Adsorption and Separation Characteristics of Graphene Oxide

Graphene 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

The relationship between obstructive sleep apnea and osteoarthritis: evidence from an observational and Mendelian randomization study

ObjectivesObstructive sleep apnea (OSA) and osteoarthritis (OA) are common comorbidities that significantly impact individuals’ quality of life. However, the relationship between OSA and OA remains unclear. This study aims to explore the connection between OSA and OA and evaluate causality using Mendelian randomization (MR).MethodsA total of 12,454 participants from the National Health and Nutrition Examination Survey (2009–2012) were included. OSA participants were identified based on self-reported interviews. The association between OA and OSA was assessed through multivariable logistic regression analysis. A two-sample MR was employed to investigate the relationship between OSA and OA, specifically hip OA and knee OA, utilizing the inverse variance-weighted (IVW) approach.ResultsBased on the observational study, individuals with OSA exhibited a higher risk of OA (OR = 1.67, 95% CI = 1.40–1.98). IVW demonstrated that the risk of OA (OR = 1.13, 95% CI: 1.05–1.21, p = 0.001), hip OA (OR = 1.11, 95% CI: 1.04–1.18, p = 0.002), and knee OA (OR = 1.08, 95% CI: 1.02–1.14, p = 0.005) was significantly associated with OSA. Reverse MR analyses indicated no effect of OA on OSA. Additionally, body mass index (BMI) was found to mediate 36.9% (95% CI, 4.64–73.2%, p = 0.026) of the OSA effects on OA risk.ConclusionThe cross-sectional observational analysis unveiled noteworthy associations between OSA and OA. Meanwhile, findings from the MR study provide support for a causal role

Enhanced graphitic domains of unreduced graphene oxide and the interplay of hydration behaviour and catalytic activity

Previous studies indicate that the properties of graphene oxide (GO) can be significantly improved by enhancing its graphitic domain size through thermal diffusion and clustering of functional groups. Remarkably, this transition takes place below the decomposition temperature of the functional groups and thus allows fine-tuning of graphitic domains without compromising with the functionality of GO. By studying the transformation of GO under mild thermal treatment, we directly observe this size enhancement of graphitic domains from originally 40 nm2 to 200 nm2 through an extensive transmission electron microscopy (TEM) study. Additionally, we confirm the integrity of the functional groups during this process by comprehensive chemical analysis. A closer look into the process confirms the theoretically predicted relevance for the room temperature stability of GO. We further investigate the influence of enlarged graphitic domains on the hydration behaviour of GO and catalytic performance of single-atom catalysts supported by GO. Surprisingly, both, the water transport and catalytic activity are damped by the heat treatment. This allows us to reveal the critical role of water transport in laminated 2D materials as catalysts

Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes

Graphene oxide (GO) intercalated with transition metal oxides (TMOs) has been investigated for optimal supercapacitance performance. However, attaining the best performance requires conducting numerous experiments to find an optimal material composition. This raises an important question; can resource consumption associated with extensive experiments be minimized? Here, we combine the machine learning (ML)-based random forest (RF) model with experimentally observed X-ray photoelectron spectroscopy (XPS) data to construct the complete chemical analysis dataset of Co(Ⅲ)/Co(Ⅱ) ratio for thermally synthesized Co-rGO supercapacitor electrodes. The ML predicted dataset could be further coupled with other experiment results, such as cyclic voltammetry (CV), to establish a precise model for predicting capacitance, with ML coefficient of determination (R) value of 0.9655 and mean square error value of 6.77. Furthermore, the error between predicted capacitance and experimental validation is found to be less than 8%. Our work indicates that RF can be used to predict XPS data for the TMO-GO system, thereby reducing experimental resource consumption for materials analysis. Moreover, the RF-predicted result can be further utilized in experimental and computational analysis

Performance Optimization in UAV-Assisted Wireless Powered mmWave Networks for Emergency Communications

In this paper, we explore how a rotary-wing unmanned aerial vehicle (UAV) acts as an aerial millimeter wave (mmWave) base station to provide recharging service and radio access service in a postdisaster area with unknown user distribution. The addressed optimization problem is to find out the optimal path starting and ending at the same recharging point to cover a wider area under limited battery capacity, and it can be transformed to an extended multiarmed bandit (MAB) problem. We propose the two improved path planning algorithms to solve this optimization problem, which can improve the ability to explore the unknown user distribution. Simulation results show that, in terms of the total number of served user equipment (UE), the number of visited grids, the amount of data, the average throughput, and the battery capacity utilization level, one of our algorithms is superior to its corresponding comparison algorithm, while our other algorithm is superior to its corresponding comparison algorithm in terms of the number of visited grids

