Digital Manufacturing for Microfluidics.

The microfluidics field is at a critical crossroads. The vast majority of microfluidic devices are presently manufactured using micromolding processes that work very well for a reduced set of biocompatible materials, but the time, cost, and design constraints of micromolding hinder the commercialization of many devices. As a result, the dissemination of microfluidic technology-and its impact on society-is in jeopardy. Digital manufacturing (DM) refers to a family of computer-centered processes that integrate digital three-dimensional (3D) designs, automated (additive or subtractive) fabrication, and device testing in order to increase fabrication efficiency. Importantly, DM enables the inexpensive realization of 3D designs that are impossible or very difficult to mold. The adoption of DM by microfluidic engineers has been slow, likely due to concerns over the resolution of the printers and the biocompatibility of the resins. In this article, we review and discuss the various printer types, resolution, biocompatibility issues, DM microfluidic designs, and the bright future ahead for this promising, fertile field.

Assessment of spatial distribution of soil heavy metals using ANN-GA, MSLR and satellite imagery.

This study aims to assess and compare heavy metal distribution models developed using stepwise multiple linear regression (MSLR) and neural network-genetic algorithm model (ANN-GA) based on satellite imagery. The source identification of heavy metals was also explored using local Moran index. Soil samples (n = 300) were collected based on a grid and pH, organic matter, clay, iron oxide contents cadmium (Cd), lead (Pb) and zinc (Zn) concentrations were determined for each sample. Visible/near-infrared reflectance (VNIR) within the electromagnetic ranges of satellite imagery was applied to estimate heavy metal concentrations in the soil using MSLR and ANN-GA models. The models were evaluated and ANN-GA model demonstrated higher accuracy, and the autocorrelation results showed higher significant clusters of heavy metals around the industrial zone. The higher concentration of Cd, Pb and Zn was noted under industrial lands and irrigation farming in comparison to barren and dryland farming. Accumulation of industrial wastes in roads and streams was identified as main sources of pollution, and the concentration of soil heavy metals was reduced by increasing the distance from these sources. In comparison to MLSR, ANN-GA provided a more accurate indirect assessment of heavy metal concentrations in highly polluted soils. The clustering analysis provided reliable information about the spatial distribution of soil heavy metals and their sources.