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The evaluation of the wear state of the hob during gear machining can effectively optimize the tool change strategy, which is of great significance to the improvement of machining efficiency. Aiming at the wet machining conditions for large gears, a method of hob wear monitoring called 'Drift Prediction of Gamma Distribution Parameter (DP-GDP)' is proposed based on time domain impulses statistical distribution. Firstly, a vibration signal model of the hob is established based on the combined action of periodic cutting force excitation and random impact excitation. The vibration parameters such as damping and stiffness are discussed. Then, the Poisson process mechanism of random collision between hob and material particles during hob cutting is taken into consideration. The relationship between hob wear stage and occurrence of impulses excited by material particles collision is modeled under over-damping condition. The gamma distribution mechanism of the time required for impulses generation is elucidated. Furthermore, a wear feature extraction method based on the statistical parameters of random impulses is proposed. The method focuses on single impulse identification and description of impulses occurrence probability distribution in time domain signal. Based on this, a prediction curve that reflects the wear state can be got for hob life degeneration assessment. Finally, a full-life test of hobs during the hob cutting process in the industrial field is completed. The data of hob whole life in different wear states are acquired. The simulation and experimental results show that the proposed method can effectively predict the whole life wear process of the hob. The extracted features can accurately map the hob wear state. Compared with traditional methods such as RMS and wavelet, the proposed method has better validity and accuracy under specific processing conditions. [ABSTRACT FROM AUTHOR]

β-MnO 2 and δ-MnO 2 submicrospheres with different crystallographic forms were parallelly synthesized via a facile way using MnCO 3 submicrospheres as manganese source and template. Fine observation enclosed that the β-MnO 2 and δ-MnO 2 submicrospheres were composed of nanoparticles and nanosheets respectively. The performances of these samples as cathode for aqueous zinc ion batteries were investigated: the prepared β-MnO 2 and δ-MnO 2 submicrospheres exhibit an initial capacity of 100 and 126 mAh/g and preserve about 99% and 81% of their original capacity over 100 cycles at a current density of 100 mA/g, respectively. It is observed from the galvanostatic charge/discharge profiles that the majority of excess capacity for δ-MnO 2 than β-MnO 2 is contributed by the first discharge step referred to H+ insertion process, as δ-MnO 2 has lower charge transfer resistance and larger diffusion coefficient than β-MnO 2. We ascribe this to the larger layer space of δ-MnO 2 which would benefit for the easily insertion/extraction of hydronium ions. For contrast, the β-MnO 2 encounters a sluggish transportation of hydronium ion along with the breaking of H–H 2 O bond during ion inserting. Image 1 [ABSTRACT FROM AUTHOR]

• A method for constructing grayscale images of vibration signals has been proposed. • An unconventional convolutional kernel design method has been proposed. • The validity of the fault diagnosis method in this paper were verified. Rolling bearings are widely used in various industries, including rail transit, aerospace, and wind power generation, playing a critical role. However, bearing failures can lead to serious consequences, impacting equipment operation and even causing safety accidents. Hence, the diagnosis of bearing faults is of utmost importance. However, the variable speed conditions experienced during bearing operation pose significant challenges to fault diagnosis. To overcome the limitations of traditional methods in diagnosing bearing faults under variable speed conditions, this paper proposes a fault diagnosis method based on the gray-level co-occurrence matrix (GLCM) and Dual Channel Convolutional Neural Network (DCCNN). The method introduces a two-dimensional grayscale matrix construction (2D-GMC) technique to extract grayscale texture features for fault diagnosis. Additionally, an unconventional kernel design method, based on grayscale image contrast, is proposed to reduce the complexity associated with traditional square kernels. A new DCCNN architecture is developed accordingly. Furthermore, the transfer learning concept is utilized to train the proposed DCCNN model using fault signals at specific rotational speeds. The method intercepts the variable speed into multi-speed short-time series, then constructs gray image under different speed to realize the rapid fault diagnosis of bearings under variable speed conditions. [ABSTRACT FROM AUTHOR]

