaches. These are the Siegfried, Valdez, One-Pot and Dieckmann methods, with some supplemental analysis of the original Janssen and emerging Gupta-patent method.
Chemical profiling methods are based on determining and quantifying a particular compound of interest while also identifying the impurities present. The characterisation of the impurities can be used as an impurity profile, chemical fingerprint, or Chemical Attribution Signature (CAS). In the case of illicit synthetic drugs, certain impurities are often route-specific. This might be due to the available starting material, poor chemical handling during synthesis, side reactions of the intermediates formed, inadequate purification procedures, or contamination either in the reagents, the adulterants or diluents. Identifying and understanding the impurity profiles would allow drug samples to be exploited for chemical forensic information, such as determining their method of synthesis.
The need to detect fentanyl and its analogues in the field has increasingly become important to prevent unintentional exposure to first responders or the public. Because of this, the use of portable spectrometers has been increasing, but these can lack sensitivity. To address this, machine-learning algorithms can be used to improve the information gained from handheld devices to help protect law enforcement and first responders and inform the next steps for more detailed laboratory analyses. In support of both drug profiling and improving portable spectrometers, the integration of chemometric data analysis techniques such as Multivariate Analysis (MVA) can assist in the analysis and interpretation of complex impurity profile patterns derived from spectroscopy data. They can also predict if/how samples can be classified by their synthetic methods due to unique impurity profiles. To address these challenges, this thesis seeks to contribute to existing knowledge of impurity profiles and investigate the utility of portable instruments coupled with chemometric techniques.
The first study in this thesis was an in-depth review and evaluation of the known synthesis methods, to determine each method's optimum conditions and key features. This was followed by the analysis of low-field Nuclear Magnetic Resonance (NMR) spectroscopy data by MVA techniques, which effectively facilitated the classification of the fentanyl precursors, N-phenethyl-4-piperidone (NPP) and 4-anilino-N-phenethylpiperidine (ANPP). This study revealed that 1H low-field NMR spectra contain sufficient information for successful MVA, and subsequent classification based on the distinctive impurity profiles associated with each synthetic method. It has underscored the utility of low-field benchtop NMR in the forensic attribution of clandestine fentanyl, particularly in situations where high-field NMR may not be accessible.
In a third study investigating the utility of field-portable instruments, data obtained from portable Fourier-transform infrared (FTIR) and Raman instruments for NPP and ANPP samples were analysed to determine their specific synthesis methods using chemometric approaches. The study revealed that both FTIR and Raman spectra contain sufficient information for effective MVA and subsequent classification for ANPP, with models exhibiting excellent fitting and predictive abilities. However, when it came to discriminating between the Siegfried, Valdez, or Dieckmann classes for NPP, neither FTIR nor Raman alone proved sufficient. To address this limitation, both low-level and mid-level data fusion were explored, where mid-level data fusion yielded the best results, surpassing the individual instruments by significantly reducing variables, demonstrating good predictive power, and correctly classifying all test samples.
In the final two studies, NPP, ANPP and fentanyl were investigated by Liquid Chromatography – High-Resolution Mass Spectrometry (LC-HRMS) and MVA. The fourth study identified twenty-two impurities specific to the Janssen and twenty-one to the Siegfried method. Additional profiling of NPP and ANPP identified a further twenty-three impurities present in both ANPP and the final fentanyl. Of these, eleven were specific to the Janssen method and five to the Siegfried method. A group of carbamate impurities identified during this study was of particular interest, with three being indicative of the Valdez or Siegfried methods and one specific for just the Valdez method. The fifth study saw the NPP and ANPP LC-HRMS data coupled with MVA to build models for method attribution. This study again identified the same group of carbamate impurities and provided a proof of concept for identifying ANPP samples and subsequently classifying them by a synthetic method.
