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Travel Efficiency Investigation: Unravelling Local and Global Insights via Taxi Trajectory Analysis
No full textTransportation issues have a significant impact on people's lives because they spend a significant amount of time commuting for either daily needs or entertainment. These issues can be associated with travel time, longer travel distance, and/or fuel consumption. Due to the global positioning system (GPS) enabled devices installed in these vehicles, enormous amounts of trajectory data have been collected over the last decade from travelling vehicles such as cars, buses, and taxis, among others. This data provides an excellent opportunity to trace vehicle movements in fine spatiotemporal granularity. Moreover, this data tackles many of the traffic problems, including bottleneck identification. Identifying traffic bottlenecks is essential in traffic planning it also aids in the prevention of traffic congestion. Traffic congestion begins with congested road segments in key locations and spreads to other parts of the urban road network, causing additional congestion. The problem investigated in this thesis is analysing the road network travel efficiency locally and globally to reduce travel times, minimising fuel consumption, energy demands, and making better use of existing infrastructure. In much of the current literature, the focus is often on either a global analysis, which identifies the most efficient trip destinations, or a local analysis, which identifies the cause of traffic anomalies or congestion. However, it is necessary to consider both of these scales in order to gain a nuanced understanding. Specifically, it is crucial to quantify the extent to which each individual road segment affects travel efficiency, both at a local and a global scale. In order to provide a comprehensive understanding of urban traffic data, this thesis integrates both local and global analyses. In local analysis, we dive deep into each trajectory, much like deep-sea exploration, to uncover reasons for inefficiencies by examining all combined road segments. Then we extend the analysis globally to understand the behaviour of each part on road networks and how it effects on other road parts. The local analysis of the road network explores the measuring of the travel efficiency for each single trajectory trip across numerous origin-destination (OD) pairs in an entire city. Moreover, the consideration of a low travel efficiency path rises a question of exactly which road segment is causing low efficiency. So, local analysis aims to measure the travel efficiency for each path. Furthermore, the local analysis provides the road segment inside a particular path that is responsible for low travel efficiency. In contrast, a small set of road segments that affect globally in the congestion problem is known as global analysis in this thesis. The global analysis seeks to identify a major source of traffic congestion. The global analysis provides some important opportunities for furthering the understanding of the congestion value for each edge in the road network and provides the top-k congested edges that influence the greatest number of other edges in the road network with the highest influence value recorded. The highest number of the influence values proves evidence of the global congestion effect in the entire road network.</p
Biodegradable Seed Encapsulant for Aerial Reforestation using UAV’s
No full textThe use of Uncrewed Aerial Vehicles (UAVs) is the future of reforestation practices, with the potential to enhance targeted delivery, increase efficiency, reduce costs, and provide a flexible system capable of operating in challenging environments. Current technologies often employ seed balls, which are spherical objects generally consisting of clay, soil, and other additives to support the growth and germination of seeds. However, while these can be effective, they face an array of challenges, including timely manufacturing and transport, inaccurate delivery, susceptibility to environmental disturbance, limited support for long-term establishment, and vulnerability to pests, all of which greatly limit their efficacy.
The use of hydrogels was identified as a novel encapsulant for UAV dispersal and may provide distinct advantages over commercial counterparts including surface adherence to improve targeted delivery and providing greater support for seeds through a nutrient-rich hydrogel matrix as a seed encapsulant. Previous research into hydrogel coatings and applications has focused on, coating the seed with a thin layer of material, applying the hydrogel to the soil before delivery, application onto established seedlings. Whereas in this study, research focused on encapsulating the entire seed in a nutrient rich encapsulant. Investigating the encapsulants shall greatly enhance understanding of the viability of hydrogel based encapsulants in a reforestation setting and assess the entire usability of the chosen natural and biodegradable materials in terms of production, deployment, germination and growth across a range of species. Hence, the project assesses the development and application efficacy of biodegradable hydrogel seed encapsulants capable of being deployed via UAVs for applications in environmental reforestation.
The research was therefore divided into four areas, which formed the basis for the investigation and structure of this investigation. Firstly, assessment of the encapsulant’s requirements, including both seed and mechanical requirements, as well as material analysis to justify research pathways. Secondly, provision of encapsulant formulations to enable deployment and growth, this includes assessment of mechanical properties to determine attributes and suitability to achieve the aims of the project. Third, performance of encapsulant (i) upon deployment via UAVs for reforestation and/or (ii) upon automated deployment in agricultural applications. Lastly, simulation of field assessment of encapsulant performance in laboratory environment which will include identified native as well as common agricultural seeds.
