A photoionization detector (PID) is well known for its high sensitivity, large dynamic range, and non-destructive vapor detection capability. However, due to its tardy response, which results from the relatively large ionization chamber and dead volume, the application of the PID in gas chromatography (GC) has been limited. Here, we developed a rapid, flow-through, and highly sensitive microfluidic PID that was microfabricated directly on a conductive silicon wafer. The microfluidic PID has a significantly reduced ionization chamber volume of only 1.3 μL, nearly 10 times smaller than that of state-of-the-art PIDs and over 100 times smaller than that of commercial PIDs. Moreover, it has virtually zero dead volume due to its flow-through design. Consequently, the response time of the microfluidic PID can be considerably shortened, ultimately limited by its residence time (7.8 ms for 10 mL min(-1) and 78 ms for 1 mL min(-1)). Experimentally, the response of the microfluidic PID was measured to be the same as that of the standard flame ionization detector with peak full-widths-at-half-maximum of 0.25 s and 0.085 s for flow rates of 2.3 mL min(-1) and 10 mL min(-1), respectively. Our studies further show that the microfluidic PID was able to detect analytes down to the picogram level (at 3σ of noise) and had a linear dynamic range of six orders of magnitude. Finally, because of the very short distance between the electrodes, low voltage (<10 VDC, over 10 times lower than that in a regular PID) can be used for microfluidic PID operation. This work will open a door to broad applications of PIDs in gas analyzers, in particular, micro-GC and multi-dimensional GC.

We developed a fully automated portable 2-dimensional (2-D) gas chromatography (GC x GC) device, which had a dimension of 60 cm × 50 cm × 10 cm and weight less than 5 kg. The device incorporated a micropreconcentrator/injector, commercial columns, micro-Deans switches, microthermal injectors, microphotoionization detectors, data acquisition cards, and power supplies, as well as computer control and user interface. It employed multiple channels (4 channels) in the second dimension (D) to increase the D separation time (up to 32 s) and henceD peak capacity. In addition, a nondestructive flow-through vapor detector was installed at the end of the D column to monitor the eluent fromD and assist in reconstructing D elution peaks. With the information obtained jointly from theD and D detectors,D elution peaks could be reconstructed with significantly improved D resolution. In this Article, we first discuss the details of the system operating principle and the algorithm to reconstructD elution peaks, followed by the description and characterization of each component. Finally, 2-D separation of 50 analytes, including alkane (C-C), alkene, alcohol, aldehyde, ketone, cycloalkane, and aromatic hydrocarbon, in 14 min is demonstrated, showing the peak capacity of 430-530 and the peak capacity production of 40-80/min.

Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies, its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here, we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity (> 200 at the resolution of 1), high sensitivity (sub-ppb), and rapid analysis capability (~30 min) was designed and made in-house for on-site analysis of patients' breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 min. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes. Keywords Breath analysis. Acute respiratory distress syndrome gas chromatography. 2D GC. Machine learning Menglian Zhou and Ruchi Sharma contributed equally to this work.