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(2011)Journal ArticleThick anatase films were fabricated on graphite substrates using a method of anodic aqueous electrophoretic-deposition using oxalic acid as a dispersant. Thick films were subsequently fired in air and in nitrogen at a range of temperatures. The morphology and phase composition were assessed and the photocatalytic performance was examined by the inactivation of Escherichia coli in water. It was found that the transformation of anatase to rutile is enhanced by the presence of a graphite substrate through reduction effects. The use of a nitrogen atmosphere allows higher firing temperatures, results in less cracking of the films and yields superior bactericidal performance in comparison with firing in air. The beneficial effects of a nitrogen firing atmosphere on the photocatalytic performance of the material are likely to be a result of the diffusion of nitrogen and carbon into the TiO2 lattice and the consequent creation of new valence band states.
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(2008)Journal ArticleFire activity has varied globally and continuously since the last glacial maximum (LGM) in response to long-term changes in global climate and shorter-term regional changes in climate, vegetation, and human land use. We have synthesized sedimentary charcoal records of biomass burning since the LGM and present global maps showing changes in fire activity for time slices during the past 21,000 years (as differences in charcoal accumulation values compared to pre-industrial). There is strong broad-scale coherence in fire activity after the LGM, but spatial heterogeneity in the signals increases thereafter. In North America, Europe and southern South America, charcoal records indicate less-than-present fire activity during the deglacial period, from 21,000 to ∼11,000 cal yr BP. In contrast, the tropical latitudes of South America and Africa show greater-than-present fire activity from ∼19,000 to ∼17,000 cal yr BP and most sites from Indochina and Australia show greater-than-present fire activity from 16,000 to ∼13,000 cal yr BP. Many sites indicate greater-than-present or near-present activity during the Holocene with the exception of eastern North America and eastern Asia from 8,000 to ∼3,000 cal yr BP, Indonesia and Australia from 11,000 to 4,000 cal yr BP, and southern South America from 6,000 to 3,000 cal yr BP where fire activity was less than present. Regional coherence in the patterns of change in fire activity was evident throughout the post-glacial period. These complex patterns can largely be explained in terms of large-scale climate controls modulated by local changes in vegetation and fuel load.
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(2021)ThesisImprovements in liquid lithium-ion battery electrolytes using of metal organic frameworks (MOFs) as a functional decoration on polymer membrane separators were investigated using a combination of experimental and theoretical methods. Zirconium-based MOF UiO-66 was introduced to the polymer support using the mixed matrix membrane (MMM) method. The method allowed the one-step manufacture of a robust, mechanically pliable polymer-MOF membrane composite of high MOF loading. MOF-MMMs imparted improved electrochemical behaviours such as a widened potential operating window, near-unity transference number, and increased presence of solid electrolyte interphase (SEI) components crucial to battery performance. Density functional theory (DFT) calculations were also performed to provide insights regarding electrolyte solvation in the presence of MOF. A simple dip-coating technique was utilised to modify the surface of the MOF-MMMs with polydopamine (PDA) for further improvement of the electrochemical properties. Increased transference numbers, as well as stability during rate cycling, were observed with the resulting PDA-MMM owing to the improved electrode/electrolyte interface. However, surface analyses using x-ray photoelectron spectroscopy (XPS) showed that there are reduced amounts vital SEI components compared to the original MOF-MMM support. The last section further explores the versatility of UiO-66 and tackled the preparation of gel polymer electrolytes (GPEs) decorated with UiO-66 via phase inversion technique. Using the phase inversion method, the fabricated GPE contained pores from both polymer substrate and the intrinsic pores of the 3D nanomaterial for improvement of electrochemical properties. It was demonstrated in this work that the MOF GPE is equally inert and suitable in ether or carbonate-based electrolytes. Overall, this study demonstrated the versatility of UiO-66 metal organic frameworks for use as a functional nanofiller for electrolyte membranes. With the use of inexpensive membrane fabrication methods, the composites obtained are viable for lithium-metal battery applications. Similarly, insights drawn can provide a springboard towards future study of MOF-based electrolytes.
