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| 1 | Integrated Bi2O3 nanostructure modified with Au nanoparticles for enhanced photocatalytic activity under visible light irradiation显示文摘An integrated Bi_2O_3(i-Bi_2O_3) nanostructure with a particle size 10 nm inducing agglomerated structure were synthesized by dissolving bismuth nitrate pentahydrate in diethylene glycol at 180 ℃ with post heat treatment.The prepared i-Bi_2O_3 nanostructures were employed for the construction of Au/i-Bi_2O_3 composite system and characterized by X-ray diffraction pattern,UV-visible diffuse reflectance spectroscopy(DRS),and transmission electron microscopy,X-ray photoemission spectroscopy(XPS) and Energy dispersive X-ray spectroscopy(EDS).The i-Bi_2O_3 nanostructure and Au/i-Bi_2O_3 composite system were found to exhibit high photocatalytic activity than commercial Bi_2O_3 in decomposing salicylic acid under visible light irradiation.The high catalytic activity of i-Bi_2O_3 nanostructure was deduced to be caused by charge separation facilitated by electron hopping between the particles within the integrated structure and space-charge separation between i-Bi_2O_3 and Au.The charge separation behavior in i-Bi_2O_3 nanostructure was further bolstered by comparing the measured.OH radical produced in the solution with i-Bi_2O_3 nanostructure,commercial Bi_2O_3 and Au/i-Bi_2O_3 composite which readily react with 1,4-terephthalic acid(TA) inducing 2-hydroxy terephthalic acid(TAOH) that shows unique fluorescence peak at 426 nm.The space-charge separation between i-Bi_2O_3 and An was confirmed by measuring the electron spin resonance(ESR) spectra. | Hankwon Lim Sher Bahadur Rawal | 2017 | Progress in Natural Science:Materials International2017，27，3： | 5 |
| 2 | Process simulation and economic analysis of reactor systems for perfluorinated compounds abatement without HF effluent显示文摘 | Boreum Lee Sunggeun Lee Ho Young Jung Shin-Kun Ryi Hankwon Lim | 2016 | Frontiers of Chemical Science and Engineering2016，10，4： | 1 |
| 3 | A novel combined multi-battery dataset based approach for enhanced prediction accuracy of data driven prognostic models in capacity estimation of lithium ion batteries显示文摘To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost importance.However,battery degradation is a complex challenge involving many electrochemical reactions at anode,separator,cathode and electrolyte/electrode interfaces.Also,there is significant effect of the operating conditions on the battery degradation.Various machine learning tech-niques have been applied to estimate the capacity and State of Health of lithium ion batteries to ensure reliable operation and timely maintenance.In this paper,we study the Gaussian Process Regression(GPR)and Support Vector Machine(SVM)model-based approaches in estimating the capacity and State of Health of batteries.Bat-tery capacity and State of Health estimations are carried out using GPR and SVM models and the predictions comparatively studied for accuracy based on RMSE values.The prediction accuracy is further compared with re-spect to single sensor and multi sensor data.Further,a combined multi battery data set model is used to improve the prediction accuracy.Combining the data of multiple batteries with similar operating conditions for training a model resulted in higher prediction accuracy. | Vijay Mohan Nagulapati Hyunjun Lee DaWoon Jung SalaiSargunan S Paramanantham Boris Brigljevic Yunseok Choi Hankwon Lim | 2021 | Energy and AI2021，5，3： | 0 |
| 4 | Machine learning based fault detection and state of health estimation of proton exchange membrane fuel cells显示文摘In fuel cells, chemical energy is directly converted into heat and electricity without any emissions which makes them an attractive substitute for various energy needs. Fuel cells have high energy conversion ratio and highpower densities which make them suitable for automotive applications. However, these fuel cell systems suffer with low reliability and durability as system components develop faults during operation resulting in degradation and diminished system performance. In this context, fault detection and fault mitigation strategies are being extensively developed. Diagnostic approaches like electrochemical impedance spectroscopy, cyclic voltammetry, and galvanostatic analysis offer a truthful representation of the State of Health (SOH) of the fuel cell. However, these approaches are intrusive and require pausing the operation of the fuel cell effecting its integrity. Machine learning based fault detection and SOH estimation is a non-intrusive approach where a mapping function is established between the indicators and SOH. The SOH of a fuel cell can be correlated to the patterns in sensor signals or indicators. Indicators that influence SOH are cell voltages, current density distribution, impedance spectra, acoustic emission and magnetic fields. Developing an accurate fault detection and state estimation technique through data driven machine learning approaches will allow corrective measures to avoid irreversible faults and improve the reliability and durability of fuel cells. | Vijay Mohan Nagulapati SShiva Kumar Vimalesh Annadurai Hankwon Lim | 2023 | Energy and AI2023，12，2： | 0 |
| 5 | Landau damping of twisted waves in Cairns distribution with anisotropic temperature显示文摘The consideration of orbital angular momentum of an electric field(twisted mode)is applied to the kinetic theory of plasma.The linearized Vlasov–Poisson equation is solved for the anisotropic thermal distributed bi-Maxwellian and Cairns distributions of electrons to obtain the damping rates of twisted waves.The dispersion relation and Landau damping of Langmuir twisted modes are obtained.The presence of twisted modes opens up two more possibilities in Landau damping and dispersion relations.This may generate a mixture with ion sound waves.It seems to play the role of a control parameter of Landau damping. | Sunggeun LEE Hankwon LIM | 2021 | Plasma Science and Technology2021，23，8： | 0 |
| 6 | Prognostics and health management of alkaline water electrolyzer: Techno-economic analysis considering replacement moment显示文摘Recently,considerable attention has been paid to the installation of renewable energy capacity to mitigate global CO_(2) emissions.H_(2) produced using water electrolysis and renewable energy is regarded as a clean energy carrier,generating electricity without CO_(2) emissions,called‘Green H 2’.In this paper,a prognostics and health man-agement model for an alkaline water electrolyzer was proposed to predict the load voltage on the electrolyzer to obtain the state of health information.The prognostics and health management model was developed by training historical operating data via machine learning models,support vector machine and gaussian process regression,showing the root mean square error of 1.28×10^(−3) and 8.03×10^(−6).In addition,a techno-economic analysis was performed for a green H_(2) production system,composed of 1 MW of photovoltaic plant and 1 MW of alkaline water electrolyzer,to provide economic insights and feasibility of the system.A levelized cost of H_(2) of$6.89 kgH_(2)−1 was calculated and the potential to reach the levelized cost of H_(2) from steam methane reforming with carbon capture and storage was shown by considering the learning rate of the photovoltaic module and elec-trolyzer.Finally,the replacement of the alkaline water electrolyzer at around 10 years was preferred to increase the net present value from the green H_(2) production system when capital expenditure and replacement cost are low enough. | Hyunjun Lee Jiwon Gu Boreum Lee Hyun-Seok Cho Hankwon Lim | 2023 | Energy and AI2023，13，3： | 0 |
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