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Dipole induction by structural engineering of supports for Fe single-atom photocatalysts toward excellent photocatalytic ozonation
No full textEfforts in designing efficient polymer-based single atom photocatalysts (SAPs) have primarily focused on selecting specific metal atoms with tailored geometries and properties to control functionality. However, the impact of the light-harvesting units that bridge these single metal atoms, crucial for light absorption and energy transfer, has been largely overlooked. In this work, two carbon nitride (CN)-based iron SAPs with a similar FeN4 coordination environment are synthesized: triazine-based CN (C3N4) and nitrogen-rich triazole-based CN (C3N5), differing in the unit cell structure. C3N5 exhibits better photocatalytic ozonation performance than C3N4 due to its unit cell asymmetry, which induces a dipole field that facilitates charge transfer. The addition of iron single atoms breaks the symmetry of C3N4 to enhance the dipole moment, while they weaken the separation and migration of bulk charge carriers in the FeC3N5 SAP. The iron atoms act as active sites in both Fe-C3N4 and Fe-C3N5 SAPs, accelerating interfacial reaction kinetics. These findings demonstrate the importance of the light-harvesting unit structures of CN-based SAPs in regulating photogenerated charge kinetics and offering valuable insights for the rational design of effective photocatalysts.</p
Ultrathin Polymer Electrolyte With Fast Ion Transport and Stable Interface for Practical Solid‐state Lithium Metal Batteries
No full textUltrathin solid‐polymer‐electrolytes (SPEs) are the most promising alternative substituting for the conventional liquid electrolyte to enable high‐energy‐density, safe lithium‐metal‐batteries (LMBs). Nevertheless, developing ultrathin SPEs with both high ionic conductivity, and strong Li dendrite retardant is still a significant challenge. Here a scalable fabrication of high‐performance ultrathin (≈7.8 µm) polycarbonate‐based electrolyte (UPCE) is proposed via electrolyte structural engineering, phase separation‐derived poly(vinylidene fluoride‐co‐hexafluoropropylene) (PVH) porous scaffold, without use of additional liquid additives. The rational electrolyte structural modulation with 1‐fluoro‐4‐(1‐methylethenyl)benzene (FMB) enables a weakened Li+‐polymer interaction due to weak Li+ solvation with fluorine, benzene ring, facilitates the formation of LiF‐rich solid‐electrolyte‐interphase on Li metal surface. As a result, the designed UPCE delivers a high ionic conductivity of 4.8 × 10−4 S cm−1, an ultrahigh critical current density of 11.5 mA cm−2 at 25 °C. The solid‐state Li symmetric cell attains unprecedented ultralong cycling over 6000 h at 0.5 mA cm−2. Furthermore, the Li|LiCoO2 cell cycles stably over 1500 cycles at a high operating voltage of 4.5 V, and the pouch cell can achieve a high energy density of 495 Wh kg−1 excluding the packaging. This work offers a new pathway inspiring efforts to commercialize ultrathin SPEs for high‐energy solid‐state LMBs.</p
Data and scripts for the publication "Five years of offsetting native vegetation: The challenge of achieving no-net-loss"
No full textThe zip file contains the data and R markdown script to generate all the figures in the publication “Five years of offsetting native vegetation: the challenge of achieving no-net-loss" published in Ecological Indicators.Pre-processing of the Biodiversity Offsets Assessment Management Systemdata set, the BAM calculations of VI and ecosystem credits along with the sensitivity analysis and offset area ratio simulation were undertaken in R (R Core Team, 2024). The zip file contains the resulting data from this analysis, together with the R code to generate the figures the manuscript. The resulting dataset has been de-identified, such that original assessment, zone, and plot identifiers have been replaced and location data removed. For more details see the Supplementary Information (Section S3).To generate the figures in the paper, please execute the file R markdown file: plot_figures_bamvi_ms1.Rmd and see the file README.txt for more details on running the code. The easiest way to run the file is in RStudio. ReferencesR Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org</p
Microplastic Pollution in Stormwater and its Removal by a Novel Catch Basin Insert Using Adsorbent
