Electrodynamically actuated on-chip flow cytometry with low shear stress for electro-osmosis based sorting using low conductive medium

In the proposed paper, we demonstrate on-chip electrodynamically driven actuator flow cytometry, based on negative dielectrophoretic (nDEP) focus and alternating current electro-osmotic flow (ACEOF) sorting technique. This single chip can perform three different functions such as focusing, transportation of beads/cells to detection site and reloading the unsorted ones with two distinctive phenomena. AC EOF is achieved by the design of the asymmetric electrode pair’s array and nDEP is used to focus the beads/cells in-line. The design, simulation and experimental results of the proposed microchip are reported in this paper. The simulation and experimental results reveal well defined stable region for nDEP and ACEOF driving force. The potential severe shear stress damage caused by the sheath flow in conventional flow cytometry is eliminated. In addition, to explore the influence of conductivity of the medium, we have used low conductive formulated medium with conductivity of 81.4 μS/cm. The voltage and the frequency required to manipulate the particles decreased comparatively with the use of this medium.     

Fabrication and morphological control of three-dimensional cage type mesoporous titanosilicate with extremely high Ti content

TiSBA-1 materials with extremely high Ti content, up to silicon to titanium ratio (n_Si/n_Ti) of 2.4, have been successfully prepared through direct synthesis method by controlling the molar ratio of hydrochloric acid to silicon (n_HCl/n_Si). It has been found that the amount of Ti content and the structure of the TiSBA-1 can easily be controlled by the simple adjustment of n_HCl/n_Si ratio. X-ray diffraction pattern (XRD) of the obtained materials revealed that the materials are highly ordered and possess cubic three-dimensional cage type structure with open windows. N₂ adsorption–desorption measurement confirmed the narrow pore size distribution, high specific surface area (1317–1491 m² g⁻¹), and large specific pore volume (0.68–0.75 cm³ g⁻¹) for all the samples. UV–vis DR spectra of the prepared materials confirmed that Ti atoms are exclusively incorporated within silica framework and occupy the tetrahedral position while the presence of isolated bulk titania could be negligible. Morphologies of the TiSBA-1 materials have been also controlled by simply adjusting the n_Si/n_Ti ratio. With the appropriate Ti content, TiSBA-1 materials can be obtained as regular fine spheres. Moreover, the detailed mechanism on the morphological and phase transition control, and the incorporation of high amount of Ti in the framework of TiSBA-1 materials has been also discussed in detail.     

Artificial neural network approach for prediction of stress–strain curve of near b titanium alloy

In the present study, artificial neural network(ANN) approach was used to predict the stress–strain curve of near beta titanium alloy as a function of volume fractions of a and b. This approach is to develop the best possible combination or neural network(NN) to predict the stress–strain curve. In order to achieve this, three different NN architectures(feed-forward back-propagation network,cascade-forward back-propagation network, and layer recurrent network), three different transfer functions(purelin, Log-Sigmoid, and Tan-Sigmoid), number of hidden layers(1 and 2), number of neurons in the hidden layer(s),and different training algorithms were employed. ANN training modules, the load in terms of strain, and volume fraction of a are the inputs and the stress as an output.ANN system was trained using the prepared training set(a,16 % a, 40 % a, and b stress–strain curves). After training process, test data were used to check system accuracy. It is observed that feed-forward back-propagation network is the fastest, and Log-Sigmoid transfer function is giving the best results. Finally, layer recurrent NN with a single hidden layer consists of 11 neurons, and Log-Sigmoid transfer function using trainlm as training algorithm is giving good result, and average relative error is1.27 ± 1.45 %. In two hidden layers, layer recurrent NN consists of 7 neurons in each hidden layer with trainrp as the training algorithm having the transfer function of LogSigmoid which gives better results. As a result, the NN is founded successful for the prediction of stress–strain curve of near b titanium alloy.     

Highly active three-dimensional cage type mesoporous aluminosilicates and their catalytic performances in the acetylation of aromatics

Three-dimensional cage type mesoporous aluminosilicates (AlKIT-5) with different n_Si/n_Al ratios have been successfully synthesized hydrothermally using pluronic F127 as a template under highly acidic medium [P. Srinivasu, S. Alam, V.V. Balasubramanian, S. Velmathi, D.P. Sawant, W. Böhlmann, S.P. Mirajkar, K. Ariga, S.B. Halligudi, A. Vinu, Adv. Funct. Mater., in press]. The catalysts have been unambiguously characterized by XRD, N₂ adsorption, and TPD of NH₃, and utilized for the acetylation of veratrole, and other aromatic substrates such as anisole, isobutylbenzene, and 2-methoxynaphthalene, using acetic anhydride (AA) as an acylating agent under liquid phase conditions. The effect of various parameters such as the reaction temperature, reaction time, reactant mole ratios, and amount of aluminium, on the AA conversion in the acetylation of veratrole has been investigated in detail to discover the optimized reaction condition. The reaction conditions were optimized to get more conversion of acetic anhydride and the activity of these catalysts has been compared with zeolite catalysts namely HY, Hβ, H-mordenite, and HZSM-5 zeolites, and the results are discussed in detail. It has been found that the AlKIT-5 catalysts showed excellent performance in the acetylation of veratrole over zeolites and other mesoporous catalysts studied under the optimized reaction conditions. Among the catalysts studied, AlKIT-5(10) was found to be the best catalyst, showing high conversion of AA and very high selectivity to the desired products. The conversion of AA decreases in the following order: AlKIT-5(10) > AlKIT-5(28) > HY(13.5) > Hβ(30) > AlKIT-5(44) > AlMCM-41(22) > H-mordenite(20) > ZSM-5(60). Moreover, the kinetics of the AlKIT-5(10) catalyst in the acetylation of veratrole has been investigated in detail and the results are clearly discussed.     

