Transcript Photonic Sensing of the Atmosphere by Absorption

Xiaojuan Cui, Christophe Lengignon, Tao Wu, Weixiong Zhao, Gerard
Wysocki, Eric Fertein, Cecile Coeur, Andy Cassez, Lauence Croize, Weidong
Chen, Yingjian Wang, Weijun Zhang, Xiaoming, Gao, Wenqing Liu, Yujun
Zhang, Fengzhong Dong.
Journal of Quantitative Spectroscopy and Radiative Transfer Volume 113,
Issue 11 2012 1300 - 1316
Presentation by: Ruqayyah Askar
Outline
1) Quantitative assessments of trace gas by absorption spectroscopy.
* Absorption-based concentration measurements
* Instrumental performance characteristics
* Techniques for sensitive spectroscopic detection
- Spectroscopic approaches
- Digital signal processing
* Photonic sensing of the atmosphere by absorption spectroscopy
- Quantitative assessments of gaseous Nitrous acid (HONO)
using QCLs.
2) Wavelength measurements using Michelson Interferometer as a wavemeter.
Absorption-based concentration measurements
Based on Beer–Lambert law, the
absorbance A(ν) at frequency ν can be
expressed as:
A(ν)= ln( I0(ν) / I(ν) )
= Nσ(ν)L
I(ν) : the transmitted probing light intensity
I0(ν): the incident probing light intensity
N : the number of absorbing molecules in [molecules/cm3]
σ(ν) : the frequency-dependent absorption cross-section in [cm2/molecule]
L : the optical absorption path length in [cm]
The integrated absorbance [ in cm−1 ] is
AI= ∫A(ν)dν
=∫ln(I0(ν)/I(ν))dν = NL∫σ(ν)dν = NLS
Where S is the molecule absorption line intensity [ in cm−1/(molecule×cm−2)]
S = ∫σ(ν)dν
Trace concentration of absorbing molecule in the vapor or gas phase is usually
expressed in terms of
- ppmv (parts per million by volume, 10−6)
- ppbv (parts per billion by volume, 10−9)
- pptv (parts per trillion by volume, 10−12)
- ppqv (parts per quadrillion by volume, 10−15)
The gas species concentration C can be retrieved from the integrated absorbance AI measured
at temperature T and pressure P :
C = N/NT
= (AI P0T ) / (NL P T0 L S)
Where the Loschmidt number NL=2.6868×1019 molecules/cm3 at T0=273.15 K and P0=760 Torr.
Instrumental performance characteristics
SNR refers to signal-to-noise ratio. ( 1σ means: SNR=1 )
* Measurement sensitivity
Sensitivity is usually expressed in terms of:
- minimum detectable absorption coefficient (MDAC)
- minimum detectable concentration (MDC)
Where Cmin = C / SNR
Major noise sources:
-
White noise
Thermal noise from electronic detection systems
Quantum ( shot ) noise
-
1/f noise
-
Background fringes noise
Optical interference effects
Optical reflection losses
Instrumental performance characteristics
* Measurement selectivity
High spectral resolution spectroscopic measurements is needed.
Methods are:
1
Selection of spectral lines well isolated from interfering lines of other gas species.
By scanning the frequency of a narrow linewidth laser across the individual
absorption lines in the infrared region.
2
Performing spectral measurements at a reduced pressure to avoid spectral line
overlapping due to pressure-broadening.
3
In the UV–visible spectral region, as molecular absorption exhibits usually broad
band absorption feature, discrimination and concentration retrieval of the target
gases is achieved by the use of a multivariate analysis approach.
Instrumental performance characteristics
* Measurement accuracy
Accuracy influenced by:
Uncertainties on P and T. According to
Uncertainties on absorption path length L.
Uncertainties on molecular line absorption intensity S.
Calibrated standard reference may be used
direct calibration of concentration measurements.
higher accuracy determination of the line parameters
being used for concentration retrieval.
Instrumental performance characteristics
* Measurement precision
Related to:
Stability of the spectroscopic measurements, which can be characterized by Allan
variance analysis.
Measurement errors.
SNR
The most important limiting factor for achieving high precision.
* Dynamic measurement range
Limited by linear response of the detector.
Using a single detector in spectroscopic
absorption-based concentration
High dynamic range detection
By use of strong and weak absorption lines for low and high concentration measurements,
respectively.
Dynamic range approaching 6 orders of magnitude might be achieved.
Techniques for sensitive spectroscopic detection
* Spectral region selection to address strong absorption intensity
In the UV-visible region (250–700 nm), the absorption is due to fundamental electronic
transition associated with vibration-rotation structure.
In the mid-infrared region (2.5–25 μm), almost all molecules exhibit strong absorption
intensities arising from fundamental vibrational–rotational transitions with specific molecular
signature (this region is also called infrared fingerprint region).
Stronger fundamental absorption intensities
sensitive detection of trace gases
Fig. 1. CH4 absorption intensities in the different infrared spectral regions.
