The present paper describes a system and method for indirect emission spectroscopy of CO2 in the visible spectrum. This is achieved by using a microplasma spectrometer that first converts CO2 into CO and then measures emissions from the CO Ångström system (B1Σ+ → A1Π) at 560 nm. The experiments were performed on gaseous samples of CO2, mixed in both N2 and air, to concentrations between 0.01% and 100%. In addition to the microplasma spectrometer, the process was monitored by mass spectrometry with a residual gas analyzer. The CO2 to CO conversion efficiency was found to be very high, reaching a maximum of 41% at close to 100% selectivity. Furthermore, the CO Ångström system was shown to facilitate excellent spectroscopic measurement of CO2 concentrations below 10%, with a linearity of R2 > 0.99 and an expected limit of detection in the parts-per-thousands range. The most promising aspect of the results was that the analysis was performed on extremely small total sample amounts where the gas flow through the systems was in the 0.1 µmole/s range. Hence, the present system has the prospect of filling a void in current sensor technology, where inexpensive and easy-to-use optical systems, such as nondispersive infrared sensors, cannot handle small sample amounts, while mass spectrometers, which can handle such samples, still are expensive, complex, and bulky.

Carbon dioxide is probably the most common sample molecule in gas spectroscopy with applications spanning from regulating ventilation systems1 and tracking industrial processes2 to environmental monitoring3 or even space science.4 The most common sensors are based on nondispersive infrared (NDIR) spectroscopy and can easily and accurately detect and quantify CO2 down toward a few ppm.5 More precise optical instruments based on, e.g., cavity ringdown spectroscopy6 or off-axis integrated cavity output spectroscopy7 can reduce the limit of detection orders of magnitude more.

Although most of these sensors strive toward minimizing dead volumes, they all require fairly large total sample amounts to perform an accurate analysis. Hence, in applications where the total sample amount is inherently limited, e.g., when analyzing single cells or rare isotopes, other methodology and technology are required. Traditionally, mass spectrometry has been used, even though it lacks the simplicity of the optical methods, particularly due to the need for high-vacuum systems.

In this context, spectroscopic methods based on microplasma technology can offer an advantage, since they have the capability of combining the simplicity of the optical sensors with the small sample processing of the mass spectrometers thanks to their minute internal volume.8 We have previously investigated one such method called optogalvanic spectroscopy, which proved to be extremely effective with respect to both minimum detectable absorption and noise equivalent absorption sensitivity and capable of handling samples in the subnanogram range.9 However, optogalvanic spectroscopy relies on IR lasers that are both complex and expensive.

An alternative method could be emission spectroscopy, where different species can be detected and quantified by analyzing the radiation that is emitted from the plasma. In such a system, the emitted radiation is dispersed on a grating or in a prism, and the individual intensities of the diffracted or refracted wavelengths are analyzed by a photodetector array. From an experimental point of view, the best wavelengths for such an analysis are in the mid- and far-IR regime, where CO2 exhibits ro-vibrational transitions with good selectivity toward other common species. However, this wavelength regime is challenging from a technical point of view, where, particularly, the detector starts to become a problem, since high density IR photodetector arrays are still expensive and complex to fabricate and employ.10,11 In the visible wavelength regime, on the other hand, there is an abundance of high-performance inexpensive photodetectors as a result of the last decade’s immense development in digital camera technology. However, here, CO2 lacks useful emissions.

In this paper, we investigate a way of circumventing these challenges and facilitate CO2 emission spectroscopy in the visible wavelength regime. To achieve this, we rely on another property of the plasma, namely, its chemical reactivity, particularly its ability to dissociate CO2 molecules into CO –that happen to have several useful transitions at visible wavelengths.12 We have focused on the so-called Ångström system (B1Σ+ A1Π) that was first observed by Anders Ångström—the namesake of our laboratory—in 187513 and exhibits strong emission bands between 450 and 650 nm.14 In order to evaluate the usefulness of the proposed method for CO2 spectrometry, we investigate the ability of the plasma to reliably convert CO2 to CO using a residual gas analyzer (RGA) and the applicability of the CO Ångström system to emission spectroscopy using a microplasma emission spectrometer.