In virtue of high capacity and low manufacturing expense, transition metal carbonates (TMCs) have recently arisen enormous research interests as the anode materials for rechargeable lithium ion batteries (LIBs). However, the low electrical conductivity and unstable cycle performance impeded their further development. In this work, Co-B compounds are surface-engineered for the first time onto the mixed single-phase Mn 0.33 Co 0.67 CO 3 microspheres to accelerate the reaction kinetics and suppress the volume fluctuation of the electrodes during Li+ insertion/extraction. Specifically, Co-B nanoflakes not only function as the robust mechanical bridges between Mn 0.33 Co 0.67 CO 3 primary nanoparticles, but also provide extra pathways for electron/charge transport, both of which facilitate the improvement of electrochemical behaviors. Morever, the synergetic effect between Mn 0.33 Co 0.67 CO 3 and Co-B nanoflakes allow a high flux of Li+ across the interface to provide signifcantly boosted Li+ diffusivity. Impressively, the Mn 0.33 Co 0.67 CO 3 @Co-B electrode delivers a high reversible capacity of 806 mA h g−1 over 500 cycles at a high rate of 1.0 A g−1, demonstrating its superior cycling stability. Therefore, surface engineering of borides may be an effective and promising way to improve the electrochemical behaviors of conversion type anodes like TMCs. Image 1 [ABSTRACT FROM AUTHOR]

[Display omitted] • Superior toluene catalytic oxidation activity was achieved by Cu 1 Ce 3 binary oxides. • Details of Cu-Ce synergistic effect on toluene catalytic oxidation were studied. • The oxidation of benzyl alcohol into benzoic acid was the rate-determining step. • The toluene catalytic oxidation over Cu 1 Ce 3 binary oxides involves a MvK mechanism. A series of Cu-Ce binary oxides were synthesized via a facile co-precipitation method for toluene catalytic oxidation. The physicochemical properties of these samples were characterized by BET, XRD, SEM, TEM, H 2 -TPR, O 2 -TPD, and XPS. It reveals that Cu 1 Ce 3 catalyst possesses a higher specific surface area, stronger Cu-Ce synergistic effect, and better low-temperature reducibility than other samples. Moreover, Cu 1 Ce 3 catalyst exhibited superior toluene oxidation activity with 99.10% toluene removal efficiency and 97.28% CO 2 selectivity at 200 °C, as well as satisfactory stability and moisture resistance. Therefore, Cu 1 Ce 3 catalyst indicates a huge potential for practical applications, such as coal-fired flue gas and spray coating industry. The reaction pathways for toluene catalytic oxidation over the Cu 1 Ce 3 catalyst were further illuminated by in-situ DRIFTs. In the main pathway, toluene molecules were firstly in contact with the interface lattice oxygen, then the abstraction of H atoms occurred on the methyl group, and benzyl alcohol is considered as the primary intermediate, during which the rate-determining step is the oxidation of benzyl alcohol. In the secondary pathway, a little proportion of toluene molecules were directly oxidized into benzoic acid by the chemisorbed oxygen species. In subsequent steps, all the intermediates were converted into CO 2 and H 2 O. At last, it is confirmed that the toluene oxidation over Cu 1 Ce 3 catalyst involves a Mars-van-Krevelen mechanism via this study. [ABSTRACT FROM AUTHOR]

• Mn 3 (PO 4) 2 nanoflakes are firstly explored as the efficient polysulfide inhibitor in Li-S battery. • The Mn 3 (PO 4) 2 /rGO composites can effectively suppress the polysulfide shuttle. • The S@Mn 3 (PO 4) 2 /rGO composites show the enhanced electrochemical performances. The development of Lithium surfur batteries (LSBs) are beset with tenacious issues including the insulating property of the sulfur element and the dissolution of the reaction intermediate products into electrolyte, which result in low S utilization and poor battery cycling performances. In this work, we exploit metal phosphate as the S host for suppressing the sulfur dissolving and accelerating the polysulfide conversion, and firstly construct the S@Mn 3 (PO 4) 2 /rGO composites through an in-situ solution growth followed by S impregnation route. More importantly, the geometric compatibility of Mn 3 (PO 4) 2 nanoflakes and rGO nanosheet endows such S@Mn 3 (PO 4) 2 /rGO composites with high electrical conductivity and excellent mechanical durability. As evidenced by the electrochemical performances in LSB, the S@Mn 3 (PO 4) 2 /rGO cathode shows discharge capacities of 863 mA g−1 at 0.2 C after 100 cycles and 658 mAh g−1 after 470 cycles at 1.0 C with high Coulumbic efficiency of nearly 100%; more impressively, it delivers a high capacity of 407 mAh g−1 ever at 5.0 C rate during rate capability testing. Our research on the construction of S@Mn 3 (PO 4) 2 /rGO composites provides new insight and opportunitie to develop advanced LSBs with highly efficient inhibitors for trapping polysulfides and accelerating their conversion. [ABSTRACT FROM AUTHOR]