Through rheological, flow rate index, compression, and deployment tests, various hydrogel encapsulants successfully met the preparation and deployment criteria. The extended stabilisation times of many PHM encapsulants was crucial in determining efficacy. Initial encapsulants (Chapters 3 and 4) using psyllium husk mucilage (PHM) required measuring the stabilisation point of the material, with yield stress as the defining analysis. Adding dextran altered stabilisation time and reduced yield stress, while sodium alginate increased gelation time but enhanced yield stress. Therefore, encapsulants containing sodium alginate (ALG) did not meet processing window requirements and were not used in the growth and germination testing phase. Other key findings included the assessment of extrusion force (EF) for PHM samples namely P-2, D-4.1, and D-5.1. Dextran included samples (D-4.1 and D-5.1) demonstrated reduced extrusion force, enhancing feasibility for aerial deployment. The research identified D-4.1 and D-5.1 as effective mechanical modifiers, with D-5.1 achieving a yield stress of 156 Pa after 24 hours, outperforming other samples. The chapter also established extrusion force requirements for scaled-up models, determining maximum forces of 119N, 128N, and 137N for P-2, D-4.1, and D-5.1, respectively. These findings contribute to the selection of optimal material combinations for future applications in biodegradable gel systems. Unlike the PHM encapsulants, the bentonite-based encapsulants (Chapters 5 and 6) had no stabilisation time concerns, as ALG and carboxymethylcellulose (CMC) could be transported in solubilised form. Defining clay concentration was crucial, with rheology used to determine a minimum viscosity/yield stress for the materials. The study concluded that a minimum of 50 w/v% bentonite clay in a 1 w/v% polymer solution (CMC/ALG) was necessary for extrusion. Deployment testing also concluded that an encapsulant containing 50 w/v% bentonite clay successfully met the deployment criteria.
Additional additives were also prepared in the form of CMC-Citric Acid (CA) particles, prepared by the dehydration esterification process, and ALG(-CMC) microbeads prepared in a w/o emulsion with calcium chloride. These additives were prepared for the purpose of water and nutrient retention, in which similarly developed particles have shown great success in soil additive applications. The study mainly focused on swelling ratio (SR) in which the microparticles far outperformed the CMC-CA particles. Moreover, the CMC-CA process often produced uneven films even at reduced temperatures (40°C), resulting in material losses due to uneven crosslinking, which again favoured the use of microparticles. It is important to note though that research has produced varying results on the effectiveness of particles given their size, and as such larger particles (CMC-CA) might have a greater prolonged effect on plant growth than microbeads despite lower SR. However, the microparticle size could be increased by altering the polymer or surfactant (Tween® 80) concentration or changing the process to extrusion based which would be more environmentally friendly as there is no oil waste phase. The study concluded that the optimal ALG-CMC microbead was developed using 100mL of 4% w/v polymer solution containing an ALG-CMC ratio of 1:1, in 500mL oil phase containing 1% v/v Tween® 80. These particles had an 80% inclusion size of 196μm to 577μm and had an average SR of 48.4 which was significantly higher than beads without the inclusion of CMC (19.6).
Growth and germination testing concluded that the developed encapsulants were unsuitable for Acacia Stenophylla at drier conditions (50% Field Capacity (FC)). However, at 90% FC, the use of the bentonite-CMC encapsulants improved the development of seedlings. In the case of Cymbopogon refractus seeds, both PHM and bentonite-based encapsulants were not usable without further development. The PHM gel was entirely unsuitable resulting in seedling death due to gel degradation, and CMC showed poor germination and growth. However, for A. stenophylla growth the P-2 encapsulant at both 50% and 90% FC improved both total mass and germination rates compared to C-Surface. At 90% FC, P-2 also showed comparable root, shoot, and leaf development to C-Planted, indicating its potential for use in high-moisture environments with fast-growing species.
