Adelaide Research & Scholarship
Adelaide Research & Scholarship (AR&S) is Adelaide University’s research repository and provides online access to research outputs and theses of Adelaide University, University of Adelaide, and University of South Australia.
For more information on how to contribute your research outputs to AR&S, visit the Deposit your Research to AR&S guide.
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Recent Submissions
Designing Immune-Instructive Biomaterials: The Interplay of Biomaterial surface, IgG Unfolding, and Macrophage Modulation
(University of South Australia, 2025-12-11) Alemie, Markos Negash
This doctoral research investigates how biomaterials can be designed to guide the immune system’s response in beneficial ways. By studying how the immune system interacts with different model implant materials, the work reveals strategies to modulate immune response, promote healing and improve the integration of medical implants. These findings contribute to the development of smarter biomaterials that work in harmony with the body’s natural defences, offering new possibilities for regenerative medicine and immune modulation.
〈乐器与田野〉 [Musical Instrument and Fieldwork]
(2019) Tsai, T.
Formulation of Personalized, Fortified Beverage Nanoemulsions for Space Exploration with Omega-3 Polyunsaturated Fatty Acids
(American Chemical Society, 2026) Schmidt, S.; Fisk, I.D.; Yang, N.; Saarela, M.; Hessel, V.
Current food trends (i.e., personalization and fortification with bioactives) can reduce food monotony and health risks experienced by astronauts during space missions. Using beverage nanoemulsions (beverage emulsions: oil content ≤1 g/kg; nanoemulsions: diameter ≤200 nm for high bioavailability) as a formulation approach for fortified beverages, this study investigates (1) how product formulation influences the emulsification process (self-assembly of droplets via low-energy emulsification, i.e., spontaneous emulsification) compared to a model system, (2) how to establish beverage personalization, and (3) how to achieve future beverage production. An emulsion model system (oil phase: medium-chain triglyceride and polysorbate 80 at a surfactant-tooil mass ratio of 1.0; aqueous phase: 5 mM phosphate buffer at pH = 7.4; 1 g/kg oil) with an average droplet size of 184 ± 12 nm (batch process) was successively extended with common beverage ingredients (acids, sugars, aroma) and fish oil rich in omega-3 fatty acids. While aroma compounds and acids have a subsidiary influence on emulsion characteristics, an increase in sucrose concentration increased droplet size and the incorporation of fish oil decreased droplet size. Six different beverage recipes (varying in aroma compound and sweetness level) with a dosage of 90 mg bioactive per 330 mL serving were developed for a recipe library for future beverage personalization. Translating the emulsification to a continuous microfluidic (channel diameter ≤1 mm) process led to a further decrease in droplet diameter (final emulsions: 80−120 nm). Thus, emulsification efficiency (droplet size in relation to surfactant use) can be increased both via appropriate ingredient selection and process mode.
Estimating the Impacts of Future Extreme Heat on Dryland Threatened Mammals: An Australian Case Study
(Wiley, 2026) Bilby, J.; Cornwell, W.K.; Moseby, K.
Extreme heat is an escalating threat to biodiversity, with dryland ecosystems particularly vulnerable. Using Australia's dryland mammals as a model system, we present a framework comparing baseline and future heat envelopes for 36 threatened species to quantify future heat exposure, identify potential refugia and evaluate the suitability of translocation sites. We conducted a systematic review to assess our understanding of these species' thermal ecology. Our analyses identified eight species (22%) as high risk (three rodents, two dasyurids, one bat, one wombat and one macropod), for which their current distributions are projected to substantially exceed both current and historical heat envelopes. Rodents were overrepresented as high-risk species while bats and dasyurids were underrepresented, whereas body size, range extent and conservation status did not predict heat risk. High-risk species typically had narrow heat envelopes and have contracted to regions near their historical thermal maxima. High-risk species had rarely been translocated (50% of species), with only one moved to a site projected to remain within its historical thermal maximum under all climate futures. Most moderate-risk species (12 of 14; 86%) have been translocated, but > 60% of translocation sites are projected to exceed current thermal maxima. Our review revealed substantial knowledge gaps, with ten species (28%) entirely absent from the thermal literature, and high-risk species (mean = 1.4 studies) being less represented than moderate-and low-risk species (mean = 4.7 and 6.3 respectively). Research was biased toward behavioural and physiological responses, while critical subjects such as in situ responses, functional traits and direct evidence of heat-related consequences were limited and are urgently needed to guide adaptive management. Conservation in drylands must prioritise protection of thermal refugia, improved land condition and integrate climate projections in long-term planning. Our combined framework provides a globally relevant screening tool to identify heat-vulnerable species and direct conservation management priorities.
Exploring drivers and policy enablers of citizen engagement in renewable energy projects through explainable AI
(Elsevier, 2026) Taghikhah, F.R.; Malik, A.; Voinov, A.; Govindan, K.; Tosarkani, B.M.
Community Renewable Energy (CRE) projects play a critical role in the global clean energy transition by enabling citizens to collectively own and manage energy systems. Despite growing interest, the factors driving participation—and the extent of their influence—remain underexplored. Most prior studies conceptualize participation narrowly, overlook the relative importance of drivers, and rely on traditional statistical methods that struggle to capture complex behavioral patterns. Addressing these gaps requires approaches suited to high-dimensional data with nonlinear interactions. Machine Learning (ML) offers the flexibility to model such complexity, while explainable Artificial Intelligence (XAI) techniques provide transparency by interpreting predictions independently of the underlying algorithm—essential for generating policy-relevant insights. Using Australia as a case study, this research addresses three questions: whether ML models can accurately predict citizens’ multi-role participation levels; how robust model-agnostic explanations are when applied to heterogeneous behavioral data; and what factors drive varying levels of engagement. We surveyed 875 residents on their participation across ten CRE roles—from investing to volunteering and advocacy—synthesizing responses into a composite involvement score categorized as low, medium, or high. Classification algorithms including eXtreme Gradient Boosting (XGBoost), Multi-layer Perceptron (MLP), and Keras-based Sequential models were paired with SHapley Additive exPlanations (SHAP)—a model-agnostic XAI method—using Tree SHAP, Kernel SHAP, and Deep SHAP variants for interpretation. To evaluate explanation robustness, we developed two novel metrics: the Stability Correlation Index (SCI) and the Explanation Integrity Metric (EIM), which introduce targeted perturbations to key features. XGBoost with Tree SHAP achieved the highest stability, with SCI above 99% and EIM near zero at moderate perturbation levels. Key findings reveal that plans to adopt renewable technologies polarize participation into high or low levels, while perceptions of corporate-owned projects act as a double-edged factor driving both engagement extremes. Increased awareness of policy risks motivates moderate participation, suggesting opportunities to leverage risk communication. These insights offer practical strategies for policymakers and project developers seeking to broaden and deepen citizen engagement in CRE initiatives.