No full textThe widespread presence of microplastics (MPs) in aquatic environments is raising growing concerns about plastic contamination. Road dust and stormwater runoff are significant pathways for transporting MPs from land-based sources to surrounding ecological compartments. The aim of this study is to understand the occurrence of MPs in both road dust and stormwater samples collected under various land uses, including residential, commercial and industrial areas, within Melbourne metropolitan City, Australia. This study also evaluated the ecological risk indices of MPs polymers under different land uses. MP emission characteristics and loads (number- and mass-based) were estimated to investigate pollution risks. Higher quantities of MPs were detected in road dust and stormwater in industrial areas (2410 items kg− 1 and 35 items L− 1) than in commercial (2130 items kg− 1 and 27 items L− 1) and residential (1970 items kg− 1 and 24 items L− 1) areas. Mass loads of MPs were also higher in industrial regions in road dust and stormwater samples. It was found that 200 μm to 2450 μm-sized MPs were abundant in road dust, which was higher than the sizes of stormwater MPs (125 μm to 960 μm). The water forces in the drainage system could be the reason for the breakdown of larger MPs. Fragments and fibers were the dominant shapes of MPs in all the selected areas. Fourier Transform Infrared spectroscopy of representative samples identified several types of polymers, predominantly polypropylene (PP), polyethylene terephthalate/polyester (PET), polyvinyl chloride (PVC), and polyethylene (PE). The Hazard Index indicates that the ecological risks of MPs are higher in industrial areas than in other areas. This study revealed that MP emission via road dust was significantly higher due to traffic, industrial, and human activities. This study has demonstrated that stormwater runoff is the primary corridor for transporting MPs from road dust to the aquatic environment of wetlands. This study also explores the impact of various factors on the motion and distribution of MPs within a simulated wetland water environment, which was explored through CFD numerical simulations, utilizing a VOF model coupled with DPM. Specifically, PET, PVC, PS, and PP MP particles were released from the inlet side to examine how factors such as MP type, size, and shape, alongside two different water flow velocities and constant air velocity, influenced their behaviour. The impact of different variables on the spatial distribution of MPs was examined through the tracking of particle positions. It was found that buoyancy and particle size significantly affect the distribution of MPs, which emerged as a key discovery. The analysis of MP particle distribution, both vertically and horizontally, indicates that under 0.3 m/s water velocity conditions, the majority of large spherical PET and PVC MPs tend to sink toward the bottom, whereas numerous smaller non-spherical particles tend to float near the surface. Larger-sized spherical particles of PP and PS are more likely to float compared to shorter ones. Furthermore, among the four types of MPs examined, PP and PS large spherical particles showed the highest mobility, particularly with rising water velocity. When it comes to size, smaller particles tend to travel longer distances because they have less mass and are more sensitive to air currents. In contrast, larger particles settle more quickly due to gravity, resulting in shorter travel distances. In summary, the utilization of the CFD approach in this study improves the ability to predict and address the dispersion of MPs in dense aquatic environments.Furthermore, this study examines the effectiveness of granular activated carbon (GAC) in removing MPs from synthetic stormwater using a fixed-bed column adsorption process. Experiments were conducted with varying MP concentrations at a given constant flow rate. Different GAC sizes were tested to assess their adsorption and removal capabilities for polystyrene (PS) microplastics sized 100-200 µm. An increase in the initial MP concentration has led to the equilibrium adsorption capacity from 9.0 mg/L to a maximum of 19.7 mg/g. GAC particles 500-600 µm and 2-3 mm could remove up to 98.7% of PS. Kinetic column models indicated that the linear forms of the models aligned well with the experimental data, explaining the adsorption process accurately. The adsorption data correlated well with the Langmuir isotherm model, suggesting an 18.51 mg/g maximum adsorption capacity. The breakthrough curve obtained was also consistent with the experimental observations. Hence, physical adsorption was significant in GAC's PS adsorption mechanisms, where zeta potential measurements indicated that the adsorption removal of PS microplastics was facilitated by electrostatic interactions between the positively charged PS and the negatively charged GAC. Thus, this study demonstrates the effectiveness of GAC-packed columns as a suitable method for removing MPs from stormwater.Lastly, this study investigated MPs' adsorption on a doped granular activated carbon (GAC) bed within a catch basin insert (CBI) using computational fluid dynamics (CFD) modelling with ANSYS Fluent. The research began with an experimental study, followed by detailed modelling of the processes occurring within the CBI. An adsorption test was performed using a fixed-bed column packed with GAC, and the adsorption percentage was calculated by fitting the CFD results to the experimental data, particularly focusing on the MPs' mass transfer capacity and adsorption rate, which determine the equilibrium concentration. Simulations were conducted using CFD to evaluate velocities, pressures, and MPs adsorption rates at a water velocity of 0.3 m/s, with a constant air velocity of 2.5 m/s. The numerical results are promising, indicating a maximum