Rate capability of graphite materials as negative electrodes in lithium-ion capacitors

The lithium-ion exchange rate capability of various commercial graphite materials are evaluated using galvanostatic charge/discharge cycling in a half-cell configuration over a wide range of C-rates (0.1 similar to 60C). The results confirm that graphite is capable of de-intercalating stored charge at high rates, but has a poor intercalating rate capability. Decreasing the graphite coating thickness leads to a limited rate performance improvement of the electrode. Reducing the graphite particle size shows enhanced C-rate capability but with increased irreversible capacity loss (ICL). It is demonstrated that the rate of intercalation of lithium-ions into the graphite is significantly limited compared with the corresponding rate of de-intercalation at high C-rates. For the successful utilisation of commercially available conventional graphite as a negative electrode in a lithium-ion capacitor (LIC), its intercalation rate capability needs to be improved or oversized to accommodate high charge rates.

Fabrication of Nb2O5 nanosheets for high-rate lithium ion storage applications

Nb2O5 nanosheets are successfully synthesized through a facile hydrothermal reaction and followed heating treatment in air. The structural characterization reveals that the thickness of these sheets is around 50 nm and the length of sheets is 500~800 nm. Such a unique two dimensional structure enables the nanosheet electrode with superior performance during the charge-discharge process, such as high specific capacity (~184 mAh.g-1) and rate capability. Even at a current density of 1 A.g-1, the nanosheet electrode still exhibits a specific capacity of ~90 mAh.g-1. These results suggest the Nb2O5 nanosheet is a promising candidate for high-rate lithium ion storage applications.

Core-leaf onion-like carbon/MnO2 hybrid nano-urchins for rechargeable lithium-ion batteries

A hybrid nano-urchin structure consisting of spherical onion-like carbon and MnO2 nanosheets is synthesized by a facile and environmentally-friendly hydrothermal method. Lithium-ion batteries incorporating the hybrid nano-urchin anode exhibit reversible lithium storage with superior specific capacity, enhanced rate capability, stable cycling performance, and nearly 100% Coulombic efficiency. These results demonstrate the effectiveness of designing hybrid nano-architectures with uniform and isotropic structure, high loading of electrochemically-active materials, and good conductivity for the dramatic improvement of lithium storage.

SnO2 decorated graphene nanocomposite anode materials prepared via an up-scalable wet-mechanochemical process for sodium ion batteries

A facile and up-scalable wet-mechanochemical process is designed for fabricating ultra-fine SnO2 nanoparticles anchored on graphene networks for use as anode materials for sodium ion batteries. A hierarchical structure of the SnO2@graphene composite is obtained from the process. The resultant rechargeable SIBs achieved high rate capability and good cycling stability.

Two-dimensional tin disulfide nanosheets for enhanced sodium storage

Sodium-ion batteries (SIBs) are considered as complementary alternatives to lithium-ion batteries for grid energy storage due to the abundance of sodium. However, low capacity, poor rate capability, and cycling stability of existing anodes significantly hinder the practical applications of SIBs. Herein, ultrathin two-dimensional SnS2 nanosheets (3-4 nm in thickness) are synthesized via a facile refluxing process toward enhanced sodium storage. The SnS2 nanosheets exhibit a high apparent diffusion coefficient of Na+ and fast sodiation/desodiation reaction kinetics. In half-cells, the nanosheets deliver a high reversible capacity of 733 mAh g-1 at 0.1 A g-1, which still remains up to 435 mAh g-1 at 2 A g-1. The cell has a high capacity retention of 647 mA h g-1 during the 50th cycle at 0.1 A g-1, which is by far the best for SnS2, suggesting that nanosheet morphology is beneficial to improve cycling stability in addition to rate capability. The SnS2 nanosheets also show encouraging performance in a full cell with a Na3V2(PO4)3 cathode. In addition, the sodium storage mechanism is investigated by ex situ XRD coupled with high-resolution TEM. The high specific capacity, good rate capability, and cycling durability suggest that SnS2 nanosheets have great potential working as anodes for high-performance SIBs. © 2015 American Chemical Society.

Fabrication of symmetric supercapacitors based on MOF-derived nanoporous carbons

Nanoporous carbon (NPC) materials with high specific surface area have attracted considerable attention for electrochemical energy storage applications. In the present work, we have designed novel symmetric supercapacitors based on NPC by direct carbonization of Zn-based metal-organic frameworks (MOFs) without using an additional precursor. By controlling the reaction conditions in the present study, we synthesized NPC with two different particle sizes. The effects of particle size and mass loadings on supercapacitor performance have been carefully evaluated. Our NPC materials exhibit excellent electrochemical performance with a maximum specific capacitance of 251 F g-1 in 1 M H2SO4 electrolyte. The symmetric supercapacitor studies show that these efficient electrodes have good capacitance, high stability, and good rate capability.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

The learning capability of construction organisations engaged in collaborative contracting - Literature Review [industry report]

