Femtosecond pulsed laser ablation and patterning of 3C-SiC films on Si substrates for MEMS fabrication

Femtosecond pulsed laser (FPL) micromachining is a direct-writing technique in which an ultrashort pulse laser beam is focused to dimensions of a few microns inside or on the surface of the substrate and then moved around using a X-Y positioning table, thereby creating either features or patterns as required. It outperforms conventional micromachining technologies due to advantages such as precise resolution, minimal thermal or shock damage, and absence of discrimination among materials. 3C-SiC is a very important semiconductor in electronics and opto-electronics and more recently regarded as an optimal candidate for structural or coating applications in microelectromechanical systems (MEMS) used under harsh and high-temperature environments. However, it is a very difficult material to be machined or etched by mechanical or chemical methods.;In this work, fundamental studies on the interaction of femtosecond pulsed beam with 3C-SiC films were performed. The influence of laser parameters such as pulsed energy on the ablation and calculations of damage thresholds and ablation rates were determined. Based on these results, MEMS structures including micromotors, microturbine rotors, and lateral resonators were patterned with good quality and repeatability. Research demonstrates that FPL micromachining is capable of offering a unique solution to overcome the traditional barriers in SiC machining method, opening up opportunities for SiC materials to be used in industrial environment.;As a spinoff of femtosecond pulse micromachining, nanostructuring of 3C-SiC films on Si was observed. Nanoparticle surfaces were further studied in terms of formation conditions and characterizations of crystal structure and related properties. Incubation effects were identified and Coulomb explosion mechanism was proposed to be responsible for the generation of nanoparticles.;Results of research enhance our current understanding of ultrashort pulse-matter interactions and offer potential applications for SiC-MEMS

Soil Carbon and Nitrogen Stocks and Their Relationship with Plant and Soil Dynamics of Degraded and Artificial Restoration Grasslands in an Alpine Region

Land disturbances and management approaches can significantly alter grassland soils. Therefore, understanding the carbon and nitrogen storage accompanying plant and soil physical and chemical properties due to anthropogenic disturbance and different management strategies is important. In our study, we investigated carbon and nitrogen storage in artificial grasslands with different durations of restoration and native grasslands with different levels of degradation. We found that total carbon and nitrogen were significantly higher five years after restoration than after seven and nine years, but decreased due to grassland degradation. Furthermore, soil carbon and nitrogen had a close relationship with plant and soil factors, as reflected by a correlation index. The above-mentioned results indicate that artificial grasslands can be used as an effective method to restore “black-beach” soil grassland. In the long term, however, human intervention should be implemented to prevent the degradation of artificial grasslands

Degradation of Grassland Ecosystems in the Developing World: The Tragedy of Breaking Coupled Human-Natural Systems

Since Hardin (1968) published his famous theory Tragedy of the Commons supported by examples showing that communal grasslands can be easily overgrazed when herdsman increase their herd numbers, a lot of research has supported the viewpoint that rangeland degradation and desertification in much of the pastoral areas in the developing world are caused by overgrazing (Arnalds and Archer 2000). With increasing focus on change at the global scale, many scientists, guided by the disequilibrium theory, hypothesized that climatic variability and change rather than overgrazing is associated with rangeland degradation. We argue that neither overgrazing nor climate change can alone explain the degradation of rangelands worldwide. In contrast, failure to reconcile emergent issues at the interface between the ecological, economic and social aspects has repeatedly resulted in management and policy actions that do not achieve the objectives of optimizing yield of rangeland products in a sustainable manner. The coupled human and natural systems (CHANS) approach proposed by Liu et al. (2007) can be used to identify applicable approaches for helping pastoral societies worldwide cope with global change by facilitating effective collaboration among social scientists, bio/physical scientists, practitioners, managers, and users to protect and sustain pastoral environments (Dong et al. 2011)

A PCA-SMO Based Hybrid Classification Model for Predictions in Precision Agriculture

The human population is growing at an extremely rapid rate, the demand of food supplies for the survival and sustainability of life is a gleaming challenge. Each living being in the planet gets bestowed with the healthy food to remain active and healthy. Agriculture is a domain which is extremely important as it provides the fundamental resources for survival in terms of supplying food and thus the economy of the entire world is highly dependent on agricultural production. The agricultural production is often affected by various environmental and geographical factors which are difficult to avoid being part of nature. Thus, it requires proactive mitigation plans to reduce any detrimental effect caused by the imbalance of these factors. Precision agriculture is an approach that incorporates information technology in agriculture management, the needs of crops and farming fields are fulfilled to optimized crop health and resultant crop production. The proposed study involves an ambient intelligence-based implementation using machine learning to classify diseases in tomato plants based on the images of its leaf dataset. To analytically evaluate the performance of the framework, a publicly available plant-village dataset is used which is transformed to appropriate form using one-hot encoding technique to meet the needs of the machine learning algorithm. The transformed data is dimensionally reduced by Principal Component Analysis (PCA) technique and further the optimal parameters are selected using Spider Monkey Optimization (SMO) approach. The most relevant features as selected using the Hybrid PCA-SMO technique fed into a Deep Neural Networks (DNN) model to classify the tomato diseases. The optimal performance of the DNN model after implementing dimensionality reduction by Hybrid PCA-SMO technique reached at 99% accuracy was achieved in training and 94% accuracy was achieved after testing the model for 20 epochs. The proposed model is evaluated based on accuracy and loss rate metrics; it justifies the superiority of the approach

Enzymatic treatments to improve mechanical properties and surface hydrophobicity of jute fiber membranes

Fiber membranes prepared from jute fragments can be valuable, low cost, and renewable. They have broad application prospects in packing bags, geotextiles, filters, and composite reinforcements. Traditionally, chemical adhesives have been used to improve the properties of jute fiber membranes. A series of new laccase, laccase/mediator systems, and multi-enzyme synergisms were attempted. After the laccase treatment of jute fragments, the mechanical properties and surface hydrophobicity of the produced fiber membranes increased because of the cross-coupling of lignins with ether bonds mediated by laccase. The optimum conditions were a buffer pH of 4.5 and an incubation temperature of 60 °C with 0.92 U/mL laccase for 3 h. Laccase/guaiacol and laccase/alkali lignin treatments resulted in remarkable increases in the mechanical properties; in contrast, the laccase/2,2-azino-bis-(3-ethylthiazoline-6-sulfonate) (ABTS) and laccase/2,6-dimethoxyphenol treatments led to a decrease. The laccase/ guaiacol system was favorable to the surface hydrophobicity of jute fiber membranes. However, the laccase/alkali lignin system had the opposite effect. Xylanase/laccase and cellulase/laccase combined treatments were able to enhance both the mechanical properties and the surface hydrophobicity of jute fiber membranes. Among these, cellulase/laccase treatment performed better; compared to mechanical properties, the surface hydrophobicity of the jute fiber membranes showed only a slight increase after the enzymatic multi-step processes.Financially supported by the National Natural Science Foundation of China (51173071, 21274055), Program for New Century Excellent Talents in University (NCET-12-0883), Program for Changjiang Scholars, Innovative Research Team in University (IRT _15R26), and Fundamental Research Funds for the Central Universities (JUSRP51312B, JUSRP51505