Nanostructured Bulk Ceramics (Part I)

Han, Young-Hwan,Mukherjee, Amiya K. The Korean Ceramic Society 2009 한국세라믹학회지 Vol.46 No.3

The processing and characterization of ceramic nanocomposites, which produce bulk nanostructures with attractive mechanical properties, have been emphasized and introduced at Prof. Mukherjee's Lab at UC Davis. The following subjects will be introduced in detail in Part II, III, and IV. In Part II, the paper will describe a three-phase alumina-based nanoceramic composite demonstrating superplasticity at a surprisingly lower temperature and higher strain rate. The next part will show that an alumina-carbon nanotube-niobium nanocomposite produced fracture toughness values that are three times higher than that of pure nanocrystalline alumina. It was possible to take advantage of both fiber-toughening and ductile-metal toughening in this investigation. In the fourth section, discussed will be a silicon-nitride/silicon-carbide nanocomposite, produced by pyrolysis of liquid polymer precursors, demonstrating one of the lowest creep rates reported so far in ceramics at the comparable temperature of $1400^{\circ}C$. This was first achieved by avoiding the oxynitride glass phase at the intergrain boundaries. One important factor in the processing of these nanocomposites was the use of the electrical field assisted sintering method. This allowed the sintering to be completed at significantly lower temperatures and during much shorter times. These improvements in mechanical properties will be discussed in the context of the results from the microstructural investigations.

Nanostructured Bulk Ceramics (Part Ⅰ)

한영환,Amiya K. Mukherjee 한국세라믹학회 2009 한국세라믹학회지 Vol.46 No.3

The processing and characterization of ceramic nanocomposites, which produce bulk nanostructures with attractive mechanical properties, have been emphasized and introduced at Prof. Mukherjee’s Lab at UC Davis. The following subjects will be introduced in detail in Part ⅡI, Ⅲ, and Ⅳ. In Part Ⅱ, the paper will describe a three-phase alumina-based nanoceramic composite demonstrating superplasticity at a surprisingly lower temperature and higher strain rate. The next part will show that an alumina-carbon nanotube-niobium nanocomposite produced fracture toughness values that are three times higher than that of pure nanocrystalline alumina. It was possible to take advantage of both fiber-toughening and ductile-metal toughening in this investigation. In the fourth section, discussed will be a silicon-nitride/silicon-carbide nanocomposite, produced by pyrolysis of liquid polymer precursors, demonstrating one of the lowest creep rates reported so far in ceramics at the comparable temperature of 1400℃ This was first achieved by avoiding the oxynitride glass phase at the intergrain boundaries. One important factor in the processing of these nanocomposites was the use of the electrical field assisted sintering method. This allowed the sintering to be completed at significantly lower temperatures and during much shorter times. These improvements in mechanical properties will be discussed in the context of the results from the microstructural investigations. The processing and characterization of ceramic nanocomposites, which produce bulk nanostructures with attractive mechanical properties, have been emphasized and introduced at Prof. Mukherjee’s Lab at UC Davis. The following subjects will be introduced in detail in Part ⅡI, Ⅲ, and Ⅳ. In Part Ⅱ, the paper will describe a three-phase alumina-based nanoceramic composite demonstrating superplasticity at a surprisingly lower temperature and higher strain rate. The next part will show that an alumina-carbon nanotube-niobium nanocomposite produced fracture toughness values that are three times higher than that of pure nanocrystalline alumina. It was possible to take advantage of both fiber-toughening and ductile-metal toughening in this investigation. In the fourth section, discussed will be a silicon-nitride/silicon-carbide nanocomposite, produced by pyrolysis of liquid polymer precursors, demonstrating one of the lowest creep rates reported so far in ceramics at the comparable temperature of 1400℃ This was first achieved by avoiding the oxynitride glass phase at the intergrain boundaries. One important factor in the processing of these nanocomposites was the use of the electrical field assisted sintering method. This allowed the sintering to be completed at significantly lower temperatures and during much shorter times. These improvements in mechanical properties will be discussed in the context of the results from the microstructural investigations.

Jatropha curcas: a review on biotechnological status and challenges

Mukherjee, Priyanka,Varshney, Alok,Johnson, T. Sudhakar,Jha, Timir Baran The Korean Society of Plant Biotechnology 2011 Plant biotechnology reports Vol.5 No.3

Plant tissue culture and molecular biology techniques are powerful tools of biotechnology that can complement conventional breeding, expedite crop improvement and meet the demand for availability of uniform clones in large numbers. Jatropha curcas Linn., a non-edible, eco-friendly, non-toxic, biodegradable fuel-producing plant has attracted worldwide attention as an alternate sustainable energy source for the future. This review presents a consolidated account of biotechnological interventions made in J. curcas over the decades and focuses on contemporary information and trends of future research.

Rate Enhancement by Micelle Encapsulation for Oxidation of L-Glutamic Acid in Aqueous Media at Room Temperature

Mukherjee, Kakali,Saha, Bidyut Korean Chemical Society 2013 대한화학회지 Vol.57 No.4

Oxidation of glutamic acid is performed in aqueous acid media at $30^{\circ}C$ under the kinetic condition [glutamic acid]$_T{\gg}[Cr(VI)]_T$. Effect of combination of micellar catalyst (SDS, TX-100) and promoter (PA, bpy, phen) has been studied. Among the promoters phen accelerates the reaction most in aqueous media. But the rate acceleration is small in the case. Combination of promoter and catalyst produces much better result. Maximum rate enhancement occurs in presence of the combination of bpy and SDS.