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(2002)Conference Paper
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(2022)ThesisTwo-dimensional transition metal dichalcogenide (TMD) nanocrystals (NCs) exhibit unique optical and electrocatalytic properties. However, the growth of uniform and high-quality NCs of monolayer TMD remains a challenge. Until now, most of them are synthesized via solution-based hydrothermal process or ultrasonic exfoliation method, in which the capping ligands introduced from organic solution often quench the optical and electrocatalytic properties of TMD NCs. Moreover, it is difficult to homogeneously disperse the solution-based TMD NCs on a substrate for device fabrication since the dispersed NCs can easily aggregate. Here, we put forward a novel CVD method to grow closely-spaced TMD NCs and explored the growth mechanism and attempts on the size control. Their applications acting as electrocatalysts and adhesion layer for Au film deposition have been also well displayed. Through the whole chapters of this thesis, the following aspects are highlighted: 1. MoS2 and other TMD nanocrystals have been grown on the c-plane sapphire. The surface oxygen vacancies determine the density of TMD nanocrystals. The MoS2 nanocrystals demonstrate excellent hydrogen evolution reaction and surface-enhanced Raman scattering performance owing to the abundant edges. 2. Deep insights into the growth of MoS2 nanograins have been explored. The surface step edges and lattice structures of the underlying sapphire substrates have a significant influence on the growth behaviors. The step edges could modulate the aggregation of MoS2 nanograins to form unidirectional triangular islands. The Raman spectra of MoS2 demonstrate a linear relationship with the crystal size of MoS2. 3. The orientation of sapphire substrate has an of importance effect on the critical size of MoS2 nanocrystals. The MoS2 nanocrystals have the smallest size on the r-plane sapphire, besides, the MoS2 on r-plane sapphire demonstrates the sintering-resistance feature, which is attributed to the edge-pinning effect when MoS2 edges are anchored on the sapphire surface. 4. The MoS2 nanocrystalline layer was utilized as the adhesion layer for Au film depositing on a sapphire substrate. The Au films on MoS2 displayed superior transmittance and electrical conductivity as well as outstanding thermal stability, which lay in the strong binding of Au film with MoS2 nanocrystalline layer.
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(2023)ThesisLow melting point post-transition metals are a class of materials that melt below 330 ℃. Their low melting points offer distinctive physical and chemical properties that are yet to be fully explored. The study of properties of low melting point post-transition metals in various organic or inorganic systems enable insights into the field of biomaterials. The first stage of this thesis reports the synthesis of a liquid metal-polymer system which has the potential for patterning of liquid metals on different substrates. A sonication method is utilised for dispersion of eutectic mix of gallium and indium (EGaIn) particles into a photo-polymer. After characterisation of this inorganic-organic system, patterning of the dispersion is demonstrated through conductive tracks on flexible and rigid substrates. This method provides straightforward patterning of conductive EGaIn liquid metal traces. In the second stage, the physical, mechanical and biocompatibility properties of another liquid metal system known as Field’s metal (a eutectic mix of indium, tin and bismuth) was explored. Field’s metal (FM) has a melting point of 61 ℃. Two eutectic mixes of FM and FM-with zinc were synthesised and compared. A proof-of-concept application was demonstrated for the two biocompatible materials shaped into body implants for clinical applications. The removal of these implants from within a tissue mimic was demonstrated by utilising a mild non-contact heat source. This approach was shown to negate the need for invasive surgery for removal of implants from the body to potentially improving the health of patients. In the third stage, another liquid metal system was investigated based on gallium as the reaction media. Magnesium/bismuth intermetallic was formed on the surface of gallium through selective solidification. The intermetallic system, with a very high intrinsic melting point, formed at low temperature and hexagonal shaped domains of intermetallics on the surface were established. This inorganic system was then studied to show favourable antibacterial properties in comparison to pure gallium control. The work was a successful experimental demonstration on the possibility of the usage of liquid metal media for the formation of various mixes and intermetallic species in mild thermal conditions. Altogether, the outcomes of this successful PhD thesis will provide fundamental insights into surface chemistry of liquid metals with potential benefits in biomedical applications.