The measurement system, Fig. 1, was based on a microplasma emission spectrometer (Pithos, Fourth State Systems AB, Sweden) containing a stripline split-ring resonator microplasma source15 with an operating pressure between 0.075 and 7.5 Torr and a CCD spectrometer with a bandwidth of 200–1000 nm, a FWHM of 2 nm at 633 nm, a 20 μm by 2 mm slit, and a 600 lines/mm and 800 nm blaze grating. The microplasma source was fed with a sample gas though a 190 mm long and 40 μm diameter capillary, from a reference gas volume consisting of a 100 ml vial with a 23 G cannula that was filled with different mixtures of CO2, N2, and air. The outlet of the spectrometer was connected to a 170 l/s turbo vacuum pump (TCP 300, Pfeiffer Balzers, Germany) creating a net sample flow of about 0.1 μmol/s through the system.

FIG. 1.

Schematics of the experimental setup, showing fluidic, data, and optical connections.

FIG. 1.

Schematics of the experimental setup, showing fluidic, data, and optical connections.

Close modal

The fluidic outlet of the spectrometer was also connected to the inlet of a residual gas analyzer (Model XT100, Extorr Inc., PA, USA) though another capillary, this time 320 μm in diameter and 400 mm long. The purpose of both capillaries was to reduce the pressure, first from atmosphere to 0.75 Torr inside the spectrometer and then from 0.75 to <10−6 Torr in the RGA. The latter two pressures were monitored by separate gauges (AMS-5915, Analog Microelectronics, Germany and PKR250, Pfeiffer Balzers, Germany). In the experiments, the RGA was used to monitor the partial pressure of three molecular masses, M, that corresponded to N2 and CO, O2, and CO2, i.e., M = 28, 32, and 44.

Finally, the microplasma source, spectrometer, and RGA were connected to a computer (Yoga 530, Lenovo, China) to control the pressure and power in the former and record the outputs of the two latter. These outputs were then postprocessed as described below.

A total of 27 different samples were analyzed in the study (Table I). First, N2 was mixed with CO2 in 10% steps from 0% to 100%. The mixing was performed in the vials using their incorporated volume grading. Before mixing, the vial was rinsed with pure N2, by filling and emptying it thrice. Then, the vial was filled with 100 ml of the minority species and emptied to the correct volume fraction. Finally, the vial was filled with the majority species up to 100 ml. Throughout filling and handling, the vials were fitted with a 600 μm diameter cannula through which the inlet capillary of the microplasma emission spectrometer could be inserted. The small cross section of the cannula and large volume of the vial limited diffusion and stabilized the concentration of the reference gas within ± 1% over time spans relevant to the experiment. In addition to N2, CO2 was mixed with air sampled from the laboratory to investigate how the plasma chemistry of a more complex gas mixture affected the results. Finally, experiments with low concentrations of CO2 in N2 were performed to probe limits of detection and quantification. These samples were diluted from starting points of 10% and 50% CO2 by emptying the vial to 10 ml and refilling it with N2 to 100 ml until the decried fraction was reached. Due to repeated mixing, the composition of these samples suffered from a higher degree of uncertainty.

TABLE I.

Sample mixtures of CO2, N2 and air used in the experiments.

