Part of the book: Rapid Prototyping Technology
Artificial neural networks (ANNs) are evaluated for spectral interference correction using simulated and experimentally obtained spectral scans. Using the same data set (where possible), the predictive ability of shallow depth ANNs was validated against partial least squares (PLS, a traditional chemometrics method). Spectral interference (in the form of overlaps between spectral lines) is a key problem in large-size, long focal length inductively coupled plasma-optical emission spectrometry (ICP-OES). Unless corrected, spectral interference can be sufficiently severe to the point of preventing precise and accurate analytical determinations. In miniaturized, microplasma-based optical emission spectrometry with a portable, short focal length spectrometer (having poorer resolution than its large-size counterpart), spectral interference becomes even more severe. To correct it, we are evaluating use of deep learning ANNs. Details are provided in this chapter.
Part of the book: Advanced Applications for Artificial Neural Networks
Computational fluid dynamics (CFD) is used extensively in many industries ranging from aerospace engineering to automobile design. We applied CFDs to simulate flows inside vaporization chambers designed for micro- or nano-sample introduction into conventional, lab-based inductively coupled plasmas (ICPs). Simulation results were confirmed using smoke visualization experiments (akin to those used in wind tunnels) and were verified experimentally using an ICP-optical emission spectrometry (ICP-OES) system with a fast-response photomultiplier tube (PMT) detector, an ICP-OES system with a slower-response charge injection device (CID) detector, and an ICP-mass spectrometry (ICP-MS) system. A pressure pulse (defined as a momentary decrease of the optical emission intensity of ICP background) was not observed when employing widely used ICPs either with a CID detector or with ICP-MS. Overall, the simulations proved to be highly beneficial, for example, detection limits improved by as much as five times. Using CFD simulations as a guide, a rapidly prototyped, 3D-printed and smaller-size vaporization chamber (a scaled-down version of that used with ICPs) is being evaluated for potential use with a portable, battery-operated microplasma. Details are provided in this chapter.
Part of the book: Computational Fluid Dynamics
The science and phenomena that become important when fluid-flow is confined in microfluidic channels are initially discussed. Then, technologies for channel fabrication (ranging from photolithography and chemical etching, to imprinting, and to 3D-printing) are reviewed. The reference list is extensive and (within each topic) it is arranged chronologically. Examples (with emphasis on those from the authors’ laboratory) are highlighted. Among them, they involve plasma miniaturization via microplasma formation inside micro-fluidic (and in some cases millifluidic) channels fabricated on 2D and 3D-chips. Questions addressed include: How small plasmas can be made? What defines their fundamental size-limit? How small analytical plasmas should be made? And what is their ignition voltage? The discussion then continues with the science, technology and applications of nanofluidics. The conclusions include predictions on potential future development of portable instruments employing either micro or nanofluidic channels. Such portable (or mobile) instruments are expected to be controlled by a smartphone; to have (some) energy autonomy; to employ Artificial Intelligence and Deep Learning, and to have wireless connectivity for their inclusion in the Internet-of-Things (IoT). In essence, those that can be used for chemical analysis in the field for “bringing part of the lab to the sample” types of applications.
Part of the book: Microfluidics and Nanofluidics