This research is significant because it adds to the existing knowledge of impurity profiles of fentanyl and its precursors NPP and ANPP, which could attribute an unknown sample to a specific method. This could assist forensic chemists in any future studies or investigations and support community awareness as well as other harm reduction strategies. Due to the significant threat posed by the high toxicity of fentanyl, any additional forensic information that can supplement the current knowledge pool for law enforcement and federal security agencies will be of significant benefit.</p
Designing an Automated Barking Drone to Detect and Repulse Cattle
No full textThe growth and expansion of the Australian livestock industry have given rise to several challenges associated with the age-old mustering method. Specifically, the direct and indirect costs of mustering livestock have escalated due to increased fatalities associated with quadbikes use, soaring prices of quality sheepdogs, and concerns related to livestock welfare. In response, this research proposes an innovative approach using quadcopters equipped with image recognition technology, specifically the YOLOv4-tiny Object Detection Model (ODM), to assist livestock farmers in conducting these activities more efficiently. The primary objective of this study is to investigate the overall efficiency of such a system, with a focus on the real-time application of an Object Detection Algorithm, Animal-Machine Interaction (AMI), and a machine-vision-based drone positioning architecture for cattle mustering in a real-world environment. There are three main stages to this study: the construction of a quadcopter, the training and evaluation of an object detection model, and the testing of employing such a system to assist farmers in mustering activities. The larger goal of this study is to contribute to modernising livestock management practices and address the challenges posed by traditional mustering methods. Our results showed that the onboard ODM achieved a real-time average precision of 85.35% in detecting cattle on the farm. Additionally, cattle responded predictably to the drone, moving away as it approached. When strategically positioned, the drone effectively guided the cattle toward the desired location. On the other hand, short-term habituation to the presence of barking drones was observed, where the cattle’s escape distance decreased from an initial 85m to 12m after three consecutive trials within the same day. It is important to note that this habituation effect was not long-lasting, as similar behaviour was observed in a preliminary test conducted three months before the experiments. In conclusion, this study demonstrates the feasibility of using a quadcopter to assist with herding activities. Through real-world trials, it has established a foundation for further development and refinement of such systems, paving the way for advancements in livestock management.</p
A Rapid One-Step Microfluidic Fabrication Approach for Multiple Through-Hole Generation Enabling Spatial Transcriptomic Analyses
No full textWith the development of complex microfluidic systems, biomedical field has witnessed a revolutionary change in the way biological analyses are performed that are otherwise unattainable using conventional laboratory methods. Polydimethylsiloxane (PDMS) based microfluidic chips have enabled high parallelization, i.e. testing thousands of samples simultaneously, allowing researchers to make sound decisions in a single run. For example, spatial transcriptomic data that plays a vital role in cancer research and developmental biology can now be retained by microfluidic devices that are capable of delivering barcoded DNA tags to a tissue sample. However, reservoirs used for storing the DNA barcodes are fabricated by manually punching through the PDMS layer. There is no current method that allows us to create multiple of these through holes simultaneously. Manual punching is unreliable because not only is it time consuming but also requires skilled personnel to create those through-holes without leaving any PDMS debris inside the microchannels. This gives rise to a need for an easy interface fabrication method so that researchers take advantage of microfluidic systems and continue to explore high resolution spatial transcriptomic data through increased number of holes. Hence, my research focusses on exploring a fabrication approach to create multiple through-holes in PDMS. To do that, I have explored the utilisation of a vulcanizer. The parameters that greatly influence the fabrication of through-holes such as PDMS degassing and curing times, curing temperature and the amount of weight that needs to be exerted on the PDMS layer during the curing process were optimized to a certain value to produce repeatability. The advantages that the one-step through-hole fabrication method possesses in comparison to manual punching technique was studied. The validity of the approach was tested by creating a microfluidic device involving 100 number of reservoirs and applying it to the context of spatial transcriptomics. I checked the proper delivery of barcodes using the microfluidic design using fluorescent labels. This signifies that further binding of DNA barcodes to the tissue sample can be achieved and researchers can make use of spatial transcriptomics to its full potential by increasing through-holes.</p
Smart Food Labels: Streamlining Food Safety and Sustainability
No full textAccording to reports from Food Bank Australia, 7.6 million tonnes of food is wasted annually in the country. Unfortunately, 70 per cent of it is edible. This costs the Australian economy $36 billion, which results in 17.3 million tonnes of carbon dioxide emissions and wastes 2,600 gigalitres of water used for the production of food. Currently, there are very few reliable indicators for real-time monitoring of food quality other than the expiry date. An intelligent packaging system monitors the food packet or surrounding environment, provides information about the function and properties of the food packet, and ensures food safety by signalling food spoilage. However, dye-based colourimetric sensors currently employed in intelligent packaging devices are composed of indicators with natural limitations like narrow colour change range, easily degraded by UV radiation, and inconsistent results due to interference of stimuli like temperature, pH, and lipids. To overcome these limitations, we propose photonic crystal sensors. These sensors are expected to change colour when gases like volatile amines or organic solvents are released from spoiled fish or meat packets. An innovation to improve food safety and reduce food waste.</p
Effect of High Temperature Treatments on Buffalo Milk and Buffalo:Bovine Milk Blends
No full textMilk has been a big part of the human diet for centuries, conferring essential nutrients such as protein, fat, lactose, vitamins, and minerals. The concentration of these nutrients varies with milk source, and these components are further modified when milk is subjected to various processes to extend its shelf-life. These process-induced changes in milk had been widely studied in bovine milk being the most consumed milk. In Asian countries, however, buffalo milk is sometimes more abundant than bovine milk hence it is common practice to market milk as mixture of buffalo and bovine milk. Blending milk from different species could improve the quality of dairy products derived from them, yet there have been very few studies conducted on milk mixtures. Thus, this study aimed to investigate heat-induced changes in the physicochemical, structural, and functional properties of milk blends consisting various ratios of buffalo skim milk to bovine skim milk.