SampleCO2N2Air
1.0 
0.9 0.1 
0.8 0.2 
0.7 0.3 
0.6 0.4 
0.5 0.5 
0.4 0.6 
0.3 0.7 
0.2 0.8 
10 0.1 0.9 
11 1.0 
12 1.0 
13 0.1 0.9 
14 0.2 0.8 
15 0.3 0.7 
16 0.4 0.6 
17 0.5 0.5 
18 0.6 0.4 
19 0.7 0.3 
20 0.8 0.2 
21 0.9 0.1 
22 5 × 10−2 0.95 
23 1 × 10−2 0.99 
24 5 × 10−3 >0.99 
25 1 × 10−3 >0.99 
26 5 × 10−4 >0.99 
27 1 × 10−4 >0.99 
SampleCO2N2Air
1.0 
0.9 0.1 
0.8 0.2 
0.7 0.3 
0.6 0.4 
0.5 0.5 
0.4 0.6 
0.3 0.7 
0.2 0.8 
10 0.1 0.9 
11 1.0 
12 1.0 
13 0.1 0.9 
14 0.2 0.8 
15 0.3 0.7 
16 0.4 0.6 
17 0.5 0.5 
18 0.6 0.4 
19 0.7 0.3 
20 0.8 0.2 
21 0.9 0.1 
22 5 × 10−2 0.95 
23 1 × 10−2 0.99 
24 5 × 10−3 >0.99 
25 1 × 10−3 >0.99 
26 5 × 10−4 >0.99 
27 1 × 10−4 >0.99 

The spectroscopy and RGA measurements were performed in parallel beginning with a 20 s RGA analysis of the gas composition of the sample without plasma. Then, the plasma was ignited at a power, P, of about 38 dBm, after which both the spectroscopic and RGA signals were recorded while decreasing the power in six steps (37, 36, 34, 28, and 23 dBm). Below the last step, the plasma went out. The dwell time at each power step was 20 s for samples 1–21 and 40 s for samples 22–27. Moreover, the integration time of the CCD spectrometer was 0.10 s for samples 1–21 and 0.15 s for samples 22–27. The corresponding change in the intensity was linear with respect to integration time.

The sampling frequency of the spectrometer and the RGA were slightly different (0.2–1 s for the spectrometer and 3.7 s for the RGA). Moreover, the response of the RGA was slightly delayed with respect to the plasma due to column effects in the interconnecting capillary. Hence, the results were interpolated to a common frequency in the postprocessing. In order to synchronize the measurements, the times when the plasma went on and off—an effect that was clearly visible in both signals—were measured manually and used as calibration points.

Figure 2 shows typical spectra for pure CO2 (sample 1) as well as pure N2 (sample 11). As can be seen, the Ångström system of CO was clearly visible with distinct emissions from five bands corresponding to ν = 0–4 between 440 and 610 nm. Furthermore, N2 showed limited background at these wavelengths, which is advantageous from a spectroscopy point of view.

FIG. 2.

Typical spectra of pure CO2 and N2. The CO Ångström system with bands corresponding to ν between 0 and 4 are marked in the figure.

FIG. 2.

Typical spectra of pure CO2 and N2. The CO Ångström system with bands corresponding to ν between 0 and 4 are marked in the figure.

Close modal

Figure 3 shows two of the Ångström bands—at 520 and 560 nm—in more detail. More thorough analysis of the N2 background showed that the 520 nm band (ν = 2) was generally stronger but also perturbed by an N2 band at 518 nm. The 560 nm band, on the other hand, had a more predictable background. Hence, it was used for the subsequent spectrometric calculations despite its lower brightness. The spectroscopic CO signal of the 560 nm band, ICO, was calculated in three steps, where (1) the spectrum was filtered with respect to both wavelength and time with smoothing average filters of power 2 and 7, respectively. (2) The background was subtracted by fitting a fourth order Gaussian to the spectrum in the wavelength interval λ = 552–564 nm, but excluding points between λ = 558 and 562 nm. (3) Finally, ICO was calculated as the integral of the latter interval. The lower inset of Fig. 10 visualizes these calculations in more detail.

FIG. 3.

Close-up of the (0–2) and (0–3) bands of the Ångström system at descending carbon dioxide concentration.

FIG. 3.

Close-up of the (0–2) and (0–3) bands of the Ångström system at descending carbon dioxide concentration.