Background Value for money (VfM) on collaborative construction projects is dependent on the learning capabilities of the organisations and people involved. Within the context of infrastructure delivery, there is little research about the impact of organisational learning capability on project value. The literature contains a multiplicity of often un-testable definitions about organisational learning abilities. This paper defines learning capability as a dynamic capability that participant organisations purposely develop to add value to collaborative projects. The paper reports on a literature review that proposes a framework that conceptualises learning capability to explore the topic. This work is the first phase of a large-scale national survey funded by the Alliancing Association of Australasia and the Australian Research Council. Methodology Desk-top review of leading journals in the areas of strategic management, strategic alliances and construction management, as well as recent government documents and industry guidelines, was undertaken to synthesise, conceptualise and operationalise the concept of learning capability. The study primarily draws on the theoretical perspectives of the resource-based view of the firm (e.g. Barney 1991; Wernerfelt 1984), absorptive capacity (e.g. Cohen and Levinthal 1990; Zahra and George 2002); and dynamic capabilities (e.g. Helfat et al. 2007; Teece et al. 1997; Winter 2003). Content analysis of the literature was undertaken to identify key learning routines. Content analysis is a commonly used methodology in the social sciences area. It provides rich data through the systematic and objective review of literature (Krippendorff 2004). NVivo 9, a qualitative data analysis software package, was used to assist in this process. Findings and Future Research The review process resulted in a framework for the conceptualisation of learning capability that shows three phases of learning: (1) exploratory learning, (2) transformative learning and (3) exploitative learning. These phases combine both internal and external learning routines to influence project performance outcomes and thus VfM delivered under collaborative contracts. Sitting within these phases are eight categories of learning capability comprising knowledge articulation, identification, acquisition, dissemination, codification, internationalisation, transformation and application. The learning routines sitting within each category will be disaggregated in future research as the basis for measureable items in a large-scale survey study. The survey will examine the extent to which various learning routines influence project outcomes, as well as the relationships between them. This will involve identifying the routines that exist within organisations in the construction industry, their resourcing and rate of renewal, together with the extent of use and perceived value within the organisation. The target population is currently estimated to be around 1,000 professionals with experience in relational contracting in Australia. This future research will build on the learning capability framework to provide data that will assist construction organisations seeking to maximise VfM on construction projects.

Raman microscopy of formamide-intercalated kaolinites treated by controlled-rate thermal analysis technology

Raman spectroscopy of formamide-intercalated kaolinites treated using controlled-rate thermal analysis technology (CRTA), allowing the separation of adsorbed formamide from intercalated formamide in formamide-intercalated kaolinites, is reported. The Raman spectra of the CRTA-treated formamide-intercalated kaolinites are significantly different from those of the intercalated kaolinites, which display a combination of both intercalated and adsorbed formamide. An intense band is observed at 3629 cm-1, attributed to the inner surface hydroxyls hydrogen bonded to the formamide. Broad bands are observed at 3600 and 3639 cm-1, assigned to the inner surface hydroxyls, which are hydrogen bonded to the adsorbed water molecules. The hydroxyl-stretching band of the inner hydroxyl is observed at 3621 cm-1 in the Raman spectra of the CRTA-treated formamide-intercalated kaolinites. The results of thermal analysis show that the amount of intercalated formamide between the kaolinite layers is independent of the presence of water. Significant differences are observed in the CO stretching region between the adsorbed and intercalated formamide.

Hydrazine-hydrate intercalated halloysite under controlled-rate thermal analysis conditions

The thermal behaviour of halloysite fully expanded with hydrazine-hydrate has been investigated in nitrogen atmosphere under dynamic heating and at a constant, pre-set decomposition rate of 0.15 mg min-1. Under controlled-rate thermal analysis (CRTA) conditions it was possible to resolve the closely overlapping decomposition stages and to distinguish between adsorbed and bonded reagent. Three types of bonded reagent could be identified. The loosely bonded reagent amounting to 0.20 mol hydrazine-hydrate per mol inner surface hydroxyl is connected to the internal and external surfaces of the expanded mineral and is present as a space filler between the sheets of the delaminated mineral. The strongly bonded (intercalated) hydrazine-hydrate is connected to the kaolinite inner surface OH groups by the formation of hydrogen bonds. Based on the thermoanalytical results two different types of bonded reagent could be distinguished in the complex. Type 1 reagent (approx. 0.06 mol hydrazine-hydrate/mol inner surface OH) is liberated between 77 and 103°C. Type 2 reagent is lost between 103 and 227°C, corresponding to a quantity of 0.36 mol hydrazine/mol inner surface OH. When heating the complex to 77°C under CRTA conditions a new reflection appears in the XRD pattern with a d-value of 9.6 Å, in addition to the 10.2 Ĺ reflection. This new reflection disappears in contact with moist air and the complex re-expands to the original d-value of 10.2 Å in a few h. The appearance of the 9.6 Å reflection is interpreted as the expansion of kaolinite with hydrazine alone, while the 10.2 Å one is due to expansion with hydrazine-hydrate. FTIR (DRIFT) spectroscopic results showed that the treated mineral after intercalation/deintercalation and heat treatment to 300°C is slightly more ordered than the original (untreated) clay.