outlet speed of 0.50 m/s and efficient MPs adsorption for both polystyrene and polyethylene at the CBI outlet. The analysis of velocities, static pressure distribution, and mass variations further supports the effectiveness of this system in reducing MPs in water environments. The experimental results show strong alignment with the CFD adsorption performance for the same materials and processes. This research underscores the need for innovative solutions in plastic waste management in aquatic environments and highlights the potential of catch basin inserts as a cost-effective and environmentally beneficial approach to addressing pollution.In summary, this study highlights the presence of road dust-associated MPs in stormwater. It introduces innovative approaches to stormwater treatment by using new adsorbents and methods in the CBI system. These advancements improve the removal of different types of MPs, addressing key issues in environmental pollution and water resource management. The improved process enhances both the removal efficiency and adsorption capacity of the adsorbents, resulting in cleaner water suitable for residential, industrial, and agricultural use, or safe discharge into waterways.</p
The dark side of LLM-powered chatbots: misinformation, biases, content moderation challenges in political retrievald
No full textThis study investigates the impact of Large Language Model (LLM)-based chatbots, specifically in the context of political information retrieval, using the 2024 Taiwan presidential election as a case study. With the rapid integration of LLMs into search engines like Google and Microsoft Bing, concerns about information quality, algorithmic gatekeeping, biases, and content moderation emerged. This research aims to (1) assess the alignment of AI chatbot responses with factual political information, (2) examine the adherence of chatbots to algorithmic norms and impartiality ideals, (3) investigate the factuality and transparency of chatbot-sourced synopses, and (4) explore the universality of chatbot gatekeeping across different languages within the same geopolitical context. Adopting a case study methodology and prompting method, the study analyzes responses from Microsoft’s LLM-powered search engine chatbot, Copilot, in five languages (English, Traditional Chinese, Simple Chinese, German, Swedish). The findings reveal significant discrepancies in content accuracy, source citation, and response behavior across languages. Notably, Copilot demonstrated a higher rate of factual errors in Traditional Chinese while exhibiting better performance in Simplified Chinese. The study also highlights problematic referencing behaviors and a tendency to prioritize certain types of sources, such as Wikipedia, over legitimate news outlets. These results underscore the need for enhanced transparency, thoughtful design, and vigilant content moderation in AI technologies, especially during politically sensitive events. Addressing these issues is crucial for ensuring high-quality information delivery and maintaining algorithmic accountability in the evolving landscape of AI-driven communication platforms.</p
Rethinking Climate Extreme Events and (Im)mobility From a Place‐Based Perspective
No full textThe frequency and intensity of climate extreme events (both slow and rapid onset) are projected to increase due to human‐induced climate change. Thus, as a response, people's decisions of whether to move (mobility) or stay (immobility) from the face of climate extreme events will become increasingly important. To date, literature has established that these (im)mobilities are not linear responses to climate extreme events, and instead is a dynamic response that is also influenced by social, economic, political, cultural, environmental, and historical factors. While the theoretical understanding of climate mobilities has gained significant traction in geographical research over the past few years, gaps remain, especially in relation to the Global South. In addition, a narrative assessment of the existing knowledge on the intersection of climate extreme events and (im)mobility in the Global South is also limited. Responding to this gap in knowledge, this review paper first introduces the concept of climate mobility. Then, it examines the concept's strands (mobility and immobility) in the context of the Global South. It argues that a theoretical framing of climate mobilities underpinned by a co‐constitutive understanding of place and mobility can help to capture the plurality of climate mobility outcomes. The findings of the paper highlight how sensitivity to place offers a valuable framework for better understanding the plurality of climate mobilities in the Global South in relation to a changing climate.</p
Domestic Decay, Poetic Heritage: The Relocation of John Shaw Neilson’s Birthplace Cottage