Close modal

Figure 4 shows typical RGA measurements at varying plasma power. These were subject to several background effects primarily stemming from leaks in the setup and overlapping molecular masses. Looking at Fig. 4, it appears that the partial pressure, p, of CO2 and CO in sample 1 (100% CO2) was about the same before igniting the plasma and that the CO pressure then increased at increasing power with a corresponding reduction of CO2. However, CO has the same molecular mass as N2, M = 28, which was the major leak species. Moreover, the measurement of sample 11 (100% N2) showed that the N2 pressure was more or less unaffected by the plasma. Hence, it was reasonable to assume that pCO+N2 of sample 1 before plasma ignition was purely due to background N2 and that the change in pressure after plasma ignition was purely due to CO. Furthermore, sample 11 showed a slight background of CO2, which, again, was assumed to be due to background effects. Hence, pCO2 of sample 11 was subtracted from all pCO2 measurements to get accurate results. Similarly, pCO+N2 before plasma ignition of sample 1 was subtracted from all measured N2 pressures.

FIG. 4.

RGA measurements of partial pressures, p, corresponding to M = 44 (squares), i.e., CO2, and M = 28 (diamonds), i.e., CO and N2, at different plasma power, P, for pure CO2 (white markers) and pure N2 (black markers). The plasma was on at powers >20 dBm as indicated by the dashed line.

FIG. 4.

RGA measurements of partial pressures, p, corresponding to M = 44 (squares), i.e., CO2, and M = 28 (diamonds), i.e., CO and N2, at different plasma power, P, for pure CO2 (white markers) and pure N2 (black markers). The plasma was on at powers >20 dBm as indicated by the dashed line.

Close modal

Applying these adjustments to all 66 measurement points of samples 1–11 and adding the resulting partial pressures of CO2, CO, and N2, the total pressure stemming from the plasma became pTot=0.47±0.01μTorr, where the very small standard deviation strongly indicated that the background subtraction method was viable. Examples of adjusted RGA measurements can be seen in Fig. 5.

FIG. 5.

Adjusted RGA measurements of samples 2 (white markers) and 10 (black markers).

FIG. 5.

Adjusted RGA measurements of samples 2 (white markers) and 10 (black markers).

Close modal

Figure 6 shows the combined results of all measurements. The different panels depict the dependencies of the investigated partial pressures, pCO2andpCO, and the spectroscopic signal, ICO, with respect to power and CO2 sample concentration. Here, some general trends can be observed where, e.g., the plasma's efficiency to dissociate CO2 into CO was the greatest for low CO2 concentrations and high power.

FIG. 6.

Partial pressures of CO2 (a) and CO (b), pCO2 and pCO, as functions of plasma power, P, and CO2 concentration. The resulting relative amount of produced CO is shown in (c), while (d) shows the emission intensity of the (0–3) Ångström band of CO, ICO, under the same conditions.

FIG. 6.

Partial pressures of CO2 (a) and CO (b), pCO2 and pCO, as functions of plasma power, P, and CO2 concentration. The resulting relative amount of produced CO is shown in (c), while (d) shows the emission intensity of the (0–3) Ångström band of CO, ICO, under the same conditions.

Close modal

In fact, the plasma was generally effective in converting CO2 into CO, with maximum, average, and minimum conversion efficiencies, Y=(pCOpCO(P<20dBm))/pCO2(P<20dBm), of 41%, 28%, and 5.3%, for samples mixed in N2, and 35%, 25%, and 5.1%, for samples mixed in air. Moreover, the selectivity of the conversion, i.e., the fraction of CO2 that was converted to CO, S=(pCOpCO(P<20dBm))/(pCO2(P<20dBm)pCO2), was equally high with S = 100 ± 3.5% for samples mixed in N2 and S = 91 ± 1.6% for samples mixed in air.

