This document summarizes a technique for real-time calibration of portable trace-gas sensors using a rotating in-line gas cell. Key challenges addressed are measurement drift over time from factors like optical instability. Traditional calibration methods lack portability or require separate reference cells. The presented method uses a single gas cell with three sub-cells that rotate between a reference gas, sample gas, and empty cell. Dividing the sample and reference signals by the empty background cell suppresses parasitic interference fringes. Spectral correlation of reference and sample signals over time provides real-time calibration and improved precision without needing wavelength calibration. Future work aims to address detector non-linearity and minimize background signals for field deployment of the calibration technique.

1. CLEO/QELS
June 12, 2012
A Rotational Reference Cell for High-accuracy
Real-time Spectroscopic Trace-gas Sensing
Clinton J. Smith, Wen Wang, and Gerard Wysocki
Dept. of Electrical Engineering, Princeton University, Princeton, NJ 08544
pulse.princeton.edu

2. Project Goal & Outline
The project goal:
• Develop and implement a technique for real-time calibration of
portable trace-gas sensors
 Using a rotating in-line gas cell
 Minimizes sources of drift in real time
http://www.coas.oregonstate.edu/research/po/satellite.gif
Outline
• Key challenges to long-term sensor measurement stability
• Conventional calibration methods
• Overview of the rotational reference cell implementation
• Experimental results
• Conclusions and Future directions
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3. Measurement Noise & Drift Reduce Sensitivity
Measurement drift can be induced
by many factors:
• Fabry-Perot Fringing
 Opto-mechanical instability
Allan Deviation
(sensitive to ambient
temperature change)
 Scattering
• Electronics instability
• Optical power fluctuation
Averaging Time (sec)
Recurring Calibration Required
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4. Traditional Calibration Methods
• Send beam to separate optical
branch
 Use separate reference cell
 Subject to different parasitic
I0
IDet,1
Ambient
Detector 1
fringes
• Multiple detectors
Reference Cell
 With different noise & drift
IDet,2
Detector 2
• Single gas cell
• Single detector
• Cycle between reference and
•
sample gases
 Maintenance challenge
Lack of portability and
autonomy
Ambient
Ref. Gas
I0
Inlet
Outlet
IDet
Detector
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6. Suppress Drifts by Division of Background Signal
Raw Spectra Scans
Reference Path:
Raw Scan with Spectral Fit
I ref ( )  Tref I 0e
 ( b ( ) Lb  ref ( ) La )
Corrected Reference Scan
Background Corrected
Reference Signal
Tref , c ( ) 
Background Path:
I zero ( )  Tzero I 0 e  b ( ) Lb
I ref ( )
I zero ( )

Tref
TZero
e
 ref ( ) La
• Baseline and fringes are suppressed through division
 Same process applies to the sample signal
 Spectral fitting removes baseline but not fringes
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7. Long-term Suppression of Drift
Time Series of Uncorrected and Corrected Signals
Allan Deviation of Uncorrected and Corrected Signals
1 ppmv
1sec, 1σ = 1.75 ppmv
• Measure away from absorption line
•
•
 Assess instrument stability
1 sec. 1σ sensitivity is 3.510-4 (1.75 ppmv)
Sensitivity of 610-5 (0.3 ppmv) after 100 sec. averaging & sustained
past 3000 sec.
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8. Incomplete Correction of Absorption Peak Signal
Time Series of Signals at the Peak and Away
Allan Deviation of Signals at the Peak and Away
1 ppmv
• Measured reference gas stability at and away from absorption peak
• Long-term drift remains for on-line measurements
• Full spectral fit of corrected reference also shows drift
•
 Improves 1 sec sensitivity ~2 to 210-4 (1 ppmv)
 Uses full spectral information
Background signal 4-5 reference signal
 Difficult to suppress but can calibrate sample against reference
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9. Baseline Drift & Error Correction
Background-Corrected Scans at
Different Experiment Times
Scatter Plots Tsample,c(Tref,c)
Before & After Baseline Correction
• Baseline drift introduces error into single spectral point measurements
•
 Differences in reference & sample baseline also introduce error
Use Sample-Reference regression + fundamental principles to correct
 At 100% transmission the fit of Tsample,c(Tref,c) should intersect (1,1) coordinate
 Scale spectra to meet this condition
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10. Calibration Through Spectral Correlation
Scatter Plots Tsample,c(Tref,c)
Before & After Baseline Correction
TS (t n )  mTR (t n )  y0
Baseline
Corrected
Transmissions
TR (tn )  Tref , c (tn )
Bsim
Bmeas
TS (t n )   Tsample,c (t n )
Bsim,meas are simulated & measured baselines
β is a modeled transmission correction factor
• Use slope (m) of TS(TR) to calibrate sample concentration.
 Slope is proportional to the ratio of analyte concentrations.
[CO2 ]sample  m  [CO2 ]ref
• Point-by-point spectral correlation of time domain data.
 Uses all spectral data (like a spectral fit).
 Frequency calibration not needed.
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11. Single point, spectral fit, & spectral correlation
Time Series Showing Effects of Calibration
Allan Deviation Showing Effects of Calibration
Drift
reduction
• Single point & spectral correlation calibration suppress drift
• Full spectral fit shows ~6 % offset
•
 Consistent with sample and reference baseline differences
 1.7 increase in 1s sensitivity (2.35 ppmv to 1.33 ppmv)
 Drift remains
Spectral correlation calibration has accuracy & precision of fit.
 Same offset as fit
 Removes baseline error without frequency calibration.
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12. Conclusion and Future Work
• A novel in-line drift suppression & calibration technique.
•
•
 Uses a rotating in-line gas cell.
 Provides real-time calibration.
Divide the sample (reference) signals by the background spectrum.
 Three sub-cells share the same optical interfaces.
 Parasitic interference fringes are minimized.
Spectral correlation calibration technique.
 Maintains concentration retrieval accuracy.
 Improves measurement precision.
 Uses entire spectrum without wavelength calibration.
Future Improvements
• Address detector nonlinearity
• Minimize background signal magnitude with solid optical waveguides
 Calibration in a controlled atmosphere
 Operation at the Brewster’s angle to further suppress fringes
 Field deployment
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13. Acknowledgements
This work was sponsored in part by:
The National Science Foundation’s MIRTHE Engineering Research Center
An NSF MRI award #0723190 for the openPHOTONS systems
An innovation award from The Keller Center for Innovation in Engineering
Education
National Science Foundation Grant No. 0903661 “Nanotechnology for Clean
Energy IGERT”
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