Plant Disease Identification using Deep Neural Networks

Mukherjee, Subham,Kumar, Pradeep,Saini, Rajkumar,Roy, Partha Pratim,Dogra, Debi Prosad,Kim, Byung-Gyu Korea Multimedia Society 2017 The journal of multimedia information system Vol.4 No.4

Automatic identification of disease in plants from their leaves is one of the most challenging task to researchers. Diseases among plants degrade their performance and results into a huge reduction of agricultural products. Therefore, early and accurate diagnosis of such disease is of the utmost importance. The advancement in deep Convolutional Neural Network (CNN) has change the way of processing images as compared to traditional image processing techniques. Deep learning architectures are composed of multiple processing layers that learn the representations of data with multiple levels of abstraction. Therefore, proved highly effective in comparison to many state-of-the-art works. In this paper, we present a plant disease identification methodology from their leaves using deep CNNs. For this, we have adopted GoogLeNet that is considered a powerful architecture of deep learning to identify the disease types. Transfer learning has been used to fine tune the pre-trained model. An accuracy of 85.04% has been recorded in the identification of four disease class in Apple plant leaves. Finally, a comparison with other models has been performed to show the effectiveness of the approach.

A supervised-learning-based spatial performance prediction framework for heterogeneous communication networks

Mukherjee, Shubhabrata,Choi, Taesang,Islam, Md Tajul,Choi, Baek-Young,Beard, Cory,Won, Seuck Ho,Song, Sejun Electronics and Telecommunications Research Instit 2020 ETRI Journal Vol.42 No.5

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

Fight Detection in Hockey Videos using Deep Network

Mukherjee, Subham,Saini, Rajkumar,Kumar, Pradeep,Roy, Partha Pratim,Dogra, Debi Prosad,Kim, Byung-Gyu Korea Multimedia Society 2017 The journal of multimedia information system Vol.4 No.4

Understanding actions in videos is an important task. It helps in finding the anomalies present in videos such as fights. Detection of fights becomes more crucial when it comes to sports. This paper focuses on finding fight scenes in Hockey sport videos using blur & radon transform and convolutional neural networks (CNNs). First, the local motion within the video frames has been extracted using blur information. Next, fast fourier and radon transform have been applied on the local motion. The video frames with fight scene have been identified using transfer learning with the help of pre-trained deep learning model VGG-Net. Finally, a comparison of the methodology has been performed using feed forward neural networks. Accuracies of 56.00% and 75.00% have been achieved using feed forward neural network and VGG16-Net, respectively.

Order parameter with line nodes ands±-wave symmetry for the noncentrosymmetric superconductor Li2Pt3B

Mukherjee, Soumya P.,Takimoto, Tetsuya American Physical Society 2012 Physical review. B, Condensed matter and materials Vol.86 No.13

Genetic Similarity between Cotton Leafroll Dwarf Virus and Chickpea Stunt Disease Associated Virus in India

Mukherjee, Arup Kumar,Mukherjee, Prasun Kumar,Kranthi, Sandhya The Korean Society of Plant Pathology 2016 Plant Pathology Journal Vol.32 No.6

The cotton leafroll dwarf virus (CLRDV) is one of the most devastating pathogens of cotton. This malady, known as cotton blue disease, is widespread in South America where it causes huge crop losses. Recently the disease has been reported from India. We noticed occurrence of cotton blue disease and chickpea stunt disease in adjoining cotton and chickpea fields and got interested in knowing if these two viral diseases have some association. By genetic studies, we have shown here that CLRDV is very close to chickpea stunt disease associated virus (CpSDaV). We were successful in transmitting the CLRDV from cotton to chickpea. Our studies indicate that CpSDaV and CLRDV in India are possibly two different strains of the same virus. These findings would be helpful in managing these serious diseases by altering the cropping patterns.

Simulation of tissue differentiation around acetabular cups: the effects of implant-bone relative displacement and polar gap

Mukherjee, Kaushik,Gupta, Sanjay Techno-Press 2014 Advances in biomechanics & applications Vol.1 No.2

Peri-acetabular bone ingrowth plays a crucial role in long-term stability of press-fit acetabular cups. A poor bone ingrowth often results in increased cup migration, leading to aseptic loosening of the implant. The rate of peri-prosthetic bone formation is also affected by the polar gap that may be introduced during implantation. Applying a mechano-regulatory tissue differentiation algorithm on a two-dimensional plane strain microscale model, representing implant-bone interface, the objectives of the study are to gain an insight into the process of peri-prosthetic tissue differentiation and to investigate its relationship with implant-bone relative displacement and size of the polar gap. Implant-bone relative displacement was found to have a considerable influence on bone healing and peri-acetabular bone ingrowth. An increase in implant-bone relative displacement from $20{\mu}m$ to $100{\mu}m$ resulted in an increase in fibrous tissue formation from 22% to 60% and reduction in bone formation from 70% to 38% within the polar gap. The increase in fibrous tissue formation and subsequent decrease in bone formation leads to weakening of the implant-bone interface strength. In comparison, the effect of polar gap on bone healing and peri-acetabular bone ingrowth was less pronounced. Polar gap up to 5 mm was found to be progressively filled with bone under favourable implant-bone relative displacements of $20{\mu}m$ along tangential and $20{\mu}m$ along normal directions. However, the average Young's modulus of the newly formed tissue layer reduced from 2200 MPa to 1200 MPa with an increase in polar gap from 0.5 mm to 5 mm, suggesting the formation of a low strength tissue for increased polar gap. Based on this study, it may be concluded that a polar gap less than 0.5 mm seems favourable for an increase in strength of the implant-bone interface.