277856 research outputs found
Sort by
Theoretical calculation and machine learning aided design of functional materials for energy conversion
No full textThis thesis investigates the integration of machine learning (ML) and theoretical calculations to design and optimize functional materials for photocatalytic applications. By combining experimental techniques with theoretical calculations, that is, finite-difference time-domain (FDTD) simulations, and density functional theory (DFT) calculations, this work aims to accelerate the discovery of efficient, selective, and scalable photocatalytic systems for CO2 reduction and seawater splitting. The central focus is on leveraging ML and advanced simulations into experiments to provide new insights into plasmonic photocatalysts and microenvironmental perturbations in photoreaction.
The first study explores the development of Ag-TiO2 core-shell photocatalysts for the selective reduction of CO2 to methane (CH4). A significant contribution of this work is the use of FDTD simulations to model and optimize microenvironmental perturbations, thereby enhancing the catalytic activity of the plasmonic core-shell nanoparticles. Additionally, DFT simulations demonstrate that localized surface plasmon resonance (LSPR)-induced electric field enhancements lower the energy barriers for CO2 activation and methanation. Experimentally, this system achieves 100% selectivity for CH4 with a production rate of 75 umol/g/h. This study emphasizes the advantages of microenvironmental engineering in optimizing photocatalytic activity and selectivity, with FDTD and DFT simulations further elucidating the mechanisms of microenvironmental perturbations.
The second study focuses on the design of Co-NC@Cu core-shell photocatalysts for solar-driven hydrogen production from seawater. By dispersing single Co atoms on a nitrogen-doped carbon (NC) shell surrounding a Cu core, this novel catalyst achieves a hydrogen production rate of 9080 umol/g/h and a solar-to-hydrogen (STH) conversion efficiency of 4.78%. A key highlight of this work is the detailed investigation of the local coordination environment of the single Co atoms, as well as the thermodynamic and kinetic effects of electric field perturbations on the catalytic process. DFT calculations reveal that the single Co atoms act as highly active sites for hydrogen evolution, exhibiting low energy barriers for the reaction. Furthermore, the electric field's role in enhancing the reaction thermodynamics and kinetics was elucidated, providing insights for further optimization of catalytic performance. Integrating single atoms, photothermal effects, and localized surface plasmon resonance (LSPR) demonstrates a robust and efficient design for seawater splitting.
The third study showcases a comprehensive workflow combining ML and DFT calculations to accelerate the discovery and optimization of single-atom-based (SA) 2D photocatalysts. Using a dataset of Janus-TMD materials as a case study, ML models were trained to identify high-activity catalytic sites and screen potential substrates for photocatalytic CO2 reduction. The ML-driven predictions successfully prioritized optimal single-atom catalysts, with experimental validation confirming the activity and selectivity of two synthesized Janus substrates MoOSe with single-atom Pt. Photocatalytic experiments demonstrated the potential of the ML-guided design in delivering efficient and selective catalysts, underscoring the synergy between computational and experimental approaches. The growing dataset of atomic structures, intermediates, Janus configurations, and adsorption models provides a robust foundation for refining ML models and driving innovations in SA-based 2D materials discovery.
