A real-time, nondestructive mid-infrared (mid-IR) platform was proposed for isotopic methane detection. The measurement system consisted of a tunable mid-IR laser, a miniaturized gas chamber, and a mid-IR signal receiver. The isotope ratio of the
12
C
H
4
/
13
C
H
4
was identified by measuring the mid-IR spectrum at
λ
=
3
.
2
−
3
.
5
µ
m
. In-situ
12
C
H
4
/
13
C
H
4
monitoring was then achieved by tracing the characteristic mid-IR absorption peaks assigned to the
12
C
H
4
at
λ
=
3.328
µ
m
and
13
C
H
4
at
λ
=
3.340
µ
m
. The real-time methane isotope analysis can be applied to environmental monitoring and petroleum industries.
An intelligent mid-infrared (mid-IR) integrated photonic device was demonstrated applying a machine learning (ML) algorithm. The design model and the estimation model of mid-IR micro-rings were trained by the artificial neural network (ANN) to create the performance-structure relationships. The sensing devices were then designed to align the micro-ring resonance with the characteristic mid-IR absorption wavelengths according to the gases of interest. Further applying the cascade micro-ring structures enables the device to monitor several gas analytes simultaneously. The ML-based mid-IR device provides a miniaturized sensing platform for remote and precise environmental monitoring.
In-Situ gas analysis were illustrated by using mid-Infrared (mid-IR) gradient Bragg grating cavities. Gas mixtures were noninvasively monitored by tracing the characteristic gas absorption bands with algorithmic analysis.
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