Despite extensive testing, future studies could overlook the agricultural seeds if the focus is reforestation as no correlation was observed between agricultural and non-agricultural seeds. For agricultural seeds, it was concluded that seed selection had a greater impact on encapsulant effectiveness than soil moisture. For beans, the PHM-DEX blend was the best encapsulant for growth, while for cucumbers, the bentonite-CMC encapsulant with water retention beads and fertilisers (CMC-AB) performed best. However, encapsulant performance was affected by soil moisture, with PHM encapsulants ineffective at 50% FC for cucumbers but comparable to CMC-AB at 90% FC. This indicates that encapsulants must be optimised for specific conditions to maximise seed usage efficiency. Moreover, encapsulants largely reduced germination for all seeds, which was concluded to be a result of the encapsulant creating a barrier that prevented emergence. As such, despite certain improvements in growth parameters, the overall results suggest limited justifiability for large-scale agricultural use of both the developed ALG/CMC and PHM gels without overcoming the barrier properties of the gel.
Therefore, it was concluded that while hydrogel encapsulants were effective in an agricultural setting, they performed poorly for arid/semi-arid species, with germination largely impeded by the encapsulants, particularly for smaller seeds. The research indicates that while hydrogels have great potential for creating a material that can be easily prepared on-site and effectively deployed from a 3m height, even onto hard surfaces, their effectiveness in promoting germination and growth was rather poor. However, the inclusion of hydrogel bead additives resulted in significant improvements in growth, suggesting that future investigations addressing the highlighted challenges may hold potential for success.</p
Forecasting Solar Power Time Series: Strategies For Multi-Modal Data Fusion, Feature Relevance, and Sparse Data Management
No full textThe forecasting of solar photovoltaic power (SPVP) is a significant challenge. Solar is the least reliable renewable energy, as it depends on the weather, among other things. However, it is also one of the cheapest sources if it can be harnessed, particularly during daylight hours when people work and use electricity. The ultimate aim of forecasting solar power using deep learning (DL) techniques is to enable the aggregated use of solar power stations by day, supplemented by alternative sources of electricity whenever solar energy is forecast to fall below a particular level. The more accurate the solar power predictions, the better the use and supply of this valuable resource.
This thesis proposes an SPVP forecasting method that applies DL methodologies using real data from multiple solar power stations. SPVP time series data is complex and characterized by variable, dynamic, and multi-dimensional attributes. Consequently the research in this thesis has to address various challenges, predominantly stemming from the inherent characteristics of SPVP data. The multifaceted nature of these challenges includes data variability and non-stationarity, where the influence of diverse environmental conditions, seasonal variations, and geographical factors introduces significant fluctuation and unpredictability into the data. To address this variability, forecasting models that have the capability to adapt and predict based on changing patterns are needed. Additionally, the multi-dimensional nature of the inputs required for precise forecasting poses another hurdle.
Accurate SPVP generation forecasting models need to integrate multiple types of data, not only historical generation data but also exogenous variables such as weather conditions. Compounding these challenges is the issue of data availability. Many solar installations, especially new ones or those in less-studied regions, do not have the extensive historical data crucial for training robust forecasting models. Traditional machine learning methods often prove inadequate, as they are limited by their dependence on extensive data manipulation and feature engineering, so the requirements for deep domain expertise—capabilities are not always available. These methods struggle to capture and utilize the dynamic interplay between the factors affecting SPVP generation, and this underscores the need for innovative approaches that can navigate these complexities more effectively.
% This thesis addresses these challenges by developing and applying advanced DL models to enhance the accuracy and efficiency of SPVP generation forecasting.
Motivated by the limitations of existing forecasting approaches, this research explores innovative DL techniques capable of handling the complexities of SPVP data. To address the challenges posed by data variability, we introduce an aggregated SPVP model with a Wavelet-based-coefficient (Wcoeff) approach that is used for univariate data decomposition to denoise the data. The Wcoeff model redefines the wavelet transform (WT) application to streamline feature extraction. This approach provides a scalable and accurate forecasting solution by mitigating computational complexity yet retaining temporal relationships.
Exogenous data is then integrated to enhance forecasting accuracy, and the research addresses the multi-dimensional nature of these inputs through the innovations of the Multilevel Data Fusion and Neural Basis Expansion Analysis (MF-NBEA) model. This model represents a pivotal advance in using DL for SPVP forecasting. Indeed, understanding the most important lagged variables influencing the generation is crucial for refining forecasting models. Given the high dimensionality and evolving nature of the data to be used, a dynamic approach to lagged variable selection and modeling is required. The research develops dynamic feature selection that adjusts to changing conditions and highlights the most predictive variables over time. This adaptability ensures models remain accurate and relevant, even as the underlying data patterns shift.