In conclusion, this thesis demonstrates the successful integration of ML, FDTD, and DFT techniques with experimental approaches for the design of advanced functional materials, which contribute to the development of sustainable energy solutions through CO2 reduction and hydrogen production
Nitrogen deficiency drives fungal compositional shifts without functional changes in wheat rhizosphere
No full textNitrogen (N) deficiency reduces crop yield, but this effect may be mitigated by symbiotic interactions between crops and fungi. However, the response of wheat-fungal interactions to N deficiency remains unclear. We hypothesised that wheat cultivars with a higher reported nitrogen use efficiency (NUE), would induce shifts in the fungal community composition and functional profiles within the wheat rhizosphere to tolerate N deficiency. A glasshouse experiment was conducted to examine the effects of N deficiency on the rhizosphere fungal communities of wheat (Triticum aestivum L.) cultivars Gladius (low N-use efficiency) and Mace (high N-use efficiency). Plants were grown until the mid-anthesis stage in a Dermosol soil treated with either 0 (Low-N) or 90 kg N ha⁻1 (High-N). The rhizosphere fungal communities were characterised using quantitative PCR, ITS rRNA metabarcoding, and metagenomics. The abundance and diversity of the rhizosphere fungal community were not significantly influenced by N deficiency in either Mace or Gladius cultivars (P > 0.05). However, the fungal community composition showed significant variation across N treatments in Mace (P 0.05). Differential abundance analysis and fungal trait predictions indicated a reduction in fungal symbionts in both cultivars under N deficiency (P 0.05) but significantly differed between Mace and Gladius (P < 0.05). This study reveals intraspecific variation in rhizosphere fungal responses to N deficiency between Mace and Gladius. The metabarcoding and metagenomic data suggest functional redundancy within the fungal community, which may enhance wheat resilience under N-deficient conditions. These findings highlight the potential of using fungal community stability in developing biofertiliser products for sustainable agriculture.This research was supported by the Australia Research Council's Industrial Transformation Research Program funding scheme (IH200100023). The wheat seeds for this research were provided by the Australian Grain Technology (AGT). The computational resources were supported by The University of Melbourne's Research Computing Services and the Petascale Campus Initiative.Peer-reviewe
Perceptions of science, science communication, and climate change attitudes in 68 countries - the TISP dataset
No full textScience is integral to society because it can inform individual, government, corporate, and civil society decision-making on issues such as public health, new technologies or climate change. Yet, public distrust and populist sentiment challenge the relationship between science and society. To help researchers analyse the science-society nexus across different geographical and cultural contexts, we undertook a cross-sectional population survey resulting in a dataset of 71,922 participants in 68 countries. The data were collected between November 2022 and August 2023 as part of the global Many Labs study "Trust in Science and Science-Related Populism" (TISP). The questionnaire contained comprehensive measures for individuals' trust in scientists, science-related populist attitudes, perceptions of the role of science in society, science media use and communication behaviour, attitudes to climate change and support for environmental policies, personality traits, political and religious views and demographic characteristics. Here, we describe the dataset, survey materials and psychometric properties of key variables. We encourage researchers to use this unique dataset for global comparative analyses on public perceptions of science and its role in society and policy-making.Peer-reviewe
Maintaining robust terrestrial ecological monitoring amid technological advancements
No full textLong-term terrestrial biodiversity monitoring is crucial for understanding species and ecosystem responses to global change, yet it requires significant investment. Technological advancements offer opportunities for more efficient, scalable, and cost-effective monitoring, but transitioning to new methods presents significant risks to data integrity. Guidance for researchers and practitioners to manage these transitions, therefore, is critical. We present a novel seven-step framework and decision-making tool to guide the integration of new methods into established monitoring programs. The framework includes compatibility assessment, concurrent method cross-validation, and ongoing review, balancing the benefits of new technologies with the need to maintain dataset integrity. Our framework can help to ensure that new methods enhance the value and robustness of long-term biodiversity datasets while maintaining monitoring continuity.J.C.B. was supported by funding from The Department of Climate Change, Energy, the Environment and Water (DCCEEW) Innovative Biodiversity Monitoring grant scheme, which was awarded to D.B.L., B.C.S., and M.J.E.Peer-reviewe
International norms clash with China’s consumer nationalism
No full textThis study interrogates the motivations and impacts of China’s state-sponsored political boycotting on the behaviour of multi-national corporations (MNCs). In 2016–2021, Lancome, Cathay Pacific, H&M, and the National Basketball Association (NBA) were exposed to political boycotts due to their stance on human rights and political issues in China. In these incidents, the state media’s censure and consumers’ political boycotting jointly generated a spiral of outrage. Lancome and Cathay Pacific reversed their positions in accordance with China’s demands. Conversely, H&M and NBA stood by their stance. Drawing on the four case studies, this article argues that the tug of war between China’s markets and the liberal world’s values influenced an MNC response. It contributes to the political consumerism literature in three ways. First, it examines political consumerism in non-democratic contexts. Second, it offers nuances of political boycotts by pinpointing the dynamics between the state’s mobilisation and consumers’ motivation. Third, it underlines the conditions that make MNCs susceptible to political boycotts. Meanwhile, the article also speaks to the economic statecraft literature. Political boycotting is an underexplored instrument in China’s economic statecraft. By pressuring MNCs to conform to the Chinese rules, state-sponsored political boycotts gradually contest norms in the liberal international order.Peer-reviewe
Rights, Relationships and Respect Evaluation
No full textThe ANU Sexual Violence Prevention Strategy (2019-2026) envisions a violence-free campus, emphasising primary prevention of sexual violence. This includes addressing systemic power imbalances and social norms that drive violence. In response to a 2021 internal review identifying gaps in respectful relationships and consent education, the Respectful Relationships Unit (RRU) and Student Safety and Wellbeing (SSW) developed the Rights, Relationships and Respect (RRR) program. The program includes a compulsory online module for incoming residential students and a pilot curriculum of workshops in select halls.