Climate policy uncertainty and corporate investment: evidence from the Chinese energy industry

In recent years, with the increasing attention paid to climate risks, the changes in climate policies are also more full of uncertainties, which have brought tremendous impact to economic entities, including companies. Using the dynamic threshold model, this study investigates the nonlinear and the asymmetric effect of climate policy uncertainty on Chinese firm investment decisions with panel data of 128 Chinese energy-related companies from 2007 to 2019. The empirical findings indicate that the influence of climate policy uncertainty on firm investment is significantly nonlinear. Overall, climate policy uncertainty is not apparently related to corporate investments in the high-level range, while it negatively affects the investments in the low-level range. In addition, to be more specific, the negative impact of climate policy uncertainty on the mining industry is tremendous, while the influence on the production and supply of electricity, heat, gas, and water sector is remarkably positive. The results of this study could help the company managers and policymakers to arrange appropriate related strategies under different climate policy conditions

Development of a Throughflow-Based Simulation Tool for Preliminary Compressor Design Considering Blade Geometry in Gas Turbine Engine

Gas turbine engines are highly intricate machines, and every component of them is closely associated with one another. In the traditional engine developing process, vast experiment tests are needed. To reduce unnecessary trials, a whole gas turbine engine simulation is extremely needed. For this purpose, a compressor simulation tool is now developed. Considering the inherent drawbacks of 0D analysis and 3D CFD (Computational Fluid Dynamics) calculation, the 2D throughflow method is an indispensable tool. Based on the circumferential average method (CAM), 3D Navier–Stokes is transformed into a 2D method. One phenomenon arising is that the lack of description about circumferential motion leads to the need for the blade force modeling in compressor simulation. Previous models are based on the assumption that flow passes through the average stream surface without entropy increasing, which is not applicable in the CAM. An improved model is proposed based on the result analysis from CAM and NUMECA method in a linear cascade. Whereafter, the model is applied in a highly loaded and low-speed fan, which has been tested for its performance characteristics. Utilizing the new model, the error of the adiabatic efficiency between CAM and experiment decreases from 4.0% to 1.0% and the accuracy of the mass flow, and pressure ratio remains unchanged. The time involved in the CAM simulation is nearly 70 times faster than that of the 3D simulation

Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes

Graphene oxide (GO) intercalated with transition metal oxides (TMOs) has been investigated for optimal supercapacitance performance. However, attaining the best performance requires conducting numerous experiments to find an optimal material composition. This raises an important question; can resource consumption associated with extensive experiments be minimized? Here, we combine the machine learning (ML)-based random forest (RF) model with experimentally observed X-ray photoelectron spectroscopy (XPS) data to construct the complete chemical analysis dataset of Co(Ⅲ)/Co(Ⅱ) ratio for thermally synthesized Co-rGO supercapacitor electrodes. The ML predicted dataset could be further coupled with other experiment results, such as cyclic voltammetry (CV), to establish a precise model for predicting capacitance, with ML coefficient of determination (R2) value of 0.9655 and mean square error value of 6.77. Furthermore, the error between predicted capacitance and experimental validation is found to be less than 8%. Our work indicates that RF can be used to predict XPS data for the TMO-GO system, thereby reducing experimental resource consumption for materials analysis. Moreover, the RF-predicted result can be further utilized in experimental and computational analysis.Dali Ji acknowledges UNSW Tuition Fee Scholarship and Australian Research Council Discovery Project DP180101436. Vanesa Quintano acknowledges the funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska Curie Grant Agreement No. 101066462.Peer reviewe

Supplementary data of the article Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes

Figures S1-S9. -- Spreadsheet S1-S5.1-s2.0-S0008622323005870-mmc1.docx1-s2.0-S0008622323005870-mmc2.xlxsPeer reviewe

Understanding water transport through graphene-based nanochannels via experimental control of slip length

The water transport along graphene-based nanochannels has gained significant interest. However, experimental access to the influence of defects and impurities on transport poses a critical knowledge gap. Here, we investigate the water transport of cation intercalated graphene oxide membranes. The cations act as water-attracting impurities on the channel walls. Via water transport experiments, we show that the slip length of the nanochannels decay exponentially with the hydrated diameter of the intercalated cations, confirming that water transport is governed by the interaction between water molecules and the impurities on the channel wall. The exponential decay of slip length approximates non-slip conditions. This offers experimental support for the use of the Hagen-Poiseuille equation in graphene-based nanochannels, which was previously only confirmed by simulations. Our study gives valuable feedback to theoretical predictions of the water transport along graphene-based channels with water-attracting impurities