Interfacial Interaction Enhanced Rheological Behavior in PAM/CTAC/Salt Aqueous Solution—A Coarse-Grained Molecular Dynamics Study

Interfacial interactions within a multi-phase polymer solution play critical roles in processing control and mass transportation in chemical engineering. However, the understandings of these roles remain unexplored due to the complexity of the system. In this study, we used an efficient analytical method—a nonequilibrium molecular dynamics (NEMD) simulation—to unveil the molecular interactions and rheology of a multiphase solution containing cetyltrimethyl ammonium chloride (CTAC), polyacrylamide (PAM), and sodium salicylate (NaSal). The associated macroscopic rheological characteristics and shear viscosity of the polymer/surfactant solution were investigated, where the computational results agreed well with the experimental data. The relation between the characteristic time and shear rate was consistent with the power law. By simulating the shear viscosity of the polymer/surfactant solution, we found that the phase transition of micelles within the mixture led to a non-monotonic increase in the viscosity of the mixed solution with the increase in concentration of CTAC or PAM. We expect this optimized molecular dynamic approach to advance the current understanding on chemical–physical interactions within polymer/surfactant mixtures at the molecular level and enable emerging engineering solutions

Life cycle impact assessment of airborne metal pollution near selected iron and steelmaking industrial areas in China

Toxic metals in particulate matter pose a significant health risk to humans via inhalation and dermal exposure. Additionally, airborne pollution has negative impacts on terrestrial and aquatic quality as a result of atmospheric deposition. Iron and steelmaking industry is considered as a major contributor to airborne metal pollution. Given that China has been the largest steel producer and consumer since 1996, a detailed investigation of airborne metal pollution is required to assess the potential risks to both human health and ecosystem quality near iron and steelmaking areas in China. This study applied an environmental impact assessment approach to evaluate the freshwater ecotoxicity, terrestrial ecotoxicity, marine ecotoxicity and human toxicity caused by metal concentrations in PM1.1, PM1.1-2.1 and PM2.1-9.0 fractions. Results showed that heavy metals Cu and Zn associated with steelmaking activities were largely responsible for aquatic and terrestrial ecotoxicity. This study also found that As and Pb contamination presented the largest fraction of the impacts on human toxicity. Findings presented in this study showed that more stringent control measures are required to improve the environmental performance of the iron and steelmaking industries in China

AuPt Nanoparticles/ Multi-Walled Carbon Nanotubes Catalyst as High Active and Stable Oxygen Reduction Catalyst for Al-Air Batteries

A series of AuPt nanoparticles supported on multi-walled carbon nanotubes (AuxPt/MWNTs) catalysts with ultrafine distribution (d ≈ 3.0 nm) were synthesized for Al-air battery cathode to enhance the oxygen reduction reaction. Among them, Au0.67Pt/MWNTs catalyst with metal loading of 10.2wt.% (Au:4.1wt.%, Pt:6.1wt.%) exhibited a superior ORR catalytic activity and competitive durability to 20wt.% Pt/C catalyst. When applied as Al-air battery, appropriate increasing Au loading encourage better battery performance. Au1.68Pt/MWNTs with 8.95wt.% of Au and as little as 5.3 wt.% Pt content exhibit larger specific capacity (921 mAh g-1) and power density (146.8 mW cm-2) as well as better durability than 20 wt.% Pt/C catalyst when it is assembled as cathode in Al-air battery

Multi-Core-shell structured LiFePO4@Na3V2(PO4)3@C composite for enhanced low-temperature performance of lithium ion batteries

In this work, a multi-core–shell-structured LiFePO4@Na3V2(PO4)3@C (LFP@NVP@C) composite was successfully designed and prepared to address inferior low-temperature performance of LiFePO4 cathode for lithium-ion batteries. Transmission electron microscopy (TEM) confirms the inner NVP and outer carbon layers co-existed on the surface of LFP particle. When evaluated at low-temperature operation, LFP@NVP@C composite exhibits an evidently enhanced electrochemical performance in term of higher capacity and lower polarization, compared with LFP@C. Even at − 10 °C with 0.5C, LFP@NVP@C delivers a discharge capacity of ca. 96.9 mAh·g−1 and discharge voltage of ca. 3.3 V, which is attributed to the beneficial contribution of NVP coating. NASICON-structured NVP with an open framework for readily insertion/desertion of Li+ will effectively reduce the polarization for the electrochemical reactions of the designed LFP@NVP@C composite

Nickel oxide immobilized on the carbonized eggshell membrane for electrochemical detection of urea

Urea oxidation reaction (UOR) has been known as a viable method for renal/liver disease diagnostic detection. Here, we reported a three-dimensional (3D) nickel oxide nanoparticles dressed carbonized eggshell membrane (3D NiO/c-ESM) as a modified electrode toward urea detection. Several common physical measurements were employed to confirm its structural and morphological information. NiO/c-ESM modified electrode exhibits an outstanding performance for urea determination with a linear range from 0.05 to 2.5 mM, and limit detection of ∼20 µM (3σ). This work offered a green approach for introducing 3D nanostructure through employing biowaste ESMs as templates, providing a typical example for producing new value-added nanomaterials with urea detection

Highly efficient urea oxidation via nesting nano nickel oxide in eggshell membrane-derived carbon

Here, we reported a strategy of using an eggshell membrane to produce hierarchically porous carbon as a low-cost substrate for synthesizing a nano-nickel oxide catalyst (C@NiO), which can effectively turn biowaste—urea—into energy through an electrochemical approach. The interwoven carbon networks within NiO led to highly efficient urea oxidation due to the strong synergistic effect. The as-prepared electrode only needed 1.36 V versus reversible hydrogen electrode to realize a high efficiency of 10 mA cm–2 in 1.0 M KOH with 0.33 M urea and delivered an even higher current density of 25 mA cm–2 at 1.46 V, which is smaller than that of the porous carbon and commercial Pt/C catalyst. Benefiting from theoretical calculations, Ni(III) active species and the porous carbon further enabled the electrocatalyst to effectively inhibit the “CO2 poisoning” of electrocatalysts, as well as ensuring its superior performance for urea oxidation