ANU POLIS: The Centre for Social Policy Research conducted an evaluation to assess the program’s implementation and effectiveness.
RRR Online Module Findings
In 2023, two-thirds of incoming students (4,494) completed the online module. Key survey results from 1,284 respondents indicate:
• 89% completed the module due to its perceived importance.
• 85% rated it as clear and valuable, with positive feedback from female, postgraduate, and international students.
• Students expressed trust in ANU support services, especially among male and international students.
• Learning activities revealed strong student understanding of sexual misconduct policies, consent, and bystander action.
However, students requested clearer guidance on sensitive topics and managing personal boundaries in professional settings.
RRR Workshop Findings
The workshops targeted three residential halls, offering progressive learning on identity, sexual violence prevention, and empowerment. The evaluation highlighted four key themes:
1. Engagement: Attendance decreased across workshops (394 → 130). Challenges included session timing, content relevance, and mandatory attendance policies. Female students noted a need for greater male participation. Language and cultural barriers appeared to hinder engagement among international students.
2. Culture: Cultural differences across halls appeared to impact engagement. Self-catered and catered halls reported stronger community ties, while the privately-operated hall displayed a dominant party culture, overshadowing inclusivity efforts. Addressing such cultural dynamics is critical to fostering respect.
3. Development and Implementation: Co-designed workshops with peer facilitators were well-received for their adaptability and safe learning environment. Continued feedback-driven development was seen as a strength.
4. Student Experience and Learning: Reactions were mixed. Some students found content too simple, while others, particularly international students, encountered it for the first time. The workshops raised awareness of consent and gendered violence but called for more actionable strategies and deeper discussions on hall culture.
Conclusion and Recommendations
The RRR program showed positive engagement, particularly through the online module, and has potential to shape a respectful campus culture. However, face-to-face workshops faced challenges in sustaining participation and addressing cultural and language barriers. Future efforts should focus on flexible scheduling, tailoring content to student demographics, and addressing hall-specific cultural dynamics.
Key Recommendations:
• Continue co-designing workshops with peer facilitators.
• Expand workshop topics, including masculinity, coercive behaviour, and alcohol use.
• Tailor approaches for different hall cultures and foster leadership training.
• Monitor and evaluate ongoing program development.
With thoughtful implementation, these recommendations will strengthen the University’s violence prevention efforts and compliance with the upcoming National Higher Education Code to Prevent and Respond to Gender-based Violence
Trump’s Harvard ban exposes Australia’s foreign student problem: For lecturers striving to provide a meaningful learning experience for all, it presents a real dilemma when some students struggle with basic English.
No full textIn the ongoing clash between Donald Trump and Harvard University, the US president criticised the top Ivy League institution for allegedly prioritising foreign students over domestic applicants. This has reignited debates about who benefits most from access to elite universities.Not peer-reviewe
State Estimation and System Identification of Distribution Grids
No full textAs we combat climate change, transitioning to a carbon-neutral economy has become imperative, particularly in the electricity sector. This shift necessitates moving from fossil fuel-based electricity generation to the integration of distributed energy resources (DERs) such as solar power, batteries, and electric vehicles. However, integrating DERs presents significant challenges, such as over-voltage; larger negative demand during sunny days resulting in negative power flows, known as the duck curve. Addressing these challenges requires the development of advanced control strategies for DERs within the distribution grid (DG). Effective distribution network management and operational strategies are crucial for facilitating a smooth transition to a sustainable and reliable energy system.
