Thio Sol–Gel Process for the Synthesis of Titanium Disulfide

A thio sol–gel process for the synthesis of titanium disulfide using titanium alkoxide as the metal source is demonstrated. The alkoxide reacts at room temperature with H₂S to form a precipitate which is a precursor to the sulfide. Through infrared spectroscopy and chemical analysis of the powder and gas chromatographic analysis of the liquid byproducts of the reaction, it is shown that a partially sulfidized alkoxide precursor forms through the displacement of alkoxy groups from the alkoxide by sulfur-bearing thiol groups. This thiolysis reaction is very similar to that which occurs in the case of sol–gel reactions to form oxides. The alkoxy–thiol species then undergo condensation–polymerization by the liberation of H₂S. When it is heat-treated in H₂S, this precursor transforms to TiS₂ at ∼700°C. Systematic heat treatments have been performed and the formation of TiS₂ has been observed using X-ray diffractometry. The change in the morphology has also been studied using scanning electron microscopy.     

Thiophene-based dendronized macromonomers and polymers

The synthesis of thiophene-containing second (G2) and third generation (G3) dendronized macromonomers with methacrylate polymerizable units as well as their corresponding dendronized polymers is reported. The dendrons are prepared from branched thiophene oligomers and are decorated with straight alkyl chains for solubility reasons. The polymerization reactions were done with AIBN as initiator and the polymers were characterized by NMR spectroscopy, elemental analysis and GPC. Molar masses are in the range of 2.2-5.4 × 10⁵ g mol⁻¹ (G2) and 1.3-3.0 × 10⁴ g mol⁻¹ (G3) for different runs. These polymers are investigated by cyclic voltammetry and optical spectroscopy.     

Survey of denoising,segmentation and classification of magnetic resonance imaging for prostate cancer

Multimedia Tools and Applications - Prostate cancer (PCa) has become the second most dreadful cancer in men after lung cancer. Traditional approaches used for treatment of PCa were manual, time...     

Error compensation in machine tools: a neural network approach

For a non-idealized machine tool, each point in the workspace is associated with a tool point positioning error vector. If this error map can be determined, then it is possible to substantially improve the positioning performance of the machine by introducing suitable compensation into the control loop. This paper explores the possibility of using an artifical neural network (ANN) to compute this mapping. The training set for the ANN is obtained by mounting a physical artifact whose dimensions are precisely known in the machine's workspace. The machine, equipped with a touch trigger probe, measures the positions of features on the artifact. The difference between the machine reading and the known dimension is the machine error at that point in the workspace. Using standard modeling techniques, the kinematic error model for a CNC turning center was developed. This model was parameterized by measurement of the parametric error functions using a laser interferometer, electronic levels and a precision square. The kinematic model was then used to simulate the artifact-measuring process and develop the ANN training set. The effect of changing artifact geometry was explored and a machining operation was simulated using the ANN output to provide compensation. The results show that the ANN is capable of learning the error map of a real machine, and that ANN-based compensation can significantly reduce part-dimensional errors.     

Modeling mineral phase change chemistry of groundwater in a rural-urban fringe

This research paper aims to determine the genetic origin of the chemical elements in groundwater. It deals with the results of physicochemical parameters, to evaluate the hydro-geochemistry of groundwater in rural-urban fringe of district Bareilly, India. Pre- and post-monsoon sampling has been carried out, which reveals inter-seasonal variability effect on the hydro-geochemical processes. Geochemical modeling especially computation of saturation index was undertaken using the WATEQ4F model. Majority of samples fall in the category of undersaturation, which further suggests that groundwater still has potential to dissolve more minerals. Chemical categorizations of groundwater samples were performed with the help of the Aquachem model. Grouping of groundwater on the Piper diagram reveals a common composition and origin. In most of the area, water facies is of Ca(2+)-HCO(3)(-) type in both the seasons. It also indicates that in pre-monsoon, ion exchange is the dominant process, whereas in post-monsoon, both ion exchanges as well as reverse ion exchanges are reported in the groundwater of the study area.     

Ferret: An RFID-enabled pervasive multimedia application

This paper presents the design and implementation of Ferret, a system for locating nomadic augmented with RFID tags and visually displaying them to a user in real-time. We present a novel algorithm to infer location of tagged objects using the location of a camera and reader that observes them. We also present techniques to refine location estimates using multiple observations and a method to display and update object locations on a video camera screen. An experimental evaluation of the Ferret prototype shows that (i) Ferret can refine object locations to only 1% of the reader’s coverage region in less than 2 min with small error rate (2.22%); (ii) Ferret can detect nomadic objects with 100% accuracy when the moving distances exceed 20 cm; and (iii) Ferret is robust against different movement patterns of user’s mobility.     

Superparamagnetic Nanostructures for Off‐Resonance Magnetic Resonance Spectroscopic Imaging

This study proposes a new method to generate positive contrast in magnetic resonance imaging (MRI) using superparamagnetic contrast agents. Superparamagnetic nanostructures consisting of octahedron manganese ferrite nanoparticles embedded in spherical nanogels are fabricated using a bottom‐up approach. The composite nanoparticles are strongly magnetized in an external magnetic field and produce a unique NMR frequency shift in water protons, which can be demonstrated in MR spectroscopy and imaging to be different from the bulk pool. Moreover, the particles exhibit excellent colloidal stability in aqueous media and good cell biocompatibility. Hence, these particles are potentially useful as biomarkers by taking advantage of the positive contrast effects produced in MRI.