In the first stage of this research, the impact of heat treatment (80, 85, 90 or 95°C for 5 min) on the physicochemical characteristics and protein structural properties in buffalo:bovine milk blends (0:100, 25:75, 50:50, 75:25, and 100:0) was studied. Results showed that heat treatment at ≥85°C induced significant changes in pH and viscosity, and modified the levels of non-sedimentable caseins and whey proteins in milk. The extent of these changes increased with increasing proportion of buffalo skim milk, mainly due to differences in protein and calcium concentrations. Variation in protein and calcium contents led to differences in the level of heat-induced casein dissociation, alternation in salt balance, denaturation of whey proteins and subsequent association with caseins.
Based on the physicochemical and structural properties of milk blends, heat treatment temperatures of 85 and 95°C were selected for the second stage of the study. In this stage, calcium equilibria in buffalo:bovine milk blends was modified through the addition of the calcium sequestering salts (CSS) trisodium citrate (TSC) and disodium hydrogen phosphate (DSHP) before the heat treatment. Results indicated that the physicochemical properties of milk blends were markedly changed by the addition of CSS mainly as a result of dissolution of colloidal calcium phosphate due to calcium sequestration. This subsequently affected the structural and physicochemical characteristics of proteins in the milk blends mostly during heat treatment at 95°C. The extent of these heat-induced changes increased proportionally to the concentration of buffalo skim milk in the mixture. Overall, DSHP addition impacted milk with higher proportion of bovine skim milk while the addition of TSC mostly affected milk with higher proportion of buffalo skim milk. This is mainly due to the presence of high amounts of micellar calcium in buffalo milk which subsequently formed calcium-citrate complexes that are not capable of associating with dispersed caseins.
The process parameters used in the second phase of the research were also adopted for the third phase to assess the impact of CSS addition and heat treatment on the rheological and textural properties of buffalo:bovine milk blends. In this stage, buffalo:bovine milk blends were acidified through the addition of 2.5% glucono-δ-lactone (GDL). Gelation time decreased with heat treatment and with increasing proportion of buffalo skim milk in the samples. This is parallel to an increasing gel strength, gel firmness, and water-holding capacity (WHC). However, when milk is subjected to heat treatment at 95°C, gelation time of milk mixtures increased but the resulting acid gels exhibited higher gel strength and gel firmness than acid gels prepared from either buffalo skim milk or bovine skim milk alone. This underscores the influence afforded by the presence of micellar whey protein-casein complex on the rheological and textural properties of acid gels. The addition of CSS increased gelation time and WHC but decreased gel strength and gel firmness, irrespective of milk ratio and heat treatment. While TSC and DSHP had similar effects on gel WHC, gelation time was lower in DSHP-added milk but the resulting acid gels exhibited higher gel strength and gel firmness than TSC-added milk. This confirms the fact that CSS-induced casein dissociation and the formation of calcium-citrate complex greatly affect physicochemical and structural properties of buffalo:bovine milk blends and this subsequently influence the rheological and textural properties of acid gels prepared therefrom.