Nanoplough-constrictions on thin YBCO films made with atomic force microscopy

Utilizing atomic force microscope (AFM) with a diamond tip, we were able to successfully plough nano-constrictions on epitaxially grown YBa2Cu3O(7-x) thin films deposited on MgO substrates. The thickness, width, and length of the obtained constrictions were in the range of a few 100 nm. Furthermore, we managed to produce a new S-type constriction, of which the dimensions are easier to control than for conventional constrictions.     

Novel microporous carbon material with flower like structure templated by MCM-22

Novel flower like microporous carbons with very high surface area have been synthesized for the first time using MCM-22 zeolite as a template and sucrose as a carbon source. The textural parameters of the materials can easily be tuned by the simple adjustment of the sucrose to MCM-22 weight ratio. It has been also found that the specific surface area of the microporous carbon materials is much higher as compared with that of its parent zeolite template.     

Halogen-free acylation of toluene over FeSBA-1 molecular sieves

Three-dimensional cage type iron substituted mesoporous silica with different iron contents (FeSBA-1) was synthesized in a highly acidic media using cetyltriethylammonium bromide and tetraethylorthosilicate as a template and a silica source, respectively. Acylation of toluene with acetic anhydride (AA) was carried out over FeSBA-1 mesoporous catalysts with different n_Si/n_Fe ratios in the temperature range 80–180 °C for a time-on-stream of 1–6 h under liquid phase conditions. The important factors affecting the conversion and the selectivity of the reaction, such as the reaction temperature, feed ratio, catalyst weight and time-on-stream were studied and the results are discussed in detail. The reaction conditions were optimized and the n_AA/n_Toluene ratio of 2 and catalyst weight of 0.1 g (3.3 wt% of total reaction mixture) were maintained for all catalytic runs. It was found that the catalytic activity is strongly influenced by the amount of tetrahedral iron in the catalysts. Among the catalysts used in the present study, FeSBA-1(36) showed a high toluene conversion and selectivity to p-methylacetophenone (p-MAP) under the optimized reaction conditions. It was also found that the selectivity for p-MAP was always higher than m-MAP and o-MAP for all the catalysts and the activity of the catalysts changes in the following order: FeSBA-1(36) > FeSBA-1(90) > FeSBA-1(120).     

An adaptive control strategy for the abrasive waterjet cutting process with the integration of vision-based monitoring and a neuro-genetic control strategy

This paper presents an integrated approach for the monitoring and control of abrasive waterjet (AWJ) cutting process. A machine-vision-based monitoring approach was proposed to obtain the bore diameter of the focusing nozzle from time to time. A neuro-genetic approach, proposed by Srinivasu and Ramesh Babu (Appl Soft Comput 8(1):809–819, 2008) was employed as a control strategy to modify the process parameters, such as water pressure, abrasive flow rate, and jet traverse rate, so as to maintain the desired depth of cut, with changes in the diameter of the focusing nozzle monitored with a machine vision system. By combining the monitoring and control strategies, an integrated approach for adaptive control of AWJ cutting process is realized. The effectiveness of the proposed integrated approach for adaptive control of AWJ cutting process was shown by comparing the results obtained from the experiments with the process parameters suggested by the control strategy to achieve the desired depth of cut. From the results of the study, it is seen that the proposed monitoring system is capable of monitoring the focusing nozzle diameter with a mean absolute deviation of 0.05 mm and that the neuro-genetic strategy is capable of modifying the controllable process parameters to maintain the desired depth of cut with a mean absolute deviation of 0.87 mm.     

An AW-HARIS Based Automated Segmentation of Human Liver Using CT Images

In the digestion of amino acids, carbohydrates, and lipids, as well as protein synthesis from the consumed food, the liver has many diverse responsibilities and functions that are to be performed. Liver disease may impact the hormonal and nutritional balance in the human body. The earlier diagnosis of such critical conditions may help to treat the patient effectively. A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver. The proposed approach can recognize the abnormalities with better accuracy without training, unlike in supervisory procedures requiring considerable computational efforts for training. In the earlier stages, the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better segmentation. Then, the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted–-Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information, which has resulted in precise outcome with acceptable computational latency when compared to that of its counterparts. It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%. The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.