No full textThe Australian poet John Shaw Neilson (1872–1942), an impoverished itinerant labourer who rarely had a stable dwelling during his lifetime, is memorialised through a ‘birthplace cottage' which was moved from Penola across the state border to Nhill in 1961. The relocation of heritage buildings, often involving deconstruction, rebuilding and a change of setting, inevitably reduces heritage value and precludes it from official listing. The current structure now standing in Nhill might be seen as an assemblage that has been remixed and altered over time rather than an ‘authentic' heritage building. Instead the cottage can be encountered as a kind of ‘stage set' for imagining Shaw Neilson's impoverished childhood, conditions that were borne by many working people of the late nineteenth century. In this article I explore the meanings of the cottage, arguing that it can be read as an artefact of Nhill’s ‘pioneering' settler colonial past and a celebration of a peripatetic poet, at a time when such memorialisations are being challenged.</p
Contributions to Joined Wing Aircraft Conceptual Design Considering Trim and Longitudinal Stability
No full textIn the wake of climate change and the strive to reduce emissions in commercial air transport, the traditional aircraft design goal of maximum efficiency is more relevant than ever. While radically new concepts, such as unconventional configurations, promise significant improvements, they also pose hard-to-calculate risks to aircraft designers. The presented research focusses on flight dynamics and bare-airframe handling qualities of joined wing aircraft, investigating the applicability of traditional aircraft design methods and analyzing design parameter effects on joined wing stability and control. RANS CFD and a vortex-lattice-based low order tool (VSPAERO) are evaluated concerning their ability to model joined wing longitudinal and lateral aerodynamic characteristics, as well as control derivatives by comparison to reference wind tunnel data. The results show that VSPAERO efficiently completes these tasks, as long as flow separation does not become prominent. Its speed advantage and automation features promote its application in large scale aircraft design studies, such as the parameter study presented in this thesis. Steady RANS results match wind tunnel data accurately, though high AoAs require a switch to unsteady RANS. Following the verification of VSPAERO’s applicability to joined wing configurations, handbook methods, implemented into a proven aircraft design software (AAA), and vortex-lattice-based VSPAERO are run back-to-back, analyzing both conventional, and joined wing configurations. The studies indicate very reliable performance of empirical methods for conventional configurations. It can be shown that differences in the estimation of the induced downwash at the rear wing lead to limited applicability of the empirical tools to joined wing configurations. At the heart of this thesis lies a large parameter study of 3200 different joined wing and conventional configurations. It is shown that phugoid and short period approximations developed for conventional aircraft work similarly well for joined wing configurations. The study shows that static margin and horizontal stagger are the most significant drivers of flight dynamics properties and HQ ratings in joined wing aircraft, similar to conventional configurations. The long and short-term bare-aircraft handling quality ratings according to MIL-HDBK-1797 and MIL F 8785C of conventional and joined wing configurations show similar ranges for a given parameter space of the same basic configuration. In comparison to their conventional counterparts, the joined wing designs can better alleviate the handling quality penalties associated with low static margins by increasing the horizontal stagger and, thereby, the pitch damping. The large parameter study calls for an efficient approach to the trimming of joined wing aircraft designs. As traditional trimming methods struggle to perform well with the combined duties of lift-share and longitudinal stability of the joined wing’s rear wing, a novel approach was developed within the scope of this thesis. The proposed algorithm is based on surrogate models and allows the robust and efficient trimming of joined wing configurations at a given lift coefficient and static margin. A study verifies the method’s applicability to a wide range of joined wing configurations with positive and negative static stability.</p
The Cognitive Cost of Non-Player Character Companions in Video Games
No full textThe personalisation of video games allowing the catering of individual player needs and preferences, with approaches ranging from character customisation to narratives evolving based on player choices. These traditional methods rely on explicit player input. A promising alternative is adaptation, which has emerged as a frontier for creating immersive and tailored gaming experiences. Enabled by advancements in wearable technology, adaptation can now include the use of non-intrusive hardware and machine learning to track subtle changes in player experience and cognitive state over time, allowing real-time changes of game content to occur. Non-player character (NPC) companions are a emerging avenue for implementing adaptation, given their integral role in shaping the player experience. This thesis explores