Finally, we introduce a novel methodology that integrates learned knowledge from multiple source domains to address the critical challenges in forecasting accuracy when data is scarce. This innovative transfer learning approach marks a significant departure from traditional single-source forecasting methods. By leveraging the wealth of data available from already established solar power installations, the new methodology enhances the forecasting model's ability to predict solar power output in new locations or locations with limited historical data. The essence of the novelty is in the strategic fusion of knowledge from across multiple domains, utilizing advanced techniques such as average weights fusion and evolutionary optimization based fusion.
This thesis makes a significant contribution to the field of DL models and renewable energy forecasting by providing scalable, efficient, and adaptable models. The findings underscore the potential for advanced DL techniques to navigate the complexities of SPVP time series data and offer insights that will facilitate the broader integration of solar energy into the power grid. This work opens avenues for future research to enhance model interpretability, explore cross-domain applications of transfer learning, and further optimize models for real-time forecasting applications.\\</p
An Adaptable and Open-source Approach to Merging Satellite and Gauge Data over Australia
No full textThe estimation of rainfall through gridded spatial analyses is important for water resource management and water-related disaster risk mitigation, as well as for being an input into scientific models. The current rainfall analysis used by the Bureau of Meteorology (BOM), Australian Gridded Climate Dataset (AGCD) rainfall, relies purely on gauge data, leading to high uncertainty over gauge-sparse regions such as interior parts of the country. Utilising additional sources of rainfall information to form blended datasets would be highly valuable but the advancement of operational rainfall analyses has been hindered by a lack of research in the development and comparison of blended datasets over Australia, and in the verification of such datasets over gauge-sparse areas.
This thesis addresses this research gap by developing multiple correction and merging techniques that are tailored to the Australian context, and subsequently comparing them against each other to determine which is the optimal method, and whether they are still effective over gauge-sparse areas. This thesis aims to produce the optimal blended rainfall analysis for operational use in Australia, providing users globally with an adaptable technique for assimilating in-situ and gridded data.
All methods investigated demonstrated the capability to generate a blended analysis that maintains similar performance to AGCD over gauge-dense regions but with a notably more realistic representation of rainfall and generally improved performance over gauge-sparse regions.
In particular, adapting the current algorithm used for creating AGCD, Optimal Interpolation (OI) also known as Statistical Interpolation (SI), to incorporate a satellite estimate as the background field was appealing for operational usage. This was because of its ability to be adapted for different datasets and variables, its relatively low computational requirements, and its removal of the need for a preliminary correction. An open-source Python implementation of the algorithm in two dimensions, which offers adaptability to other domains and datasets, was developed.
The research completed, along with the provision of an open-source and adaptable algorithm for creating a gridded analysis, is an important contribution to the knowledge of the value of blended datasets, offering a strong motivation to the use of a blended dataset over Australia for operational purposes. The adaptable and open-source nature also ensures applicability over other regions and for other geospatial variables.</p
Extraction, Investigation, and Potential Application of Baijiu Jiuzao Glutelin
No full textJiuzao, the primary solid by-product from baijiu (a traditional Chinese spirit) distillation, contains a high protein content due to the high boiling point of these proteins. To enhance the added value of Jiuzao, utilizing its proteins presents an ideal solution. Although various methods have been developed for protein extraction, higher yields still need to be explored. Pulsed electric field (PEF) is a novel non-thermal extraction method known for its high extraction efficiency. In this study, a PEF-assisted extraction method was used to improve protein extraction efficiency from Jiuzao. Jiuzao glutelin (JG) was fractionally extracted, and this study is the first to explore the species type, secondary structure, and functional characteristics of JG. Based on JG, bioactive peptides with antioxidant activity were prepared under protease hydrolysis conditions. An AAPH-induced oxidative damage model using Spragure Dawley (SD) rat was constructed to evaluate the in vivo antioxidant activity of these peptides. For the first time, quercetin (QUE), resveratrol (RES), rhein (RH), and riboflavin were (RIB) carriers were constructed using JG-conjugated polysaccharide (Pullulan, Dextran, Carboxymethyl chitosan, Pectin, and Arabic gum) Maillard products. The incorporation of JG enhances the added value of Jiuzao and mitigates the environmental burden caused by its large-scale production.
