Subwavelength Nanopatch Cavities for Semiconductor Plasmon Lasers

We propose and analyze a family of nanoscale cavities for electrically pumped surface-emitting semiconductor lasers that use surface plasmons to provide optical mode confinement in cavities which have dimensions in the 100-300-nm range. The proposed laser cavities are in many ways nanoscale optical versions of micropatch antennas that are commonly used at microwave/RF frequencies. Surface plasmons are not only used for mode confinement but also for output beam shaping to realize single-lobe far-field radiation patterns with narrow beam waists from subwavelength size cavities. We identify the cavity modes with the largest quality factors and modal gain, and show that in the near-IR wavelength range (1.0-1.6 mum) cavity losses (including surface plasmon losses) can be compensated by the strong mode confinement in the gain region provided by the surface plasmons themselves and the required material threshold gain values can be smaller than 700 cm^-1.     

Preparation and characterization of biodegradable waterabsorbent PAN/SS nanocomposite

Polyacrylonitrile (PAN)/sodium silicate (SS) nanocomposite was prepared via nonconventional emulsion method using an in situ developed transition metal complex Cu(II)/glycine taking ammonium persulfate (APS) as initiator, with a novel motive of converting hydrophobic homopolymer PAN into hydrophilic nano material via nanotechnology by the inclusion of SS to the homopolymer. UV–visible spectral analysis was carried out which revealed various interactions between the in situ developed complex with other reaction components. The formation of the PAN/SS nanocomposite was confirmed by infrared spectra (IR). Furthermore, as evidenced by transmission electron microscopy (TEM), the composite so obtained was found to have nano scale structure. X‐ray diffraction (XRD) was carried out suggesting that the silicate layers were exfoliated during the polymerization process. An increase in the thermal stability for the developed nanocomposite was recorded by thermogravimetric analysis (TGA). Surprisingly, it was also found that the PAN/SS nanocomposite showed considerable amount of waterabsorbency and was biodegradable as tested by activated sludge and cultured media and further confirmed by scanning electron microscopy (SEM). POLYM. COMPOS., 2008. © 2008 Society of Plastics Engineers     

Preparation and analysis of poly(l-lactic acid) composites with oligo(d-lactic acid)-grafted cellulose

α-Cellulose extracted from jute fiber was grafted with oligo( d -lactic acid) (ODLA) via a graft polycondensation reaction in the presence of para-toluene sulfonic acid and potassium persulfate in toluene at 130 °C for 9 h under 380 mmHg. ODLA was synthesized by the ring-opening polymerization of d -lactides in the presence of stannous octoate (0.03 wt % lactide) and d -lactic acid at 140 °C for 10 h. Composites of poly( l -lactic acid) (PLLA) with the ODLA-grafted α-cellulose were prepared by the solution-mixing and film-casting methods. The grafting of ODLA onto α-cellulose was confirmed by Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). The analysis of the composites was performed with FTIR spectroscopy, SEM, wide-angle X-ray diffraction, and thermogravimetric analysis. The distribution of the grafted α-cellulose in the composites was uniform and showed better compatibility with PLLA through intermolecular hydrogen bonding. Only homocrystalline structures of PLLA were present in the composites, and the thermal stability increased with increasing percentage of grafted α-cellulose. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47424.     

An Expert Approach for Data Flow Prediction: Case Study of Wireless Sensor Networks

The data flow is an important parameter used in the optimization problem of Wireless Sensor Networks. This paper presents an expert approach for improved data flow prediction based on data discretization and artificial intelligence. The proposed approach has been implemented on various machine learning methods (a total of 17 methods). This data flow prediction is based on the dataset generated from the simulations with NS-2.35 for multiple Wireless Sensor Networks (5- to -50 nodes). The performance comparison of different machine learning models with continuous data and discretized data is also presented. The proposed approach considerably reduces the execution time of the machine learning models for training purposes and also enhances the accuracy of prediction. The result analysis shows that the proposed approach is better compared to various machine learning methods. Also, the proposed approach is able to handle both continuous and discrete data. The datasets used in this work are available as a supplement at NDS and DDS link.

     

MOLECULAR APPROACH TO MATERIAL DETACHMENT MECHANISM DURING CHEMICAL MECHANICAL PLANARIZATION

Mechanistic numerical analysis and molecular dynamics (MD) simulation are employed to understand the material detachment mechanism associated with chemical mechanical polishing. We investigate the mechanics of scratch intersection mechanism to obtain a characteristic length scale and compare the theoretical predictions with previous experimental observations on ductile copper discs at the micro-scale. First, an analytical model is developed based on mechanics of materials approach. The analytical model includes the effects of strain hardening during material removal as well as the geometry of indenter tip. In the next step, molecular simulations of the scratch intersection are performed at the atomistic scale. The embedded atom method (EAM) is utilized as the force field for workpiece material and a simplified tool-workpiece interaction is assumed to simulate material removal through scratch intersection mechanism. Both models are utilized to predict a characteristic length of material detachment related to material removal during scratch intersection. The predictions from two approaches are compared with experimental observations in order to draw correlations between experiment and simulation. The insights obtained from this work may assist in understanding the mechanism for chemical mechanical planarization (CMP), and even be applied to other different machining and polishing events.     

Component description for knowledge based process planning

The International Journal of Advanced Manufacturing Technology - This paper discusses a number of possible strategies for specifying the geometric characteristics of a part to be manufactured using...