Cove‐Edged Nanographenes with Localized Double Bonds

The efficient synthesis and electronic properties of two large‐size cove‐edged nanographenes (NGs), CN1 and CN2, are presented. X‐ray crystallographic analysis reveals a contorted backbone for both molecules owing to the steric repulsion at the inner cove position. Noticeably, the dominant structures of these molecules contain four (for CN1) or six (for CN2) localized C=C double bonds embedded in nine (for CN1) or twelve (for CN2) aromatic sextet rings according to Clar's formula, which is supported by bond length analysis and theoretical (NICS, ACID) calculations. Furthermore, Raman spectra exhibit a band associated with the longitudinal CC stretching mode of olefinic double bonds. Owing to the existence of the additional olefinic bonds, both compounds show a small band gap (1.84 eV for CN1 and 1.37 eV for CN2). They also display moderate fluorescence quantum yield (35 % for CN1 and 50 % for CN2) owing to the contorted geometry, which can suppress aggregation in solution.J.W. acknowleges financial support from the MOE Tier 3 programme (MOE2014-T3-1-004) and NRF Investigatorship (NRF-NRFI05-2019-0005). J.C. acknowledges MINECO and Junta de Andalucía of Spain projects (PGC2018-098533-BI00 and UMA18FEDERJA057). M.A.D.-G. and R.M.-M. thank support from MINECO through the research project MAT2015-66586-R and the FPI fellowship (no. BES-2016-077681), respectively

Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm

The prediction of sensor data can help the exoskeleton control system to get the human motion intention and target position in advance, so as to reduce the human-machine interaction force. In this paper, an improved method for the prediction algorithm of exoskeleton sensor data is proposed. Through an algorithm simulation test and two-link simulation experiment, the algorithm improves the prediction accuracy by 14.23 ± 0.5%, and the sensor data is smooth. Input the predicted signal into the two-link model, and use the calculated torque method to verify the prediction accuracy data and smoothness. The simulation results showed that the algorithm can predict the joint angle of the human body and can be used for the follow-up control of the swinging legs of the exoskeleton

Physical and Mechanical Properties of Bamboo Fiber/Glass Fiber Mesh Reinforced Epoxy Resin Hybrid Composites: Effect of Fiber Stacking Sequence

To further improve the preparation efficiency and properties of bamboo fiber reinforced polymer composites (BFRPs) fabricated by the vacuum-assisted resin transfer molding (VARTM) process. Here, the bamboo fiber/glass fiber mesh reinforced polymer hybrid composites (BGRPs) were fabricated by VARTM to determine the effects of three fiber stacking sequences (namely, GBBGBBG, BGBGBGB, and BBGGGBB; B: bamboo fiber, G: glass fiber mesh) on the physical and mechanical properties, as well as void characteristics of BGRPs (i.e. BGRP-1, BGRP-2, and BGRP-3). The results showed that the incorporation of glass fiber meshes could shorten the injection time of epoxy resin and improve the mechanical properties of BFRPs. BGRP-1 exhibited the lowest water absorption (1.31%) and the highest shear strength (15.55 MPa). Glass fiber meshes on the surface and bottom of BGRP-1, respectively, served as buffer layers to retard mechanical damage, so that BGRP-1 had the best drop hammer impact properties. BGRP-2 represented the highest flexural strength and flexural modulus of 92.22 MPa and 6.69 GPa, respectively. The mechanical properties of BGRP-3 were inferior to those of BGRP-1 and BGRP-2, and more voids were observed in the middle of BGRP-3 in micro-CT slices induced by inadequate epoxy resin impregnation on bamboo fibers

Influence Analysis and Optimization of Sampling Frequency on the Accuracy of Model and State-of-Charge Estimation for LiNCM Battery

Battery characterization data is the basis for battery modeling and state estimation. It is generally believed that the higher the sampling frequency, the finer the data, and the higher the model and state estimation accuracy. However, scientific selection strategy for sampling frequency is very important but rarely studied. This paper studies the influence of sampling frequency on the accuracy of battery model and state estimation under four different sampling frequencies: 0.2 Hz, 1 Hz, 2 Hz, and 10 Hz. Then, a function is proposed to depict the relationship between accuracy and sampling frequency, which shows an optimal selection principle. The iterative identification algorithm is presented to identify the model parameters, and state-of-charge (SOC) is estimated via extended Kalman filter algorithm. Experimental results with different operating conditions clearly show the relationship between sampling frequency, accuracy, and data quantity, and the proposed selection strategy has high practical value and universality

Investigation on Application Prospect of Refractories for Hydrogen Metallurgy: The Enlightenment from the Reaction between Commercial Brown Corundum and Hydrogen

Hydrogenous environments put forward new requirements to refractories for the hydrogen metallurgy field. The temperature and impurities in refractories played an important role in stability. A commercial brown corundum with many impurities was adopted as a raw material, thermodynamic calculations and reduction experiments of the brown corundum by high-purity hydrogen (99.99%) were accepted to investigate the stability of the oxides. The weight loss and mass fraction were tested to estimate the stability of the oxides in the brown corundum. XRD and SEM were used to analyze the mineral compositions and microstructures. The results showed that: the thermodynamic stability of the oxides in the brown corundum under high-purity hydrogen was in the order of Al2O3 > CaO > MgO > SiO2 > TiO2 > Fe2O3 at temperatures lower than 1400 °C. Obvious weight loss appeared after heating at 1400 °C for 8 h. The content of CaO did not decline after reduction even at 1800 °C, owing to the formation of hibonite (CaAl12O19), high-purity Al2O3 and CaAl12O19 -based refractories had the prospect for lining materials in the hydrogen metallurgy field, owing to their excellent chemical stability under hydrogenous environments

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time