CO2 to CO conversion is a scientific field of its own, since it constitutes a promising reaction in artificial photosynthesis, and the produced CO is an important part of syngas that, in turn, can be used to produce an abundance of different hydrocarbons.16–18 Generally, conversion efficiencies of Y > 20% are regarded as very promising, particularly if they are combined with high selectivity.19 This would put the present system in a very promising position. However, the overall energy efficiency, i.e., the supplied plasma power per converted molecule, was rather low (<1%), primarily due to the very low gas flow through the system. Here, it should be pointed out that the current flow rate was intentionally low, since the proposed system and method were intended for applications where the total available sample amount is inherently limited. The current flow rate was chosen to have a good coupling to the RGA, while the spectrometer worked well with both an order of magnitude higher and lower flows. Nonetheless, additional investigations of the conversion efficiency at higher flow rates should be performed to evaluate the system potential as a CO2 to CO converter. Such experiments are currently under way in our laboratory.

The samples mixed with air showed slightly lower CO2 and CO concentrations after passing the plasma than those mixed with N2. This was likely due to the more diverse nature of the air samples, where particularly the presence of O2 allowed for more complex chemistry. However, both contents were only shifted by a constant factor going from N2 to air as shown in Fig. 7. Hence, this effect had no impact on the spectrometric usefulness of the systems.

FIG. 7.

Relative content of CO2 and CO in the samples mixed with N2 (samples 1–11) and air (samples 12–21) at constant power, as measured by the RGA.

FIG. 7.

Relative content of CO2 and CO in the samples mixed with N2 (samples 1–11) and air (samples 12–21) at constant power, as measured by the RGA.

Close modal

Another distinct feature was the saturating behavior of the CO production rate at high CO2 concentrations. Figure 8 shows pCO at different CO2 sample concentrations for two different powers. As can be seen, the CO production rate started to saturate at CO2 concentrations higher than ∼50%. This saturation behavior could be mitigated by mixing the CO2 in air instead of N2, which suggested that the cause of the saturation likely was due to the plasma chemistry, where the presence of N2 affected reaction time constants that promoted CO production, and the presence, or lack, of O2 and/or other oxygen species confined the amount of CO produced. Moreover, the saturation behavior could also be mitigated by increasing the power, where the amount of produced CO was more or less linear with respect to supplied power at high CO2 concentrations as can be seen from the inset of Fig. 8, although it should be pointed out that the power was measured in dBm and not watts.

FIG. 8.

CO production as a function of CO2 concentration for samples mixed in N2 (black markers) and air (white markers). The inset shows the relationship between CO production and plasma power for samples 2 and 10.

FIG. 8.

CO production as a function of CO2 concentration for samples mixed in N2 (black markers) and air (white markers). The inset shows the relationship between CO production and plasma power for samples 2 and 10.

Close modal

A similar saturation was observed in the emission intensity of the (0–3) Ångström band of CO, ICO, as seen in Fig. 9. The fact that the relationship between ICO and pCO was close to linear, the inset of Fig. 9, suggested that the cause of the saturation was the same as for the CO production. Moreover, increasing the power increased the dynamic range of the measurement from 0% to 50% CO2 at P = 28 dBm up to 0%–80% at P = 37 dBm, defining the dynamic range as the linear regime with R2 > 0.95.

FIG. 9.

Emission intensity of the (0–3) Ångström band of CO, ICO, as a function of CO2 concentration for samples 1–11 (black markers) and 12–21 (white markers), respectively. The inset shows the relationship between ICO and the partial pressure of CO, pCO.

FIG. 9.

Emission intensity of the (0–3) Ångström band of CO, ICO, as a function of CO2 concentration for samples 1–11 (black markers) and 12–21 (white markers), respectively. The inset shows the relationship between ICO and the partial pressure of CO, pCO.

Close modal
FIG. 10.

Emission intensity of the (0–3) Ångström band of CO, ICO, as a function of CO2 concentration for samples 1–11 (white markers) and 22–27 (black markers) at the linear (left) and logarithmic (right) scale. The dashed lines are linear fits. The inset shows how ICO was calculated, where the dashed line is the filtered spectrum, the dashed–dotted line is the fitted background, and the solid line is the CO band after removing the background. ICO corresponded to the integral of the latter. The average power in all experiments was 37 dBm.

FIG. 10.