PEF was used as a supplementary technique for the fractional extraction of Jiuzao proteins (albumin, globulin, gliadin, and glutelin). The extraction efficiency was enhanced by 13.81% compared to the ultrasound-assisted method (0.92 mg/mL) using with 83 pulses, a field strength of 3.26 kV/cm, and a Jiuzao/distilled water ratio of 3:20. Subsequently, it was found that 59.16% of JG was derived from sorghum. Extracted JG showed excellent foaming and foam stability, as well as water and oil holding characteristics. Additionally, in vitro antioxidant assays showed that JG exhibited ABTS, DPPH, hydroxyl radical scavenging capacity, ferrous chelating, and oxygen radical absorbance capacity (ORAC). Furthermore, extracted JG exhibited favourable cytocompatibility in Caco-2 and CCD 841 CON cells.
The optimal conditions for JG hydrolysis were explored using various proteases, including alkaline protease, neutral protease, papain, pepsin, trypsin, flavor, and complex proteases, to generate bioactive peptides. The findings indicated that the peak hydrolysis rate of JG attained 80.7% under the following parameters: a pepsin-to-JG ratio of 0.378, a hydrolysis temperature at 41°C, a pH of 1.40, and a hydrolysis duration of 300 minutes. The in vivo antioxidant activity of JG hydrolyzed peptides, including Asp-Arg-Glu-Leu (DREL), Ala-Tyr-Ile (AYI), and Val-Asn-Pro (VNP), was measured using an AAPH-primed SD rat model. All three peptides activated the Nrf2/Keap1-p38/PI3K-MafK antioxidant pathway and the downstream antioxidant enzymes, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), and hemoxyrubinase-1 (HO-1), were examined at the gene and protein levels to enhance antioxidant activity in rats.
Polysaccharides such as Pullulan, Dextran (Dex), Carboxymethyl chitosan (CTS), Pectin (Pec), and Gum Arabic (GA) were used to modify JG through Maillard reaction. The optimal reaction conditions were as follows: for CTS-JG, the ratio was 2:1, the reaction time was 180 minutes, and the pH was 7 (CTS-JGI-2); for Pullulan-JG, the ratio was 2:1, the reaction time was 180 minutes, and the pH was 11 (PJC-2); for Dex-JG, the ratio was 1:1, the reaction time was 120 minutes, and the pH was 7; for Pec-JG, the ratio was 4:1, the reaction time was 180 minutes, and the pH was 7; and for GA-JG, the ratio was 2:1, the reaction time was 120 minutes, and the pH was 11. These conjugates showed significant improvements in solubility, foaming, foam stability, viscosity, and thermal stability.
CTS-JGI-2 was used to construct oil-in-water nanoemulsion for delivering RES and quercetin (QUE) because of its ideal stability, properties, and activities. The results showed that the CTS-JGI-2 stabilized oil-in-water nanoemulsion improved the encapsulation efficiency of RES and QUE (RES was 80.96%, QUE was 93.13%) and enhanced stability during the simulated digestion process (RES was 73.23%, QUE was 77.94%) through hydrogen bonding, anion, sigma, and donor compared to native JG. Furthermore, PJC-2 was combined with enteric-coated materials (polymethacrylic acid, hydroxypropyl methylcellulose phthalates, cellulose acetate phthalates, and D-mannitol) to construct a microencapsulated delivery system for rhein (RH). The encapsulation efficiency of RH in the four enteric-PJC-2 bilayer microcapsules (70.03±3.24%~91.08±4.78%) was significantly higher than that of PJC-2 microcapsules (61.84±0.47%). The antioxidant activity and stability of RH in microcapsules were increased (ABTS, 49.7% -113.93%; DPPH，40.85%-101.82%; ferrous reducing power, 62.32%-126.42%; ferrous chelate, 70.58%-147.20%) compared to free RH under in vitro simulated digestion and extreme environmental conditions.
This study successfully created a method to achieve the maximum extraction of JG from Jiuzao (with 83 pulses, a field strength of 3.26 kV/cm, and a Jiuzao/distilled water ratio of 3:20). The utilization of JG encompassed two successful approaches which are the hydrolysis for the generation of functional peptides and conjugation for the delivery of functional components. The incorporation of JG significantly elevated the added value of Jiuzao, preventing potential wastage.</p
Additive Manufacturing of Certified Aircraft Interior Components
No full textThis PhD presents a novel and comprehensive study on an end-to-end methodology for rapid on-demand design and production of aircraft interior replacement parts. The objective of the methodology is to ensure a rapid turnaround for replacement parts, which is currently a critical challenge faced by aircraft operators due to significant costs from replacement parts being unavailable or requiring extensive lead times. Additionally, the high variability and bespoke nature of aircraft interiors necessitate a flexible and on-demand approach.
