☆ 4.7 Review

Micro gas analyzers for environmental and medical applications

ANALYTICA CHIMICA ACTA (2008)

Journal

ANALYTICA CHIMICA ACTA

Volume 619, Issue 2, Pages 143-156

Publisher

ELSEVIER

DOI: 10.1016/j.aca.2008.05.010

Keywords

micro gas analyzers; environmental and medical applications; atmospheric analysis; breath and skin gas; microchannel scrubbers; micro gas chromatograph; microfluidic devices

Ask authors/readers for more resources

Protocol

Community support

Reagent

Community support

Abstract

In this review, novel microsystems and microdevices to measure gaseous species for environmental analysis and medical diagnostics are described. Miniaturization of analyzers makes field measurements affordable. As well, high sensitivity and good time resolution can be achieved by miniaturization. Some such devices have already been successfully applied to real environmental analyses. Mobile monitoring is available with the use of micro gas analyzers to investigate the natural environment, air pollution and to detect nerve or explosive gases released accidentally or through terrorist activities. Miniature devices are also attractive for medical analyses. Gases produced from the human body reflect gases contained in the blood and certain metabolic conditions. Noninvasive monitoring using miniature devices is available in hospitals and in a patient's home. Many investigations have been conducted using wet and dry chemistry methods for both applications. Instruments employing wet chemistries, which comprise liquid droplets, liquid film, miniature diffusion scrubbers, and microfluidic devices have been studied. Among the instruments using dry methods, miniature samplers, portable gas chromatographs, and microfabricated gas chromatographs have all been investigated. These instruments are expected to usher in a new era of environmental monitoring and will find uses in many medical applications. (C) 2008 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper

Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7

Not enough ratings

Secondary Ratings

Novelty

Significance

Scientific rigor

Rate this paper

Analyzing drop coalescence in microfluidic devices with a deep learning generative model

Kewei Zhu, Sibo Cheng, Nina Kovalchuk, Mark Simmons, Yi-Ke Guo, Omar K. Matar, Rossella Arcucci

Summary: In this study, the use of deep learning generative models is proposed to predict drop coalescence in chemical engineering. By training the predictive models with generated synthetic data, the lack of training data and label imbalance problem can be overcome. A novel generative model called DSCVAE is developed for labelled tabular data, which is capable of generating consistent and realistic samples compared to the standard CVAE. By enhancing random forest and gradient boosting classifiers on synthetic data and evaluating their performances based on real experimental data, numerical results show a considerable improvement in prediction accuracy, with DSCVAE outperforming the standard CVAE. This research provides valuable insights into handling imbalanced data for classification problems, particularly in chemical engineering.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2023)