The in vitro gastrointestinal protein digestibility of buffalo:bovine milk blends was assessed in the fourth stage of the study where milk was added with TSC or DSHP before heating at 85 or 95°C for 5 min. Results showed that protein digestibility decreased with increasing proportion of buffalo skim milk. Heat treatment of milk did not have a significant effect on the digestibility of proteins but the digestibility of κ-casein was slightly reduced for CSS added samples before heat treatment. When milk was heated at 85°C, the impact of TSC and DSHP addition on κ-casein digestibility was comparable but when milk was heated at 95°C, the addition of TSC prior to heating resulted to higher κ-casein digestibility than DSHP addition did. In all milk samples, α-lactalbumin and κ-casein were the most resistant proteins against degradation by digestive enzymes.
Overall, mixtures of buffalo and bovine skim milk can be prepared and subjected to heat treatment with careful control of process parameters depending on the milk ratio. This study provides important insights that can be used to obtain desirable quality characteristics in heat-treated dairy products made from buffalo:bovine milk blends.</p
Odours and Emissions from Bituminous Road Materials and their Mitigation
No full textTo manage emissions and odours from bitumen, a clear understanding of the causes of
these phenomena is crucial to developing mitigation strategies. Bitumen is a complex
product that is extracted from crude oil. The occurrence of fuming or highly odorous
events is currently unable to be predicted, leading to disruptions of projects and residents
when these events occur. The complex chemical composition of bitumen adds difficulty
in deciphering the causes of odour and emission. To date, no published work has
provided predictive models that can be widely employed to manage bitumen emissions
and odour through pre-emptive strategies. This thesis aims to investigate chemical and
physical metrics that can be used within the industry to implement proactive
management strategies in the management of odour and emissions.
To investigate fuming in bitumen, a multimethod approach was used by
quantifying benzene, toluene, ethylbenzene, and m, o, p-xylene (BTEX), measuring the
partition coefficients of these analytes, and finally, a novel method to measure the volatile
mass of bitumen utilising thermogravimetric analysis (TGA). It was found that the
concentration of BTEX varied significantly between bitumen samples. The partition
coefficients of these analytes are essentially the same regardless of the sample. Finally,
the volatile mass of each sample varied significantly between samples, independent of
bitumen grade or country of origin. The volatile masses of the bitumen correlate strongly
with fuming events in bitumen and can reliably be used as predictors of bitumen fuming
risk, allowing pre-emptive management of high-emission bitumen.
To enable the prediction of poor odour in bitumen, this work introduces a Linear
Discriminant Analysis (LDA) method, utilising data from headspace gas chromatographymass
spectrometry (HS-GC–MS) of bitumen samples to forecast the likelihood of odours
in bituminous road binders. The LDA model, developed using HS-GC–MS results from
sixteen straight-run binders of known odour status collected globally, demonstrates high
accuracy in odour prediction through two cross-validation techniques. This accuracy
enables the rapid identification of odorous bitumen samples using GC–MS data.
Furthermore, this method suggests alkanes and arenes contribute directly or indirectly
to odour in a significant way. The proposed approach provides a simple and practical
tool, offering the potential for selective use or pre-treatment of bitumen, thereby reducing
the introduction of highly odorous binders into paving projects. This methodology
presents an innovative step towards proactive odour management in asphalt paving,
contributing to community well-being, environmental quality, and the efficiency of paving
operations.
Additives that can reduce fuming or odour offer a strategy to mitigate excessive
fuming and odour in products known to cause these events. The effectiveness of
additives in managing these events is poorly catalogued in current literature. This thesis
investigated a range of additive classes, including commercial additives, natural
products, synthetic chemicals, biomolecules, and solid adsorbents. Headspace gas
chromatography-mass spectrometry (HS-GC-MS) was used to measure the release of
volatile organic compounds (VOCs) from bitumen after adding these additives. Among
the additives investigated, solid adsorbents were the most effective in mitigating the
emissions of the monitored VOCs. These results suggest that solid adsorbents such as
carbonaceous materials may be prioritised for managing VOC emissions in asphaltrelated
projects.