Spectral and Spatial Feature Integrated Ensemble Learning Method for Grading Urban River Network Water Quality

Urban river networks have the characteristics of medium and micro scales, complex water quality, rapid change, and time–space incoherence. Aiming to monitor the water quality accurately, it is necessary to extract suitable features and establish a universal inversion model for key water quality parameters. In this paper, we describe a spectral- and spatial-feature-integrated ensemble learning method for urban river network water quality grading. We proposed an in situ sampling method for urban river networks. Factor and correlation analyses were applied to extract the spectral features. Moreover, we analyzed the maximum allowed bandwidth for feature bands. We demonstrated that spatial features can improve the accuracy of water quality grading using kernel canonical correlation analysis (KCCA). Based on the spectral and spatial features, an ensemble learning model was established for total phosphorus (TP) and ammonia nitrogen (NH3-N). Both models were evaluated by means of fivefold validation. Furthermore, we proposed an unmanned aerial vehicle (UAV)-borne water quality multispectral remote sensing application process for urban river networks. Based on the process, we tested the model in practice. The experiment confirmed that our model can improve the grading accuracy by 30% compared to other machine learning models that use only spectral features. Our research can extend the application field of water quality remote sensing to complex urban river networks

Trace element and isotopic analyses of raw and commercial beehive products

Thesis by publication.Includes bibliographical references.Chapter 1. Introduction -- Chapter 2. Research method and approach -- Chapter 3. Trace elements in the environment and European honey bees (Apis mellifera) -- Chapter 4. Trace elements in the environment and the Australian native bee (Tetragonula carbonaria) -- Chapter 5. Trace elements in global honeys and their use in the identification of the geographical origin of honey -- Chapter 6. Discussion -- Conclusion -- Appendices.Bees have existed on the planet for more than 100 million years and their relationship with early humans dates to the Pliocene period (5.3 million to 2.6 million years ago). As a result of recent human industrial activity, bees' foraging environments have been subject to variable levels of contamination. Consequently, bees have been used as bio-indicators to assess anthropogenic contamination. The primary product of bees is honey, a valued and popular human food. However, honey can be subject to adulteration via the addition of sugars and misleading labelling in relation to its geographic origin. Some international studies have evaluated beehive products for their authenticity and use in environmental monitoring. This study builds on existing approaches by applying multiple geochemical analytical techniques to link environmental sources to the concentrations and compositions of both raw and commercial beehive products.This thesis uses trace element analysis and isotopic techniques to: (a) evaluate the use of bees as environmental bio-indicators; (b) investigate the quality of Australian and global honey; and (c) explore a valid method to authenticate the geographic origin of honey. Trace element analysis of beehive products demonstrates that measurements of As, Pb, Mn and Zn in two bee species (Apis mellifera, European honey bees and Tetragonula carbonaria, an Australian native bee species) correlates to co-located soils and dusts. Furthermore, Pb isotopic composition analysis verifies that the contamination found in bees can be attributed to a range of local sources, specifically, current mining activities in the city of Broken Hill (NSW), former leaded petrol depositions and geogenic background according to their locations.Carbon isotopic ratios (13C/12C) and trace elements are used in this thesis to identify if commercial honey samples (from Africa, Asia, Europe, North America and Oceania) have been adulterated by synthetic sugars derived from C-4 plants. These techniques are also used to test whether the stated geographic information on the honey samples is correct. The carbon isotopic analysis demonstrates that the adulteration practice of adding C-4 sugars remains a common problem in the Australian and global market, with a 17 % and 27 % adulteration rate, respectively. Manganese, P, K and Sr concentrations are shown to be the most important trace elements for distinguishing Australian from international honey.Overall, this thesis contributes to the growing body of research that evaluates the effectiveness of bees and their respective products for use as bio-indicators of current and legacy trace element contamination. The research findings also demonstrate that isotopic composition and trace element concentrations can be used to authenticate and establish the geographic origin of honey, which has benefits for bona fide honey producers and consumers alike.Mode of access: World wide web1 online resource (xii, 217 pages) colour illustrations, colour map

Spectral and Spatial Feature Integrated Ensemble Learning Method for Grading Urban River Network Water Quality

Urban river networks have the characteristics of medium and micro scales, complex water quality, rapid change, and time–space incoherence. Aiming to monitor the water quality accurately, it is necessary to extract suitable features and establish a universal inversion model for key water quality parameters. In this paper, we describe a spectral- and spatial-feature-integrated ensemble learning method for urban river network water quality grading. We proposed an in situ sampling method for urban river networks. Factor and correlation analyses were applied to extract the spectral features. Moreover, we analyzed the maximum allowed bandwidth for feature bands. We demonstrated that spatial features can improve the accuracy of water quality grading using kernel canonical correlation analysis (KCCA). Based on the spectral and spatial features, an ensemble learning model was established for total phosphorus (TP) and ammonia nitrogen (NH3-N). Both models were evaluated by means of fivefold validation. Furthermore, we proposed an unmanned aerial vehicle (UAV)-borne water quality multispectral remote sensing application process for urban river networks. Based on the process, we tested the model in practice. The experiment confirmed that our model can improve the grading accuracy by 30% compared to other machine learning models that use only spectral features. Our research can extend the application field of water quality remote sensing to complex urban river networks