Techniques for sensitive spectroscopic detection
** Spectroscopic approaches
In direct measurement of absorption by target gases, a small change ΔI in a large transmitted
probe light intensity (background) I0(ν) is to be measured.
ΔI = I0(ν)–I(ν)
As a result, detection sensitivity is mainly limited by the fluctuations in the detected
background intensity.
Fluctuations in the detected background intensity may be resulted from:
- Probe light excess noise
- System thermal drift
- Optical feedback and interference fringes due to etalon effects.
The background noise limited detectivity is in the range of ΔI/I0(ν)≈10−3, which is very far away
from the requirement of 10−7–10−8 for sensing the environment around us.
Various highly sensitive spectroscopic techniques have been developed and extensively
reviewed.
Techniques currently used for absorption spectroscopy detection:
-
Long path length absorption.
-
Light modulation-phase sensitive detection.
-
Sweep integration approach.
-
Dual-beam balanced detection.
-
Indirect measurements of gas absorption.
Long path length absorption
Lowering the detection limits can also be achieved by enhancing the light-molecule interaction
time.
Long path length absorption
by multiple reflections of probing light in a
multipass optical cell
providing effective optical path length 18-200 m.
or in high fineness optical cavity where light
bounces back and forth thousand times
leading to 1- 10 km long optical absorptions.
Cavity Enhanced Absorption Spectroscopy (CEAS)
Example of a multipass optical cell
Characteristics:
-
-
-
The structure is a combination of White
cell and Herriott cell.
It’s configurations require only standard
mirrors.
Allows a very great number of reflections.
Only limited by the coefficient of reflections of
the mirrors.
The configuration is simple, compact, stable
and cheap.
Injection and recovery of the beams can be on
the side of the two mirrors (configuration I2M)
or on the side of the single mirror (I1M).
Path lengths are controlled by a single
adjustment.
Gives a total path length = 2N(M+1)D
N: number of reflections of each mirror
in Herriott configuration.
M: the number of focused reflection
points between the input and output
points.
D: Distance between mirrors.
[2]
Light modulation-phase sensitive detection ( Lock-in technique )
Low frequency fluctuation in probe light intensity
can significantly degrade the
measurement sensitivity and precision
This technical noise is characterized by a power spectral density proportional inversely to frequency.
It is denoted as 1/f noise ( pink noise or flicker noise).
Reduction of 1/f noise
by using modulation techniques to shift the absorption
signal away from the base band and perform the detection at a relatively high frequency domain for
eliminating the 1/f noise.
By wavelength modulation spectroscopy (WMS) with modulation
frequencies 10–100 kHz.
1) Modulation
By frequency modulation spectroscopy (FMS) employing modulation
frequencies ranging between several MHz up to GHz.
Light modulation-phase sensitive detection ( Lock-in technique )
2) Phase-sensitive detection at harmonics of the modulation frequency
for demodulation of the modulated signal within a narrow lock-in pass band to remove 1/f-noise.
Beyond 100 MHz
1/f-noise contribution can be ignored
Sweep integration approach
Definition:
It’s a signal averaging approach in combination with fast sweeping laser wavelength across the
absorption line(s).
Characteristics:
- A simple but efficient way to remove white noise.
- By averaging N spectra, the noise may be reduced by a factor of N1/2.
- After an optimum value is reached, the instabilities of the instrumental system may nevertheless
counterbalance the noise reduction given by average.
- The optimum averaging time can be determined by an Allan variance analysis.
Fig. 2. (left):Effects of averaging spectra on improvement in SNR: from averaging number of N=10 to N=600,000 [31].
As can be seen, the optimum N is about 5000.When N>5000, fluctuation in spectral baseline appears, resulting from the
system instability; (right): Allan variance plot (black) as a function of averaging time, associated with a 1/t slope
(redline), which shows an optimum averaging time of about 90 s [32].
Dual-beam balanced detection
Description:
Two detectors are basically used to reduce light excess noise through photocurrents cancellation.
Advantages:
- A useful technique for removing excess noise in probe light.
- This technique is capable of reducing common-mode excess light intensity noise by more than
50 dB and yielding a near-shot-noise limited measurement sensitivity.
Disadvantages:
- In practice, inherent difference between two used detectors may dramatically reduce the
improvement in sensitivity.
- Its utilization in multiple-reflection-based long path length absorption spectroscopy is not
efficient.
As the reference beam could not experience the same multiple reflections
Optical noise resulting from the etalon effects would not be
fairly canceled by dual-beam balanced detection.
Indirect measurements of gas absorption
Indirect measurements methods rely on the measurement of absorption induced physical parameter
from which the absorber concentration can be inferred
the effects of fluctuation in probe light intensity on the system detectivity can be minimized.
Techniques for sensitive spectroscopic detection
** Digital signal processing
Digital data processing after data acquisition can be performed to further improve the
analytical SNR.
Examples of applied computer algorithms:
- Averaging and Smoothing
- Passive and Adaptive Filtering
- Fourier transform and correlation analysis
- Digital Lock-in detection
Examples of data processing to be discussed:
- Fringe noise removal.
