Characterization of rapidly-prototyped, battery-operated, argon-hydrogen microplasma on a hybrid chip for elemental analysis of microsamples by portable optical emission spectrometry

Weagant, Scott; Dulai, G.; Li, Lü; Karanassios, Vassili

doi:10.1016/j.sab.2015.01.009

Cited by 33 publications

(17 citation statements)

References 32 publications

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“…We have been developing and characterizing miniaturized plasmas in the form of microplasmas [41][42][43][44][45][46][47][48] that we fabricated using a variety of fabrications technologies [49][50][51][52] for taking part of lab to the sample types of applications [53][54][55][56][57][58][59][60]. Microplasmas are arbitrarily defined as those with one critical dimension in the micrometer regime.…”

Section: Why Miniaturization?mentioning

confidence: 99%

Artificial Neural Networks (ANNs) for Spectral Interference Correction Using a Large-Size Spectrometer and ANN-Based Deep Learning for a Miniature One

Li¹,

Zhang²,

Mohua³

et al. 2018

Advanced Applications for Artificial Neural Networks

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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.

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Section: Why Miniaturization?mentioning

confidence: 99%

Artificial Neural Networks (ANNs) for Spectral Interference Correction Using a Large-Size Spectrometer and ANN-Based Deep Learning for a Miniature One

Li¹,

Zhang²,

Mohua³

et al. 2018

Advanced Applications for Artificial Neural Networks

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“…Unlike ICPs discussed thus far that are tethered to a lab floor because they are bulky and heavy and because they require 1-2 kW of electrical power and consume about 20 L/min of expensive Ar gas (Figure 1), we have been developing battery-operated microplasmas we made using a variety of technologies ranging from semiconductor fabrication to 3D printing [24][25][26][27][28][29][30][31][32][33][34][35]. Microplasmas are those with one critical dimension in the micrometer (i.e., sub-mm) regime.…”

Section: A Smaller Chamber For Microplasmas Used In the Fieldmentioning

confidence: 99%

“…Microplasmas are those with one critical dimension in the micrometer (i.e., sub-mm) regime. We have been characterizing them [24][25][26][27][28][29][30][31][32][33][34][35], primarily for "taking part of the lab to the sample" types of applications, i.e., for analytical measurements on-site. Future microplasmabased spectrometers are envisioned to have wireless capability [36,37] and (some) energy autonomy [38][39][40][41][42].…”

Section: A Smaller Chamber For Microplasmas Used In the Fieldmentioning

confidence: 99%

Computational Fluid Dynamics (CFD) Applied to a Glass Vaporization Chamber for Introduction of Micro- or Nano-Size Samples into Lab-Based ICPs and to a CFD-Derived (and Rapidly Prototyped Via 3D Printing) Smaller-Size Chamber for Portable Microplasmas

Badiei¹,

Stubley²,

Fitzgerald³

et al. 2018

Computational Fluid Dynamics - Basic Instruments and Applications in Science

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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.

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“…The main focus of what is reported here will be on microplasmas we developed [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] on flat 2D-chips [21,22,25,26] and on 3D-chips [15,19,20] including those that were rapidly prototyped [15,23] using 3D-printing [27] for possible use for Cr-speciation onsite (i.e., in the field). More specifically two questions will be addressed.…”

Section: Microplasmas and Instrumentation For Cr-speciationmentioning

confidence: 99%

“…Concept diagram used for recording the Cr spectrum from 200 nm to 850 nm (from[15] with permission).…”

mentioning

confidence: 99%

Chromium speciation using large scale plasmas in a lab and towards field deployable speciation by employing a battery-operated microplasma-on-a-chip and optical emission spectrometry

German

Karanassios

2015

SPIE Proceedings

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Chemical speciation is defined as the determination of the concentration of an analyte (e.g., Chromium or Cr) in the oxidation state in which it exists in the environment (e.g., sea water). Determinations of the concentration of different Cr-species is important due to toxicity differences of the different oxidation states of Cr. For example, Cr(III) is regarded as generally non-toxic and is considered as an essential micro-nutrient. But Cr(VI) is considered as carcinogenic. In this paper, speciation methods for Cr in sea water samples using large scale plasmas, such as an ICP (Inductively Coupled Plasma) and steps taken toward using a microplasma are described.

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Characterization of rapidly-prototyped, battery-operated, argon-hydrogen microplasma on a hybrid chip for elemental analysis of microsamples by portable optical emission spectrometry

Cited by 33 publications

References 32 publications

Artificial Neural Networks (ANNs) for Spectral Interference Correction Using a Large-Size Spectrometer and ANN-Based Deep Learning for a Miniature One

Artificial Neural Networks (ANNs) for Spectral Interference Correction Using a Large-Size Spectrometer and ANN-Based Deep Learning for a Miniature One

Computational Fluid Dynamics (CFD) Applied to a Glass Vaporization Chamber for Introduction of Micro- or Nano-Size Samples into Lab-Based ICPs and to a CFD-Derived (and Rapidly Prototyped Via 3D Printing) Smaller-Size Chamber for Portable Microplasmas

Chromium speciation using large scale plasmas in a lab and towards field deployable speciation by employing a battery-operated microplasma-on-a-chip and optical emission spectrometry

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