Additive manufacturing emerges as the ideal production form for this methodology due to its rapid nature and manufacturing flexibility. This study showcases the utilisation of an additive manufacturing production system, exemplified by the “Aircraft Interiors Certification Solution” developed by Stratasys, explicitly tailored for certified aircraft interior parts. To underscore the real-world applicability, two case studies, including an aircraft seat armrest cover and tray table, are used for development and validation purposes throughout the study.
A detailed literature review is presented, which covers a range of multidisciplinary topics relevant to the study. The lack of research on a methodology for rapid on-demand design and production of aircraft replacement parts is highlighted. Additional gaps and challenges considered in this study include designing parts remotely without access to original part data or high-end data capture equipment.
The end-to-end methodology is developed, which entails establishing the key processes, components, characteristics, and stakeholders. The key processes, including geometry capture, re-engineering, certification, and production, are integrated into a high-level framework and refined into a detailed and structured methodology. Furthermore, the incorporation of additive manufacturing is complemented by the integration of knowledge-based engineering and Industry 4.0 technologies, such as model-based systems engineering and digital twin methods, to support a rapid and on-demand methodology.
The critical relationships of the methodology are meticulously investigated to establish the dependencies between the components of the methodology and the flow of information from end to end. A novel geometry capture method is developed that is tailored to remote access and the resources available in an aircraft maintenance context. Key areas investigated for relationships include geometry capture, certification, and operations. Establishing critical relationships ensures optimal and efficient utilisation of the methodology while providing a holistic and comprehensive outlook.
The performance of the methodology is assessed using time as the key performance indicator, which is crucial for ensuring a rapid turnaround. Performance measurement is conducted in terms of the elapsed time, with results showing approximate lead times of 22 hours for simple interior parts and 47 hours for complex interiors. The methodology is shown to outperform traditional manufacturing by reducing lead time by up to 68% using conservative estimation approaches.
Additionally, the performance parameters of the methodology and their impact are also established. A total of 6 parameters that relate to the operational conditions of the methodology and 20 parameters relating to the design of the interior part are identified. The design-based parameters are grouped into five distinct categories, which are shown to have a significant impact on the elapsed time of the methodology, with the printing volume category contributing over 50% relative to the other four categories. Performance management aspects are also highlighted, which demonstrate how various strategies and techniques can be used to enhance or maintain the performance of the methodology.
This PhD offers significant novel contributions to literature by addressing key research gaps and developing solutions tailored to the aircraft industry. The development of a rapid on-demand methodology for the design and production of aircraft interior replacement parts serves to minimise the unavailability of replacement parts and any associated costs for operators. Ultimately, this methodology fosters continuous and sustainable aircraft operations. Moreover, this research extends beyond immediate benefits, where broader outcomes include, but are not limited to, facilitating the expanded utilisation of additive manufacturing within the aircraft industry, enabling accelerated certification processes, and opening avenues for applying this methodology to primary and secondary aircraft parts.</p
Characterisation of Manufacturability and Mechanical Response of Additively Manufactured Strut Elements and Lattice Structures
No full textAdditive Manufacturing (AM) provides an opportunity for design innovation and sophisticated geometry compared to traditional manufacturing methods. More specifically, Metal Additive Manufacturing (MAM) using Laser-Based Powder Bed Fusion (LB-PBF) allows fabrication with various fusible metal alloys, including light alloys, superalloys, and tools steels. LB-PBF is suited to high-value engineered applications, including lattice structures for medical implants, aerospace components and custom tooling. However, the MAM process has inherent manufacturing defects such as porosity, dimensional accuracy, and surface texture, resulting in uncertainty of manufacturability and associated structural performance. Prior work in this field has focussed on either the geometric properties of strut elements or the mechanical response of entire lattice structures. This research proposes an individual systematic procedure to characterise and optimise the effect of manufacturing artefacts and defects on the structural response and efficiency associated with mechanical properties of AM strut elements.</p
