High-efficiency removal of phenol and coking wastewater via photocatalysis-Fenton synergy over a Fe-g-C3N4 graphene hydrogel 3D structure

Jinshan Hu,Pengfei Zhang,Jifang Cui,Weijia An,Li Liu,Yinghua Liang,Qingbin Yang,Hongjun Yang,Wenquan Cui 한국공업화학회 2020 Journal of Industrial and Engineering Chemistry Vol.84 No.-

To achieve rapid separation of electron-hole pairs and improve photocatalytic degradation activity, theFe-g-C3N4 reduced graphene hydrogel (rGH/Fe-g-C3N4) with a 3D network structure was fabricated viathe hydrothermal method. Using visible light irradiation, H2O2 was added to form a photocatalysis-Fenton synergy system. The results showed that the synergistic degradation rate constant of 10% rGH/Feg-C3N4 was 52% higher than that of the multiphase Fenton reaction and 1.5 times higher than that of theFe-g-C3N4. In the seventh cycle, the catalytic efficiency was still as high as 86.9%. Based on the optimizedconditions from phenol degradation, the system was further applied to coking wastewater treatment,and the degradation efficiency of TOC and COD in 60 min reached 66.3% and 68.1%, respectively. Such highand stable degradation performance was ascribed to the synergy effect of photocatalysis and Fenton. Because of the photogenerated electrons not only can promote the Fe3+/Fe2+ cycle, accelerate thedecomposition of H2O2, but also can quickly transfer to graphene and directly decompose H2O2 to formOH. Thus, a large amount ofOH were generated through the two different channels, which greatlyimprove the degradation efficiency. Furthermore, rGH/Fe-g-C3N4 can be regenerated usingfilters withoutthe need for additional complicated processing. This work provides an effective strategy for the deeptreatment of industrial wastewater.

Simulation of nonstationary wind in one-spatial dimension with time-varying coherence by wavenumber-frequency spectrum and application to transmission line

Xiongjun Yang,Ying Lei,Lijun Liu,Jinshan Huang 국제구조공학회 2020 Structural Engineering and Mechanics, An Int'l Jou Vol.75 No.4

Practical non-synoptic fluctuating wind often exhibits nonstationary features and should be modeled as nonstationary random processes. Generally, the coherence function of the fluctuating wind field has time-varying characteristics. Some studies have shown that there is a big difference between the fluctuating wind field of the coherent function model with and without time variability. Therefore, it is of significance to simulate nonstationary fluctuating wind field with time-varying coherent function. However, current studies on the numerical simulation of nonstationary fluctuating wind field with time-varying coherence are very limited, and the proposed approaches are usually based on the traditional spectral representation method with low simulation efficiency. Especially, for the simulation of multi-variable wind field of large span structures such as transmission tower-line, not only the simulation is inefficient but also the matrix decomposition may have singularity problem. In this paper, it is proposed to conduct the numerical simulation of nonstationary fluctuating wind field in one-spatial dimension with time-varying coherence based on the wavenumber-frequency spectrum. The simulated multivariable nonstationary wind field with time-varying coherence is transformed into one-dimensional nonstationary random waves in the simulated spatial domain, and the simulation by wavenumber frequency spectrum is derived. So, the proposed simulation method can avoid the complicated Cholesky decomposition. Then, the proper orthogonal decomposition is employed to decompose the time-space dependent evolutionary power spectral density and the Fourier transform of time-varying coherent function, simultaneously, so that the two-dimensional Fast Fourier transform can be applied to further improve the simulation efficiency. Finally, the proposed method is applied to simulate the longitudinal nonstationary fluctuating wind velocity field along the transmission line to illustrate its performances.

Fully Recrystallized Al0.5CoCrFeNi High-Entropy Alloy Strengthened by Nanoscale Precipitates

Haoxue Yang,Jinshan Li,Tong Guo,William Yi Wang,Hongchao Kou,Jun Wang 대한금속·재료학회 2019 METALS AND MATERIALS International Vol.25 No.5

The correlation among the strength, ductility and precipitates in a dual-phase Al0.5CoCrFeNihigh-entropy alloy has beeninvestigated. The property of plastic was improved in the recrystallized Al0.5CoCrFeNiHEA prepared as the master alloy. Formation of the stable nanosized L12phases after aging at 650 °C was revealed, along with the lath-like BCC phasesprecipitated in the grain, as the primary contribution of the strength enhancement. Excellent balanced tensile properties atroom temperature were achieved through aging treatment, as the yield strength and ultimate tensile strength show 2.68, 1.63times higher in 650 °C/140 h aged condition than that in initial states, respectively, superior to many high-entropy alloysand conventional alloys.

Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy

Ma Haozhong,Yang Jinshan,Chen Xiaolu,Jiang Xinyu,Su Yimin,Qiao Shanlei,Zhong Guowei 한국미생물학회 2021 The journal of microbiology Vol.59 No.6

Fungi of the genus Aspergillus are ubiquitously distributed in nature, and some cause invasive aspergillosis (IA) infections in immunosuppressed individuals and contamination in agricultural products. Because microscopic observation and molecular detection of Aspergillus species represent the most operator-dependent and time-intensive activities, automated and cost-effective approaches are needed. To address this challenge, a deep convolutional neural network (CNN) was used to investigate the ability to classify various Aspergillus species. Using a dissecting microscopy (DM)/stereomicroscopy platform, colonies on plates were scanned with a 35× objective, generating images of sufficient resolution for classification. A total of 8,995 original colony images from seven Aspergillus species cultured in enrichment medium were gathered and autocut to generate 17,142 image crops as training and test datasets containing the typical representative morphology of conidiophores or colonies of each strain. Encouragingly, the Xception model exhibited a classification accuracy of 99.8% on the training image set. After training, our CNN model achieved a classification accuracy of 99.7% on the test image set. Based on the Xception performance during training and testing, this classification algorithm was further applied to recognize and validate a new set of raw images of these strains, showing a detection accuracy of 98.2%. Thus, our study demonstrated a novel concept for an artificial-intelligence-based and cost-effective detection methodology for Aspergillus organisms, which also has the potential to improve the public’s understanding of the fungal kingdom.

Effects of ruminally degradable starch levels on performance, nitrogen balance, and nutrient digestibility in dairy cows fed low corn-based starch diets

Guobin Luo,Wenbin Xu,Jinshan Yang,Yang Li,Liyang Zhang,Yizhen Wang,Cong Lin,Yonggen Zhang 아세아·태평양축산학회 2017 Animal Bioscience Vol.30 No.5

Objective: This trial was performed to examine the effects of ruminally degradable starch (RDS) levels in total mixed ration (TMR) with low corn-based starch on the milk production, whole-tract nutrient digestibility and nitrogen balance in dairy cows. Methods: Eight multiparous Holstein cows (body weight [BW]: 717±63 kg; days in milk [DIM]: 169±29) were assigned to a crossover design with two dietary treatments: a diet containing 62.3% ruminally degradable starch (% of total starch, low RDS) or 72.1% ruminally degradable starch (% of total starch, high RDS). Changes to the ruminally degradable levels were conducted by using either finely ground corn or steam-flaked corn as the starch component. Results: The results showed that dry matter intake, milk yield and composition in dairy cows were not affected by dietary treatments. The concentration of milk urea nitrogen was lower for cows fed high RDS TMR than low RDS TMR. The whole-tract apparent digestibility of neutral detergent fiber, acid detergent fiber and crude protein decreased, and that of starch increased for cows fed high RDS TMR over those fed low RDS TMR, with no dietary effect on the whole-tract apparent digestibility of dry matter and organic matter. The proportion of urinary N excretion in N intake was lower and that of fecal N excretion in N intake was higher for cows fed high RDS TMR than those fed low RDS TMR. The N secretion in milk and the retention of N were not influenced by the dietary treatments. Total purine derivative was similar in cows fed high RDS TMR and low RDS TMR. Consequently, estimated microbial N flow to the duodenum was similar in cows fed high RDS TMR and low RDS TMR. Conclusion: Results of this study show that ruminally degradable starch levels can influence whole-tract nutrient digestibility and nitrogen balance in dairy cows fed low corn-based starch diets, with no influence on performance.

Facile Synthesis of MoS2 Hierarchical Nanostructures as Electrodes for Capacitor with Enhanced Pseudocapacitive Property

Shiting Luo,Limei Xu,Jinshan Li,Wenjing Yang,Minli Liu,Lin Ma 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2020 NANO Vol.15 No.02

In this work, the 3D porous hierarchical MoS2 nanostructures were prepared via a simple hydrothermal deposition method only utilizing titanium (Ti) mesh as a substrate. The as-synthesized uniform MoS2 flower-like nanostructures were assembled by nanosheets composed of several stacking layers. The curved and rough surface of cylindrical Ti wire was beneficial to assembly of MoS2 nanosheets into hierarchical architectures. Moreover, the electrochemical performance of the as-prepared MoS2 nanostructured electrodes for capacitors was also investigated. The structural advantages lead to a remarkably improved pseudocapacitive property including high capacitance and durable cycling ability.