This thesis greatly contributes to understanding emissions and odour in bitumen
materials. Strategies that reduce the entry of poor-quality products into projects are
essential in controlling emissions and improving air quality in construction. These
findings allow a proactive approach to mitigate emissions through product selection and
management strategies by employing safe additives in emission control.</p
The effect of environmental contaminants on the health of seabirds
No full textEven the nest is not a place safe from pollution for seabirds, with many short-tailed shearwaters having been fed meals of plastic by their parents before ever taking flight for the first time. Despite knowing that plastic is ingested by young shearwaters, there is still much that is uncertain with regards to how the amount and type of plastic may affect their normal growth, maturity rates, and health. This study uses methods from veterinary science and ecotoxicology to measure health and size parameters against pollution levels in birds located on Phillip Island, Victoria, Australia, in order to assist in determining the degree and types of threats faced by these birds.</p
Simultaneous Road Objects and Lane Detection Models in Autonomous Vehicles
No full textPoor road boundary lanes and detections of road objects have been identified as some of the serious causes of road accidents, in both conventional and autonomous driving. Therefore, it is critical to develop models that could help autonomous vehicles' perception systems while accurately identifying and locating road objects from images and video frames. However, the existing models face a series of challenges due to the highly complex nature of the road traffic scene and the influence of various road objects on the manoeuvring. Most of the existing models cannot simultaneously detect all the major road objects, with some, either detecting lanes or detecting some of the road objects. To address these gaps, the combined road objects and lane detection model was developed using the You Only Look Once (YOLO) algorithm. As a first step, a model was developed to detect road objects only and the results were compared with existing studies. Next, another model was developed to detect road lanes based on YOLOv8 capability. Finally, an improved YOLOv8 model was developed to simultaneously detect road objects and lanes. To achieve this, the YOLOv8 model was tuned and optimised using various optimization approaches considering several hyperparameters such as activation functions and regularisation methods. Further, the effect of augmentation was investigated using three techniques; cut-out, rotation and rotation with noise. Also, the effect of the data stream on the performance of the model was investigated based on the obtained hyperparameter. The relevant performance metrics such as precision, F1, and recall were deployed. In addition, mean average precision calculated at an intersection over union (IoU) threshold of 0.5 and 0.95 was reported to assess the model's detection capabilities. The results from this study were further compared with some existing studies such as Feature Pyramid Networks, Task-aligned One-stage Object Detection, Dynamic R-CNN Probabilistic Anchor Assignment with IoU Prediction, Sparse R-CNN and CenterNet to demonstrate the contribution of the model. Further, the performance of the models based on different dataset (Curated data, COCO, and KITTI) showed that curated data outperformed others across all the performance metrics. Notably, curated data has the most promising results with precision, recall and F1 score of 0.68, 0.61, and 0.64, respectively. The success of the curated dataset highlights the significance of tailoring datasets to the specific nuances of the targeted application domain. Finally, the conclusion and recommendations were made based on the findings from the study.</p
Shining the Light On Australia’s Brightest: 25 years of the Australian Technology Network of Universities
No full textThis is the story of how the Australian Technology Network of Universities was established, its journey, the role of its visionary leaders, and successes over the past 25 years of advocacy. Several key moments in the timeline need to be noted, recognised and, therefore, placed in print. In November 2023, on the eve of the 25th anniversary, Frank Coletta sent me an email, enquiring if I was available and interested in writing a story on the ATN group. As someone who has been employed in Australia’s higher education for the past 35 years, I had no hesitation, this piqued my interest, so I told him yes that same day. I usually write commentary pieces which draw insights from history, past performance, and provide a view towards the future. The story that is now published emerged from a couple of conversations with Frank and Luke Sheehy (now at the helm of Universities Australia) and is largely drawn from material which I retrieved in December 2023, using the Factiva database. I retrieved newspaper stories about ATN and developments in higher education published in Australia’s mainstream media between 1996 and early December 2023. During the holiday period, I read all those stories and then I sat down and wrote most of this story in the summer months of 2024. I also conducted extensive web searches of information about ATN and group advocacy in Australia’s higher education. Truth be told, the public evidence is, at best, patchy, partly because websites tend to be updated and information that is deemed outdated is removed. To deepen our understanding of the changing environment of Australia’s higher education, preserving the evidence of activity is crucial. Over the years, I have been exploring the proliferation of university networks and the extent to which universities seek to establish and belong to networks. Being a member of an alliance is a key means to influence public policy through lobbying, advocacy on policy matters, and the provision of expertise. In Australia’s context, there are four national networks, all of which emerged during a period of turbulence within the national umbrella organisation. Each of those networks was established to influence public policy and derive outcomes to the benefits of their members. Over the past 25 years, Australia’s higher education has undergone significant policy reviews and the ATN has worked to ensure the best possible outcomes for all. I hope that the perspectives presented in this story will contribute to the development of policy and inform debate as well as improve practices regarding Australia’s higher education.</p