Development of Metal Oxide-Based Adsorbents for the Removal of Dyes From Contaminated Water
No full textThe preservation of pure water resources, essential for life on Earth, is under severe threat due to extensive contamination from industrial activities. These activities introduce a mix of both organic and inorganic substances into water bodies. Dyes, known for their carcinogenic properties, present a substantial hazard to both human and aquatic life. Industries, particularly those in textiles and printing sectors, often discharge wastewater containing concentrated dyes, underscoring the need for effective removal strategies. This study introduces an innovative approach, utilising Strontium-doped neodymium manganite, Nd0.6Sr0.4MnO3 (NSMO) as an adsorbent for removing organic contaminants, particularly the Fast Green (FG) dye, from wastewater. Synthesized via a solid-state reaction route, NSMO displays unique orthorhombic polycrystalline properties and a dense particle growth pattern. The findings demonstrate a remarkable 99% removal efficiency of FG dye from a 100 mg/L solution using just 0.05 g of NSMO within 60 minutes. The higher removal efficiency of NSMO is due to the presence of Mn, which exists as trivalent (Mn+3) as well as tetravalent manganese ion (Mn+4). Due to mixed valence, the sites with three positive and four positive charges serve as highly efficient adsorption sites for the anionic FG dye. Thus, electrostatic interactions between adsorbent and adsorbate occur and help in the presently observed effective adsorption process. This result underscores the exceptional adsorptive potential of NSMO, marking a shift from the traditional focus on its photocatalytic properties to its effectiveness as an adsorbent. To address the low adsorption capacity of materials for FG dye, the study presents a novel adsorbent, ZnOS+C, synthesized by modifying zinc peroxide with sulfur and activated carbon. Batch adsorption experiments highlight ZnOS+C exceptional potential, achieving a maximum adsorption capacity of 238.28 mg/g for FG dye within 120 minutes over a wide pH range. The Freundlich isotherm model suggests multilayered adsorption on the outer surface of ZnOS+C, while kinetics studies align with the intraparticle diffusion model. Moreover, ZnOS+C shows good removal efficiency in up to 5 successive adsorption-desorption studies where the removal effectiveness of ZnOS+C decreased by no more than 14.2% even after five cycles relative to their initial adsorption capacity.
This study also explores the removal of Crystal Violet (CV) dye, a highly toxic substance commonly found in textile industries using surface modification of zinc peroxide (ZnO2) with the sodium salt of dioctyl sulfosuccinate. ZnO2, which was inactive for the uptake of CV dye from wastewater, is made highly active by surface modification with the help of sodium dosusate. The long hydroscopic chain contains an aliphatic hydrocarbon chain of sodium docusate and a polar part of sodium docusate creates the SO3– group over the surface of ZnO2. Further, the presence of xanthan gum forms a reverse micelles system around the CV dye present in water. This micelle formation results in the uptake of CV dye from water by ZnSD. Besides, this electrostatic interaction between the SO3– group of ZnSD and the cationic nitrogen of CV dye also enhances the adsorption capacity. Also, the zeta-potential studies indicate that the potential of ZnSD decreases from −15 to −60 mV as we increase the pH from 3 to 9, which suggests a higher negative charge on adsorbent at higher pH and results in more electrostatic interaction between ZnSD and CV dye at higher pH. Surface modification significantly enhances ZnO2 adsorption efficiency for CV, achieving over 99.5% removal. The adsorption capacity reaches 123 mg/g, emphasizing the effectiveness of the modified ZnO2. Optimal physiochemical parameters, including pH, contact time, initial dye concentration and adsorbent dosage, were determined for maximal adsorption.
Furthermore, the study adopts a green chemistry approach to synthesize zinc oxide (ZnO) nanoparticles using lychee peel extract for removing Congo Red (CR) dye from wastewater. The synthesized ZnO NPs could effectively remove >98% of CR dye from wastewater within 120 min of contact time at a wide pH range from 2 to 10. The primary mechanism involved in removing dye was the electrostatic interaction between ZnO adsorbent and CR dye. The antimicrobial performance of synthesized ZnO NPs was found to show 34% inhibition against Bacillus subtilis (ATCC 6538), 52% against Escherichia coli (ATCC 11103), 58% against Pseudomonas aeruginosa (ATCC 25668) and 32% against Staphylococcus aureus (ATCC 25923) using well diffusion assay. ZnO demonstrates a suitable anti-bacterial property over both gram-positive and gram-negative pathogenic bacteria. Overall, the green synthesized method for developing ZnO NPs shows promising and significant anti-bacterial performance and is a highly potential adsorbent for removing CR dye from wastewater.