To improve the control of DERs, it is essential to have accurate knowledge of the current state of the DG and its network model. However, the system states are often unknown, and network line parameters are typically either unknown or inaccurate for a given DG. Therefore, estimating system state variables and line parameters is crucial for the successful management of DERs and maintaining grid stability. This thesis focuses on distribution system state estimation (DSSE) and line parameter estimation, utilizing data from readily available devices, such as smart meters (SMs) in the DG.
An accurate and computationally efficient power flow model is essential for any estimation problem. Chapter 3 proposes a new power flow model called the modified Distflow model, a modified version of the widely known Distflow model. It incorporates line losses and is expressed by explicit equations, eliminating the need for an iterative solver, and thereby maintaining accuracy at a lower computational cost.
Chapter 4 introduces a comprehensive DSSE framework and proposes a new DSSE approach using the modified Distflow model. It focuses on estimating the system state variables with limited availability of measurements. An analysis of the accuracy with measurements available at different locations has been performed. All required gradients for the approach are provided. Here it is assumed that the network parameters are known.
However, line parameter information is typically unavailable in the DG. Hence it is important to estimate the line parameters. Chapter 6 proposes a new method for estimating the line parameters and system state variables with noisy measurements at all nodes. Additionally, the voltage magnitudes are estimated accurately. This is achieved using a novel combination of Linearized Distflow and expectation maximization (EM) with Bayesian regression.
Despite achieving accurate results in the previous study, it utilized a linearized power flow model that neglects line losses. This approach requires more data and higher accuracy can be achieved with a power flow model considering losses. Hence, in Chapter 7, the non-linear modified Distflow model is utilized, including line losses. As a non-linear model, it requires a different solution approach. EM is still employed, but a first-order Taylor expansion to approximate the distribution of state variables is implemented using the efficient square root form. This changes the cost function and improves accuracy compared to the method proposed in Chapter 6 and other similar studies.
In the previous chapters, noisy measurements at all nodes were considered for line parameter estimation. Chapter 8 addresses the challenging case of missing measurements in the DG while estimating line parameters. While EM is used as a solver, handling missing data significantly alters the problem formulation. Despite these challenges, accurate results are achieved with as little as 50% measurement coverage. Accurate results were achieved even with noise levels of 4.5%. Additionally, the impact of missing measurements at different locations in the DG is analyzed to assess their impact on estimation accuracy
Automated alignment of an optical cavity using machine learning
No full textOptimised alignment is important in optical systems, particularly in high-precision instrumentation such as gravitational wave detectors, in order to maximise the sensitivity. During operations, high performing optical wave-front sensing and feedback systems are used to maintain optical cavity performance. However, the need for an automated initial alignment process arises after maintenance or large environmental disturbances such as earthquakes, as it can be challenging to manually achieve optimised as well as consistent optical alignments. In this study, a machine learning control system is presented to determine the optimal input beam alignment of an optical cavity based on a digital camera stream of the transmitted cavity mode. We use convolutional neural networks to classify the cavity mode from its image, with 100% prediction accuracy for the desired mode. A genetic algorithm is applied to find experimental parameters that maximise the transmitted power of a chosen cavity mode. The system demonstrates consistent alignment outcomes that the median intensity over multiple trials exceeds 95% by the sixth generation of the algorithm. These results show that machine learning techniques can be implemented to automate the alignment process that is compatible for a broad range of optical resonator platforms.This research is supported by the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav), Project No. CE170100004. S J also acknowledges support from Council for Scientific and Industrial Research (CSIR), India. P C is funded by Paul Lasky and Eric Thrane via ARC LE210100002.Peer-reviewe