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(2018)ThesisGraphene oxide is a single layer of carbon atoms with decorated oxygen functional groups. Stacked monolayers in the laminate form create an interlayer space of sub-nanometer scale with oxygenated functional group to attract water molecules, and graphitic domains to allow frictionless flow of water molecules and achieve maximum efficiency of water transportation. The research reported herein is aimed to understand and explore characteristics of the diffusion-dependent mass transportation across an array of cascading nanochannels confined by graphene oxide laminates at sub-nanometer level. This dissertation has 6 Chapters. Chapter 1 is the introduction and Chapter 2 reports the recent progress in graphene oxide for mass transport application. Chapter 3 discusses efforts of engineering the channel confinement, which is represented by the interlayer spacing in between graphene oxide laminates. By adjusting the fundamental factors of graphene oxide suspension, the interlayer spacing can be controlled at 0.7 to 0.8 nm. Based on the engineered interlayer spacing, separation of vaporous mixture by graphene oxide membrane is studied in Chapter 4. Numerical description of nanochannels enclosed by graphene oxide monolayers is determined by time lag analysis. The feature of ethanol vapor transportation with the support of water vapor is revealed, showing accelerated transportation of non-permeable matter, which enriches the existing knowledge. A geometrical model of graphene oxide membrane for vapor separation was established and analyzed. In Chapter 5, adsorption and intercalated of molecules and solvated ions are studied and proved as a size-dependent enlargement of graphene oxide nanochannels. Carriers such as water and ethanol are used for transporting ions and molecules into graphene oxide slits. Taking the adsorption into consideration, permeation of vaporous substances through adsorbed graphene oxide membrane is investigated in Chapter 6. The research initiates researching crystallization of adsorbed matters in graphene oxide interlayer structure. A simplified model was directed to predict the water vapor permeation behavior of intercalated graphene oxide membrane. Such efforts not only lead to a better understating of graphene oxide membrane for gas separation but also give a hint of spatially efficient matter transport in achieving excellent electrochemical devices with graphene oxide components.
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(2023)ThesisTumbling ball milling is a critical comminution process in materials and mineral processing industries. It is an energy intensive process with low energy efficiency. It is important that ball mills and the milling process are properly designed and operated. To achieve this, models at different scales are needed to provide accurate prediction of mill performance under various conditions. This study aimed to develop a combined discrete element method (DEM) and machine learning (ML) modelling framework to link mill design, operation parameters with particle flow and mill efficiency. A scale-up model was developed based on DEM simulations to link mill size ratio, rotation rate, and filling level with power draw and grinding rate. Then, an ML model using the Support Vector Machine (SVM) algorithm was developed to predict the angle of repose (AoR) and collision energy based on various operation conditions. The ML model was trained by the data generated from the DEM simulations and able to predict the AoR and collision energy. In the process monitoring, an artificial neural network (ANN) was firstly proposed to predict internal particle flow properties of a rotating mill based on acoustic emission (AE) signal generated using the DEM. Main features of AE signals and power draw were fed into the ANN to predict flow properties such as particle size distributions, collision energy distribution and filling levels. Further, a convolution neural network (CNN) was used to replace the ANN to extract more efficient features of AE signals non-linearly based on different local frequency ranges in a ball milling process partially filled with steel balls and grinding particles. Last, a physics-informed ML model was developed based on continuous convolution neural network (CCNN) to learn particle contact mechanisms provided by DEM data at different rotation speeds. The ML model coupled with DEM simulation can accelerate DEM simulation to accurately predict particle flow in a long time series. In summary, this work has demonstrated that combining physics-based numerical models DEM to ML models not only improves the efficiency and accuracy of predictions of complicated processes but also provides more insight to the process and makes predictions more transparent.