Emission intensity of the (0–3) Ångström band of CO, ICO, as a function of CO2 concentration for samples 1–11 (white markers) and 22–27 (black markers) at the linear (left) and logarithmic (right) scale. The dashed lines are linear fits. The inset shows how ICO was calculated, where the dashed line is the filtered spectrum, the dashed–dotted line is the fitted background, and the solid line is the CO band after removing the background. ICO corresponded to the integral of the latter. The average power in all experiments was 37 dBm.

Close modal

Figure 10 shows the combined data of samples 1–11 and 22–27, which exhibited a linear dependence on the CO2 concentrations up to concentrations of 10% after which the dependence became exponentially decreasing. Hence, despite the mitigations addressed above, the proposed spectrometric method cannot be regarded as promising for measuring high concentrations of CO2. At lower concentrations, on the other hand, the measurement showed a close to linear relationship to the CO2 concentration with R2 > 0.99 (Fig. 10). These results should still be regarded as preliminary but suggest that the method is currently capable for measuring CO2 concentrations down toward the part-per-thousands range. However, it is also clear that, even though the precision is good, the accuracy has to be improved.

Further studies of the limit of detection of the method should be performed on premixed standard samples, since the mixing process used in this paper started to become increasingly inaccurate below concentrations of ∼1%. Moreover, more complex sample compositions than CO2 in N2 should be investigated in more detail to see how they affect the accuracy of the measurement. Still, it should be pointed out that the analysis in this paper was based on only one emission band in a vast spectrum—the inset of Fig. 10 shows in more detail how ICO was calculated. In fact, we are currently using only about 1.5% of the collected spectrometric data. Hence, we believe that more complex postprocessing has that potential of improving the limits of detection and quantification several orders of magnitude and intend to investigate this by using methods for multivariate data analysis and machine learning.

Finally, it should be pointed out that the present system and method suffers from some inherent limitations of its own. Particularly, it cannot distinguish CO converted from CO2 from CO that was already in the sample before the conversion. Hence, it requires some postulated knowledge of the sample composition. Still, most potential applications, e.g., studies of metabolism in small cell cultures, or rare isotope ratios, fulfill this postulate; therefore, the proposed system should be of interest to the scientific community despite this limitation. Moreover, the operating pressure of the microplasma source requires the system to have a pump, making it more complex than, e.g., diffusion based NDIR spectroscopy systems. Still, it requires a much less high-end pump than a mass spectrometer. In the present study, the microplasma source was pumped with the rough pump of the RGA turbo-pump system. It is also possible to increase the operating pressure of the plasma up to 75 Torr by changing its geometry. This would enable the use of much simpler pumps, e.g., membrane pumps. However, increasing the operating pressure to these levels will likely also affect the spectroscopic properties of the plasma. Hence, an investigation of the effects of increased pressure on the results reported in this study is currently under way in our laboratory.

A spectroscopic method to indirectly detect and quantify CO2 in the visual spectrum by utilizing emissions of dissociated CO in a microplasma was studied and found to be useful, particularly for CO2 concentrations between 0.1% and 10%. The CO2 to CO conversion efficiency of the system was very high, reaching above 40% with selectivities close to 100%. Furthermore, the (0–3) Ångström band of CO was shown to facilitate spectroscopic measurement with high precision and linearity over the full investigated range for CO and below concentrations of 10% for CO2. Achieving such performance at sample flow rates in the 0.1 μmol/s regime suggests that, even though the investigated system and method could not compete with, e.g., NDIR spectroscopy in large scale applications, it has great potential to become useful when the total available sample amount is inherently limited.

Erika Åkerfeldt, Ragnar Seton, and Karl Håkansson are kindly acknowledged for their help in the laboratory. The Swedish National Space Agency (No. 104/14) and FORMAS (No. 2016-00706) are acknowledged for their financial support to the project. This is part of ATTRACT that has received funding from the European Union's Horizon 2020 Research and Innovation Program. The microplasma spectrometer used in the experiments was made by the company Fourth State Systems AB, which is co-owned by the authors.