how adaptive NPC companions can enhance personalisation by utilising psychophysiological data. However, current methods for designing adaptive characters lack standardisation, and there is limited validation of the data used for adaptation. To address these gaps, this thesis proposes a standardised framework for creating adaptive companions and applies it in two user experiments. The first experiment examines methods for measuring cognitive load during gameplay as a proxy for player engagement, while the second employs a Wizard-of-Oz methodology to implement and test an adaptive NPC companion using previously validated data types. The findings indicate that adaptive companions differentially impact the player experience based on player skill, highlighting the potential of this approach to meet diverse player needs and preferences. This work provides evidence for the importance of continuing research into adaptive gaming systems to further advance personalised and engaging video game experiences.</p
Deep Graph-Based Novelty Analysis in Social Media Data
No full textAnalysing novelty in social media data has attracted significant attention due to its wide-ranging applications, including market research, understanding customer preferences, personalised recommendations, and improving customer service. For example, in live-streaming e-commerce events, novel content tends to capture audience attention, making the detection of such novelty essential. From the perspective of influencers, they can learn how to create more novel content by analysing detected novelties and continuously evaluate the novelty level of their current creations. From the platform's perspective, they can gain insights into users' preferences for specific content and understand the trend-driven behaviours within the user community. From the users' perspective, in addition to enjoying user experience improvements based on novelty analysis, they can also interact on social platforms, generating a significant amount of new data. Obviously, it is crucial to discover these novelties based on social media data, recommend them, and effectively plan their utilisation. However, existing detection, recommendation, and planning algorithms fail to consider the multi-party interactions on social platforms and the unique characteristics of social media data. Therefore, in this thesis, three approaches are proposed to handle novelty analysis in social media data: detecting novelties over online video streams, making high-quality influence-aware group recommendations over online streams, and making session planning over heterogeneous social platforms. We propose a general framework, the Long-term Information REconstruction-based Model (LIREM) for online novelty detection. Specifically, we design a novel outlier detection method to filter noisy features, thereby enhancing the model’s learning capability. Based on the cleaned features, we construct an LSTM-Decoder model to predict the reconstruction error, which serves as the novelty score for each video segment. To accommodate continuously evolving data streams, we introduce an incremental model updating strategy that dynamically adapts the model without requiring full retraining. Additionally, we develop a bounding-based technique to improve detection efficiency by reducing unnecessary computations. An adaptive optimisation strategy is further proposed to dynamically select optimal bounds for filtering, ensuring efficient and effective identification of novelty candidates. Next, we propose a comprehensive framework for Influence-aware Group Recommendation (IGR) tailored for high-speed social streams. GroupGCN is proposed to mitigate data sparsity and effectively capture media dynamics, enabling superior data representation. Building on this, TGGCN-RA enhances the accuracy of group interest predictions for future time points, making it well-suited for dynamic environments. In addition, we propose the DYIC model, which captures key aspects of group behaviour, including group activeness, similarity, and their willingness to propagate items. This is achieved through a novel dynamic item-aware graph, DI2PROG, which simulates information propagation more effectively. Finally, we present the GES algorithm for edge sampling in GREG. GES preserves the distribution of the sampled dataset and tracks interest drifts within groups, enabling efficient and effective training of the TGGCN-RA model. Furthermore, we propose the Motivation-Aware Session Planning (MASP) framework for session planning across heterogeneous social platforms. MASP introduces HeterBERT, which addresses attribute-level heterogeneity by managing attribute uncertainty and capturing correlations in heterogeneous items. To improve accuracy, we develop a motivation-aware user preference prediction method that leverages the motivations behind user activities. Additionally, we design a multi-constraint session generation algorithm with optimisation strategies to ensure efficient and effective planning under various constraints. Experimental results confirm MASP's strong performance in complex social environments.</p