A series of silica-gel-supported
sulfur-capped PAMAM dendrimers
(SiO2-G0-MITC–SiO2-G2.0-MITC) were synthesized
and used for the adsorption of Hg(II) from aqueous solution. The optimum
adsorption pH was found to be 6. Adsorption kinetics indicated that
equilibrium can be approached in about 220 min and that the adsorption
capacity increased with increasing generation of sulfur-capped PAMAM
dendrimers. The kinetics of the adsorption process was found to be
controlled by film diffusion and to follow a pseudo-second-order model.
The adsorption isotherms were fitted well by the Langmuir isotherm
model, and adsorption was found to take place by a chemical mechanism.
Thermodynamic analysis demonstrated that the adsorption was a spontaneous,
endothermic, and randomness-increasing process. Adsorption selectivity
experiments showed that SiO2-G0-MITC–SiO2-G2.0-MITC can selectively adsorb Hg(II) from binary systems containing
Hg(II) with Ni(II), Cd(II), Fe(III), and Zn(II). DFT calculations
revealed that G0-MITC interacts with Hg(II) through the S atom in
a monocoordinated manner, whereas G1.0-MITC behaves as a pentadentate
ligand to coordinate with Hg(II) through the N atom of the tertiary
amine group, the O atoms of the amide groups, and the S atoms. Charge
transfer from G0-MITC and G1.0-MITC to Hg(II) was found to occur during
the adsorption process.
A direct vehicle-to-vehicle (V2V) charging scheme supplies flexible and fast energy exchange way for electric vehicles (EVs) without the supports of charging stations. Main technical challenges in cooperative V2V charging may include the efficient charging navigation structure designs with low communication loads and computational complexities, the decision-making intelligence for the selection of stopping locations to operate V2V charging services, and the optimal matching issue between charging EVs and discharging EVs. In this paper, to solve the above problems, we propose an intelligent V2V charging navigation strategy for a large number of mobile EVs. Specifically, by means of a hybrid vehicular ad-hoc networks (VANETs) based communication paradigm, we first study a mobile edge computing (MEC) based semi-centralized charging navigation framework to ensure the reliable communication and efficient charging coordination. Then, based on the derived charging models, we propose an effective local charging navigation scheme to adaptively select the optimal traveling route and appropriate stopping locations for mobile EVs via the designed Q-learning based algorithm. After that, an efficient global charging navigation mechanism is proposed to complete the best charging-discharging EV pair matching based on the constructed weighted bipartite graph. A series of simulation results and theoretical analyses are presented to demonstrate the feasibility and effectiveness of the proposed V2V charging navigation strategy.
INDEX TERMSElectric vehicles, intelligent V2V charging, charging models, VANETs. NOMENCLATURE N mec Number of MEC servers. N sl Number of stopping locations. N v Number of all moving vehicles including EVs and oil-driven vehicles. PR ev Penetration ratio of EVs to all vehicles. PW Charging power of EVs. R Wireless communication range in VANETs. T Information broadcast interval of NCC. TA (SL k ) Arrival time of an EV in stopping location SL k . TC (SL k ) Charging time of an EV in stopping location SL k . TG (u, v) Arrival time gap between charging EV u and discharging EV v. TR (SL k ) Global traveling time of an EV moving from its current position to the destination going through stopping location SL k . N f (SL k ) Number of free slots in stopping location SL k in current time. T k (e i ) Average traveling time of a mobile EV going through road segment e i . T cw (SL k ) Charging waiting time of EVs for free slots in stopping location SL k . v k (e i ) Average traveling velocity of a mobile EV going through road segment e i . CC/CV Constant-current/constant-voltage. EV Eletric vehicle EVC EVs with charging requirements. EVD EVs with discharging abilities. EVN EVs without charging/discharging interests. G2V Grid-to-vehicle. IMC Information managing centers. ITS Intelligent transportation systems. KM Kuhn-Munkres-based algorithm. KWh KiloWatt-hour. LSTM Long short-term memory. MAC Media access control. MEC Mobile edge computing. MWM Maximum weighted matching. NCC Navigation control center. OBUs On board units...
Aluminum oxidation by ozone produces an aluminum oxide layer which is superior in its corrosion properties compared to natural oxide, as measured by electrochemical methods. The electrochemically measured impedance of the O3-grown films is ∼10 times greater than that of O2-grown films of equivalent thickness. An enhanced pitting potential is observed for the O3-grown oxide film. Transmission electron microscopy results show that the pore size of O3-grown oxide films is considerably smaller than that of O2-grown films. Transmission electron microscopy electron diffraction studies show that the amorphous O3-grown films are ∼4% more dense than the O2-grown film. The initial sticking coefficient for ozone on atomically clean polycrystalline aluminum is 3.8 times larger than for oxygen at 300 K.
To simplify the layout of a purely electric vehicle transmission system and improve the acceleration performance of the vehicle, this paper utilizes the characteristics of the large torque of a hydraulic transmission system and proposes a new mechanical–electric–hydraulic dynamic coupling drive system (MEH-DCDS). It integrates the traditional motor and the swashplate hydraulic pump/motor into one, which can realize the mutual conversion between the mechanical energy, electrical energy, and hydraulic energy. This article explains its working principle and structural characteristics. At the same time, the mathematical model for the key components is established and the operation mode is divided into various types. Based on AMESim software, the article studies the dynamic characteristics of the MEH-DCDS, and finally proposes a method that combines real-time feedback of the accumulator output torque with PID control to complete the system simulation. The results show that the MEH-DCDS vehicle has a starting time of 4.52 s at ignition, and the starting performance is improved by 40.37% compared to that of a pure motor drive system vehicle; after a PID adjustment, the MEH-DCDS vehicle’s starting time is shortened by 1.04 s, and the acceleration performance is improved by 23.01%. The results indicated the feasibility of the system and the power performance was substantially improved. Finally, the system is integrated into the vehicle and the dynamic performance of the MEH-DCDS under cycle conditions is verified by joint simulation. The results show that the vehicle is able to follow the control speed well when the MEH-DCDS is loaded on the vehicle. The state-of-charge (SOC) consumption rate is reduced by 20.33% compared to an electric vehicle, while the MEH-DCDS has an increased range of 45.7 m compared to the EV. This improves the energy efficiency and increases the driving range.
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