1.
J.
Kwon
,
G.
Ahn
,
G.
Kim
,
J. C.
Kim
, and
H.
Kim
, in 2009 ICCAS-SICE (IEEE, 2009), pp. 1683–1687.
2.
S.
Neethirajan
,
D. S.
Jayas
, and
S.
Sadistap
,
Food Bioprocess Technol.
2
,
115
(
2009
).
3.
D.
Bastviken
,
I.
Sundgren
,
S.
Natchimuthu
,
H.
Reyier
, and
M.
Gålfalk
,
Biogeosciences
12
,
3849
(
2015
).
4.
C. R.
Webster
,
P. R.
Mahaffy
,
G. J.
Flesch
,
P. B.
Niles
,
J. H.
Jones
,
L. A.
Leshin
,
S. K.
Atreya
,
J. C.
Stern
,
L. E.
Christensen
,
T.
Owen
,
H.
Franz
,
R. O.
Pepin
,
A.
Steele
, and
MSL Science Team
,
Science
341
,
260
(
2013
).
5.
T.
Yasuda
,
S.
Yonemura
, and
A.
Tani
,
Sensors
12
,
C1
(
2012
).
6.
F. R.
Vogel
,
L.
Huang
,
D.
Ernst
,
L.
Giroux
,
S.
Racki
, and
D. E. J.
Worthy
,
Atmos. Meas. Tech.
6
,
301
(
2013
).
7.
J. B.
Paul
,
L.
Lapson
, and
J. G.
Anderson
,
Appl. Opt.
40
,
4904
(
2001
).
8.
M.
Berglund
,
G.
Thornell
, and
A.
Persson
,
J. Appl. Phys.
114
,
033302
(
2013
).
9.
A.
Persson
and
M.
Berglund
,
Laser Phys. Lett.
13
,
075703
(
2016
).
10.
A.
Rogalski
,
J.
Antoszewski
, and
L.
Faraone
,
J. Appl. Phys.
105
,
091101
(
2009
).
11.
Y.
Liu
,
N.
Wei
,
Q.
Zeng
,
J.
Han
,
H.
Huang
,
D.
Zhong
,
F.
Wang
,
L.
Ding
,
J.
Xia
,
H.
Xu
,
Z.
Ma
,
S.
Qiu
,
Q.
Li
,
X.
Liang
,
Z.
Zhang
,
S.
Wang
, and
L.-M.
Peng
,
Adv. Opt. Mater.
4
,
238
(
2016
).
12.
Y.
Itikawa
,
J. Phys. Chem. Ref. Data
31
,
749
(
2002
).
13.
A.
Ångström
and
L.
Thalen
,
Nova Acta Regiae Soc. Sci. Ups.
9
, 1–34 (
1875
).
14.
A. C.
Le Floch
and
C.
Amiot
,
Chem. Phys.
97
,
379
(
1985
).
15.
M.
Berglund
,
M.
Grudén
,
G.
Thornell
, and
A.
Persson
,
Plasma Sources Sci. Technol.
22
,
055017
(
2013
).
16.
X.
Liu
,
C.
Kunkel
,
P.
Ramírez de la Piscina
,
N.
Homs
,
F.
Viñes
, and
F.
Illas
,
ACS Catal.
7
,
4323
(
2017
).
17.
M. D.
Porosoff
,
S.
Kattel
,
W.
Li
,
P.
Liu
, and
J. G.
Chen
,
Chem. Commun.
51
,
6988
(
2015
).
18.
K.
Zhang
,
G.
Zhang
,
X.
Liu
,
A. N.
Phan
, and
K.
Luo
,
Ind. Eng. Chem. Res.
56
,
3204
(
2017
).
19.
G.
Chen
,
L.
Wang
,
T.
Godfroid
, and
R.
Snyders
,
Plasma Chemistry and Gas Conversion
(
IntechOpen
,
2018
), pp.
59
69
.