Lastly, the study investigates the synthesis of pure and doped zinc oxide (ZnO) nanoparticles, incorporating manganese (Mn), silver (Ag) and iron (Fe) dopants for Congo Red (CR) dye removal. The batch adsorption investigation revealed adsorption efficiencies of 99.4% for CR dye at an optimal dose of 0.03 g/30 ml for Mn-doped ZnO at a solution pH of 2. The adsorption capacity of each of the synthesized materials was found to be in order Mn-doped ZnO (232.5 mg/g) > Ag-doped ZnO (222.2 mg/g) > pure ZnO (212.7 mg/g) > Fe-doped ZnO (208.3 mg/g). Both pseudo-second-order kinetics model and the Langmuir isotherm model accurately explained the adsorption behaviors of CR dye. As such, van der Waal interactions, H-bonding and electrostatic interaction were found to be the adsorption mechanisms responsible for dye removal. In addition, the desorption–regeneration investigation indicated the successful reuse of the exhausted Mn-doped ZnO material for five cycles of CR dye adsorption with an efficiency of 83.1%.
In summary, these studies collectively represent pioneering and innovative approaches to wastewater treatment, introducing novel adsorbents and methodologies as significant advancements in the efficient removal of various toxic dyes, thereby addressing a crucial aspect of environmental pollution and water resource management. Different novel metal oxide-based adsorbents are developed by doping, surface modification and functionalization by looking at the chemical structure of dyes to have better chemical interactions between developed adsorbents and targeted dyes. This results in a significant increase in the removal efficiencies and adsorption capacity of the adsorbents.</p
Circular Economy Platforms: Enablers and Organizers of Economic and Environmental Value Creation in the Circular Economy
No full textThe transition from the traditional linear economy model to the circular economy (CE) has been designed to transform global production/service-consumption cycles into more sustainable ones. However, the transition has been incremental, and a better understanding of enablers and accelerators of economic and environmental value creation in the CE are needed. To shift a model of the economy from linear to circular, all levels, and actors of the socioeconomic system, from individual consumers to organizations and institutions, should participate in the new economic activities. Digital platforms' adaptable technological architecture and matching capabilities have enabled the development of circular resource and service markets. Therefore, platforms have become important elements in enabling the resource circulation and the implementation of new circular business models. However, regardless of the rapid platform-based market development, the academic literature and theory building regarding platforms as the CE transition enablers remain in infancy. Therefore, this dissertation investigates how platforms facilitate economic and environmental value creation in the CE context and accelerate the circular transition.
Based on qualitative research, this dissertation combines a systematic literature review and two multiple case studies of 20 firms in Europe. This study's findings construct a conceptual and thematic map of contemporary CE platform research and empirically identify the key socio-technological enablers and meta-organizational mechanisms that support the economic and environmental value creation in the CE. Additionally, the platform-enabled circular business models are conceptualized as the CE platforms. Overall, this dissertation contributes to and advances the contemporary platform and CE streams of literature by explaining how CE platforms can be used to enable and organize CE activities. For managers, the dissertation provides actionable strategies for implementing platform-enabled circular business models.</p
Quantification of Naturally Occurring Oligosaccharides in Goat’s Milk and Goat’s Milk-Based Infant Formula and their Impact on Infant Gut Health
No full textRecently, infant nutrition has gained attention as growing data suggest disruptions during the early stages of life can have impacts that last till later in life. Breastfeeding is recognized as the “golden standard” for infant nutrition due to the various health benefits for both mother and child. However, breastfeeding may not always be viable, and mothers will have to rely on infant formulas (IF), yet IFs are perceived negatively and are reported to lead to childhood diseases and obesity. Reasons for such adverse outcomes are suggested to be due to the compositional difference with human milk (HM), particularly that of milk oligosaccharides (HMO), a bioactive compound that is shown to promote the growth of beneficial Bifidobacterium, protection against infections, and reduce inflammation. To meet the nutritional gap, modern IFs are often fortified with man-made oligosaccharides such as fructooligosaccharides (FOS) and galactooligosaccharides (GOS) yet their efficacies in improving infant gut health remain uncertain.</p