Shape-Based Separation of Micro-/Nanoparticles in Liquid Phases

The production of particles with shape-specific properties is reliant upon the separation of micro-/nanoparticles of particular shapes from particle mixtures of similar volumes. However, compared to a large number of size-based particle separation methods, shape-based separation methods have not been adequately explored. We review various up-to-date approaches to shape-based separation of rigid micro-/nanoparticles in liquid phases including size exclusion chromatography, field flow fractionation, deterministic lateral displacement, inertial focusing, electrophoresis, magnetophoresis, self-assembly precipitation, and centrifugation. We discuss separation mechanisms by classifying them as either changes in surface interactions or extensions of size-based separation. The latter includes geometric restrictions and shape-dependent transport properties

Metal-organig framework MIL-68(In)-NH2 on the membrane test bench for dye removal and carbon capture

The metal-organic framework (MOF) MIL-68(In)-NH2 was tested for dye removal from wastewater and carbon capture gas separation. MIL-68(In)-NH2 was synthesized as a neat, supported MOF thin film membrane and as spherical particles using pyridine as a modulator to shape the morphology. The neat MIL-68(In)-NH2 membranes were employed for dye removal in cross-flow geometry, demonstrating strong molecular sieving. MIL-68(In)-NH2 particles were used for electrospinning of poylethersulfone mixed-matrix membranes, applied in dead-end filtration with unprecedented adsorption values. Additionally, the neat MOF membranes were used for H2/CO2 and CO2/CH4 separation

CAR-T cell. the long and winding road to solid tumors

Adoptive cell therapy of solid tumors with reprogrammed T cells can be considered the "next generation" of cancer hallmarks. CAR-T cells fail to be as effective as in liquid tumors for the inability to reach and survive in the microenvironment surrounding the neoplastic foci. The intricate net of cross-interactions occurring between tumor components, stromal and immune cells leads to an ineffective anergic status favoring the evasion from the host's defenses. Our goal is hereby to trace the road imposed by solid tumors to CAR-T cells, highlighting pitfalls and strategies to be developed and refined to possibly overcome these hurdles

Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique

This research was aimed at developing a new model to predict flyrock distance based on a genetic programming (GP) technique. For this purpose, six granite quarry mines in the Johor area of Malaysia were investigated, for which various controllable blasting parameters were recorded. A total of 262 datasets consisting of six variables (i.e., powder factor, stemming length, burden-to-spacing ratio, blast-hole diameter, maximum charge per delay, and blast-hole depth) were collected applied to developing the flyrock predictive model. To identify the optimum model, several GP models were developed to predict flyrock. In the same way, using non-linear multiple regression (NLMR) analysis, various models were established to predict flyrock. Finally, to compare the performance of the developed models, regression coefficient (R2), root mean square error (RMSE), variance account for (VAF), and simple ranking methods were computed. According to the results obtained from the test dataset, the best flyrock predictive model was found to be the GP based model, with R2Â =Â 0.908, RMSEÂ =Â 17.638 and VAFÂ =Â 89.917, while the corresponding values for R2, RMSE and VAF for the NLMR model were 0.816, 26.194, and 81.041, respectively

Shear behaviour of jointed rockmass of sandstone quarry, Mizoram state, India

66-70The shear behaviour of jointed rockmass in a sandstone quarry of south Hlimen, Mizoram were investigated for the slope stability analysis. A computer program was developed in ‘C’ language, to predict the shear strength and dilation of rock joints, based on Barton-Bandis Model. The program further helps to obtain field equivalents of physico-mechanical properties of such rock joints by using laboratory test data. Detailed joints survey were carried out in the field and basic geotechnical description (BGD) of the rockmass was described for rock joints as per recommendations of International Society for Rock Mechanics (ISRM). Tilt apparatus was used to determine the angles of tilt for the natural joint surfaces as well as for the smooth core samples. Jointwall compressive strength (JCS) and uniaxial compressive strength (UCS) of intact rock were also determined by using Schmidt’s hammer. The present study showed that the shear behaviour of the rock joints influenced by many factors, among which joint roughness, level of normal effective stress and size of blocks (joint spacing) are of paramount importance

Artificial neural network as a tool for backbreak prediction

Backbreak is one of the destructive side effects of the blasting operation. Reducing of this event is very important for economic of a mining project. Involvement of various parameters has made the backbreak analyzing difficult. Currently there is no any specific method to predict or control the phenomenon considering all the effective parameters. In this paper, artificial neural network (ANN) as a powerful tool for solving such complicated problems is used to predict backbreak in blasting operation of the Sangan iron mine, Iran. Network training was fulfilled using a collected database of the practiced operation including blast design details and rock condition. Trying various types of the networks, a network with two hidden layers was found to be optimum. Performance of the ANN model was compared with statistical analysis using datasets which were kept apart from the original database. According to the obtained results, for the ANN model there existed a higher correlation (R2 = 0.868) and lesser error (RMSE = 0.495) between the predicted and measured backbreak as compared to the regression model. Also, sensitivity analysis revealed that the inputs rock factor and number of rows are the most and the least sensitive parameters on the output backbreak, respectively. © 2013 Springer Science+Business Media Dordrecht

Evaluation of flyrock phenomenon due to blasting operation by support vector machine

Flyrock is an undesirable phenomenon in the blasting operation of open pit mines. Flyrock danger zone should be taken into consideration because it is the major cause of considerable damage on the nearby structures. Even with the best care and competent personnel, flyrock may not be totally avoided. There are several empirical methods for prediction of flyrock phenomenon. Low performance of these models is due to complexity of flyrock analysis. Support vector machine (SVM) is a novel machine learning technique usually considered as a robust artificial intelligence method in classification and regression tasks. The aim of this paper is to test the capability of SVM for the prediction of flyrock in the Soungun copper mine, Iran. Comparing the obtained results of SVM with that of artificial neural network (ANN), it was concluded that SVM approach is faster and more precise than ANN method in predicting the flyrock of Soungun copper mine. © 2011 Springer-Verlag London Limited