- Adaptive filtering technique.
Fringe noise removal
Periodic oscillatory structure superimposed on the baseline or absorption feature is a serious issue
to be addressed for sensitive spectroscopic measurements.
Optical fringes:
- Source : Arising from the Fabry-Perot etalon effects among optical components, in
particular when implementing multipass cell or optical cavity approach.
- Solution1: Can be first reduced
by careful optical design and alignment to
avoid optical feedback
by dithering optical elements.
(The best design practice for multipass cells is
to avoid overlap of neighboring spots on the
mirror)
- Solution2: Could be normalized out by a synthetic baseline.
Fig. 3 shows a result reported by Weidong Chen and co-worker.
Fig. 3. Removal of fringe noise from spectral background using synthetic baseline [62].
Left: Experimental spectrum (upper curve). The lower line is a synthetic baseline. Right: water vapor
absorption spectra near 1844/cm after baseline correction (lower curve) by normalizing the
experimental spectrum to the synthetic baseline. The upper line is a theoretical simulation assigned by
using the HITRAN database [5].
Adaptive filtering technique
Further improvements in sensitivity can be obtained by using adaptive digital filter that is
capable of adapting to real measurement environment via self-adjustment of its transfer
function.
Examples of using Kalman filter will be discussed.
As Kalman filter uses a recursive procedure for “true value” prediction based on the previously
determined value, it can be adaptive to real-time noise and data statistics.
Kalman adaptive filter can thus efficiently remove the shot-to-shot fluctuation in the real time
measured data while without affecting the measurements of time evolution of trace gas
concentration (non-stationary).
Example 1: Leleux and coworker successfully
applied Kalman filtering
technique to
simultaneous detection of
NH3 and CO2 with a diodelaser-based sensor
operating at 1.53 μm.
A sensitivity enhancement
of 6 times was achieved.
Example 2: Recently, Tao Wu and co-worker applied Kalman filtering to real-time water isotopic
ratios measurements using laser absorption spectroscopy at 2.73 μm.
They applied discrete Fourier transform to fit a residual spectrum exhibiting sinewave-like
undulation to determine the oscillation frequencies, and then removed the fringes noise from the
baseline with an adapted Fourier filtering.
High measurement precisions (<1‰) have been realized for the oxygen and hydrogen isotopologue
ratios δ18O, δ17O and δ2H in water by sampling the output of a Kalman filter at 1-s time intervals,
while 30-s is needed for the same precision level when using a standard running average technique.
Photonic sensing of the atmosphere by absorption
Spectroscopy
Particular interest is in spectral range of 3–5 μm and 8–12 μm in the mid-infrared (mid-IR).
Reason: Several types of continuous-wave lasers are available as photonic probing sources
for this mid-IR region: - lead-salt diodes
- color-center lasers
- optical parametric oscillators
- difference frequency generation
- quantum cascade lasers ( recently available )
- fiber lasers
QCLs
the most useful laser source for mid-IR gas sensor application nowadays.
- high single-mode output power
- single-frequency tunability
- narrow emission linewidth
- wide spectral coverage
- room temperature operation and commercial availability
Photonic sensing of the atmosphere by absorption
Spectroscopy
Absolute quantitative assessments of gaseous Nitrous acid (HONO)
Technique: Quantum cascade lasers-based long path length absorption
spectroscopy at 8 μm.
A thermoelectric cooled (TEC) VIGO detector
(PVMI-4TE-10.6)
Optical length
up to ∼100 m.
Free spectral range of
∼0.03 cm−1 for relative
wavelength calibration
Probe light operating at
1254.6 cm−1 (∼8 μm).
Fig. 4. (a): QCL-based laser instrument set-up.
A single-mode output
power of up to 35 mW.
Fig. 4. (b) Absorption spectrum of 95 ppm HONO at a pressure of 44 mbar in a 26.3 m long multipass
cell. The laser wavelength was scanned at a rate of 2.5 kHz.
Results:
- Experimentally recorded absorption spectrum of HONO near 8 μm is shown inFig. 4(b).
- A 1σ MDC of 396 pptv was achieved for 1-s integration time by 125 m long path
absorption in a multipass cell.
- The accuracy was estimated to be 10%, mainly limited by accuracy of line intensity of
HONO used for concentration retrieval.
Michelson Interferometer
Basic principle
Incident plane wave:
split into two waves:
by a beam splitter of negligible absorption
( R+T=1)
Fig. 5. Two beam interference in a Michelson
interferometer.
Michelson Interferometer
Amplitudes are determined by:
where
Total Field amplitude coming out is
The detector measures the time-averaged intensity:
Michelson Interferometer as a Wavemeter
Michelson interferometer can be
used for absolute wavelength
measurements.
From the ratio of both counting rates,
we can find the unknown wavelength:
Michelson Interferometer as a Wavemeter
Michelson Interferometer as a Wavemeter
Fig. 6. Traveling Michelson interferometer for accurate measurements of wavelengths of single mode cw lasers.
Sources: