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Accurately detecting atmospheric carbon dioxide is a vital part of responding to the global greenhouse effect. Conventional off-axis integral cavity detection systems are computationally intensive and susceptible to environmental factors. This study deploys an Extreme Learning Machine model incorporating a cascaded integrator comb (CIC) filter into the off-axis integrating cavity. It is shown that appropriate parameters can effectively improve the performance of the instrument in terms of lower detection limit, accuracy, and root mean square deviation. The proposed method is incorporated successfully into a monitoring station situated near an industrial area for detecting atmospheric carbon dioxide (CO2) concentration daily.

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Deep learning methods, a powerful form of artificial intelligence, have been applied in a number of spectroscopy and gas sensing applications. However, the speciation of multi-component gas mixtures from infrared (IR) absorption spectra using deep learning remains to be explored. Here, we propose a one-dimensional deep convolutional neural network gas classification model for the identification of small molecules of interest based on IR absorption spectra in flexible user-defined frequency ranges. The molecules considered include ten that are of interest in the atmosphere or in industrial and environmental processes: water vapor, carbon dioxide, ozone, nitrous oxide, carbon monoxide, methane, nitric oxide, sulfur dioxide, nitrogen dioxide, and ammonia. A simulated dataset of IR absorption spectra for mixtures of these molecules diluted in air was generated and used to train a deep learning model. The model was tested against simulated spectra containing noise and was found to provide speciation predictions with accuracy from 82 to 97%. The internal operation of the model was investigated using class activation maps that illustrate how the model prioritizes spectral information for classification. Finally, the model was demonstrated for the prediction of speciation for two synthetic experimental mixture spectra. The proposed model and the dataset generation strategies are generalized and can be implemented for other gases, different frequency ranges, and spectroscopy types. The multi-component speciation method developed herein is the first application of a convolutional neural network model, trained on HITRAN-based simulations, for spectral identification.

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A high sensitivity and ultra-low concentration range photoacoustic spectroscopy (PAS) gas detection system, which was based on a novel trapezoid compound ellipsoid resonant photoacoustic cell (TCER-PAC) and partial least square (PLS), was proposed to detect acetylene (C2H2) gas. In the concentration range of 0.5 ppm ∼ 10.0 ppm, the limit of detection (LOD) values of TCER-PAC-based PAS system without data processing was 66.4 ppb, which was lower than that of the traditional trapezoid compound cylindrical resonant photoacoustic cell (TCCR-PAC). The experimental results indicated that the TCER-PAC had higher sensitivity than of TCCR-PAC. Within the concentration range of 12.5 ppb ∼ 125.0 ppb, the LOD and limit of quantification (LOQ) of TCER-PAC-based PAS system combined with PLS regression algorithm were 1.1 ppb and 3.7 ppb, respectively. The results showed that higher detection sensitivity and lower LOD were obtained by PAS system with TCER-PAC and PLS than that of TCCR-PAC-based PAS system.

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An integrated near-infrared fiber-optic photoacoustic sensing demodulator was established for ultra-high sensitivity gas detection. The demodulator has capacities of interference spectrum acquisition and calculation, laser modulation control as well as digital lock-in amplification. FPGA was utilized to realize all the control and signal processing functions, which immensely improved the integration and stability of the system. The photoacoustic signal detection based on fiber-optic Fabry-Perot (F-P) acoustic sensor was realized by applying ultra-high resolution spectral demodulation technique. The detectable frequency of photoacoustic signal achieved 10 kHz. The system integrated lock-in amplification technology, which made the noise sound pressure and dynamic response range of sound pressure detection reached 3.7 µPa/√Hz @1 kHz and 142 dB, respectively. The trace C2H2 gas was tested with a multi-pass resonant photoacoustic cell. Ultra-high sensitivity gas detection was accomplished, which was based on high acoustic detection sensitivity and the matching digital lock-in amplification. The system detection limit and normalized noise equivalent absorption (NNEA) coefficient were reached 3.5 ppb and 6.7 × 10-10 cm-1WHz-1/2, respectively. The devised demodulator can be applied for long-distance gas measurement, which depends on the fact that both the near-infrared photoacoustic excitation light and the probe light employ optical fiber as transmission medium.

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Sulfur hexafluoride is widely used in power equipment because of its excellent insulation and arc extinguishing properties. However, severe damage to power equipment may be caused and a large-scale collapse of the power grid may occur when SF6 is decomposed into H2S, SOF2, and SO2F2. It is difficult to detect the SF6 concentration as it is a kind of inert gas. Generally, the trace gas decomposed in the early stage of SF6 is detected to achieve the function of early warning. Consequently, it is of great significance to realize the real-time detection of trace gases decomposed from SF6 for the early fault diagnosis of power equipment. In this work, a wafer-scale gate-sensing carbon-based FET gas sensor is fabricated on a four-inch carbon wafer for the detection of H2S, a decomposition product of SF6. The carbon nanotubes with semiconductor properties and the noble metal Pt are respectively used as a channel and a sensing gate of the FET-type gas sensor, and the channel transmission layer and the sensing gate layer each play an independent role and do not interfere with each other by introducing the gate dielectric layer Y2O3, giving full play to their respective advantages to forming an integrated sensor of gas detection and signal amplification. The detection limit of the as-prepared gate-sensing carbon-based FET gas sensor can reach 20 ppb, and its response deviation is not more than 3% for the different batches of gas sensors. This work provides a potentially useful solution for the industrial production of miniaturized and integrated gas sensors.

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Photoacoustic cells play an important role in photoacoustic trace gas analysis, as they can amplify the photoacoustic signal and improve detection limit. Therefore, the structure and dimensional design of a photoacoustic cell are very important for the performance of a photoacoustic sensing system. In this review, the theory and the method of acousto-electric analogy for the photoacoustic cell design are discussed in detail. Starting from the basics of the acousto-electric analogy, the counterparts of acoustic elements in electric circuits are first deduced from the analogies between acoustic and electric networks. Subsequently, an acoustic transmission line model is reviewed, and the model is demonstrated to optimize the geometry of the photoacoustic cell and investigate the properties of the cell. Finally, using the acousto-electric analogy method, the equivalent electric circuits of several types of photoacoustic cells, such as the Helmholtz resonant photoacoustic cell, the H-type resonant photoacoustic cell, the differential photoacoustic cell, etc., are presented.

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Exhaled nitric oxide trace gas at the ppb level is a biomarker of human airway inflammation. To detect this, we developed a method for the collection of active pumping electronic nose bionic chamber gas. An optimization algorithm based on multivariate regression (MR) and genetic algorithm-back propagation (GA-BP) was proposed to improve the accuracy of trace-level gas detection. An electronic nose was used to detect NO gas at the ppb level by substituting breathing gas with a sample gas. The impact of the pump suction flow capacity variation on the response of the electronic nose system was determined using an ANOVA. Further, the optimization algorithm based on MR and GA-BP was studied for flow correction. The results of this study demonstrate an increase in the detection accuracy of the system by more than twofold, from 17.40%FS before correction to 6.86%FS after correction. The findings of this research lay the technical groundwork for the practical application of electronic nose systems in the daily monitoring of FeNO.

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We report on the development of a highly sensitive hydrogen sulfide (H2S) gas sensor exploiting the doubly resonant photoacoustic spectroscopy technique and using a near-infrared laser emitting at 1578.128 nm. By targeting the R(4) transition of H2S, we achieved a minimum detection limit of 10 part per billion in concentration and a normalized noise equivalent absorption coefficient of 8.9 × 10-12 W cm-1 Hz-1/2. A laser-cavity-molecule locking strategy is proposed to enhance the sensor stability for fast measurement when dealing with external disturbances. A comparison among the state-of-the-art H2S sensors using various spectroscopic techniques confirmed the record sensitivity achieved in this work.

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Precise and accurate measurements of ambient HNO3 are crucial for understanding various atmospheric processes, but its ultra-low trace amounts and the high polarity of HNO3 have strongly hindered routine, widespread, direct measurements of HNO3 and restricted field studies to mostly short-term, localized measurement campaigns. Here, we present a custom field-deployable direct absorption laser spectrometer and demonstrate its analytical capabilities for in situ atmospheric HNO3 measurements. Detailed laboratory characterizations with a particular focus on the instrument response under representative conditions for tropospheric measurements, i.e., the humidity, spectral interference, changing HNO3 amount fractions, and air-sampling-related artifacts, revealed the key aspects of our method: (i) a good linear response (R2 > 0.98) between 0 and 25 nmol·mol−1 in both dry and humid conditions with a limit of detection of 95 pmol·mol−1; (ii) a discrepancy of 20% between the spectroscopically derived amount fractions and indirect measurements using liquid trapping and ion chromatography; (iii) a systematic spectral bias due to water vapor. The spectrometer was deployed in a three-week field measurement campaign to continuously monitor the HNO3 amount fraction in ambient air. The measured values varied between 0.1 ppb and 0.8 ppb and correlated well with the daily total nitrates measured using a filter trapping method.

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Photoacoustic spectroscopy (PAS) combines the advantages of high sensitivity, high specificity and zero background, which is very suitable for trace gas detection. Cantilever-enhanced photoacoustic spectroscopy (CEPAS) utilizes highly sensitive mechanical cantilevers to further enhance the photoacoustic signal and shows a gas concentration detection limit of parts per trillion. This review is intended to summarize the recent advancements in CEPAS based on different displacement detection methods, such as Michelson interference, Fabry-Perot interference, light intensity detection, capacitive, piezoelectric and piezoresistive detection. Fundamental mechanisms and technical requirements of CEPAS are also provided in the literature. Finally, potential challenges and further opportunities are also discussed.

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A high-sensitivity all-optical photoacoustic spectroscopy based on fast swept laser interferometry is proposed to trace gas detection. The momentary cavity length of the fiber-optic Fabry-Perot microphone is demodulated by a fast swept-laser interferometry with an instantaneous frequency demodulation algorithm. The all-optical photoacoustic spectroscopy based on the designed microphone was tested for trace acetylene gas detection in the near-infrared region. The normalized noise equivalent absorption coefficient for acetylene gas is achieved to be 1.06 × 10-9 cm-1 W Hz-1∕2.

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Real-time monitoring of health threatening gases for chemical safety and human health protection requires detection and discrimination of trace gases with proper gas sensors. In many applications, costly, bulky, and power-hungry devices, normally employing optical gas sensors and electrochemical gas sensors, are used for this purpose. Using a single miniature low-power semiconductor gas sensor to achieve this goal is hardly possible, mostly due to its selectivity issue. Herein, we report a dual-mode microheater integrated nanotube array gas sensor (MINA sensor). The MINA sensor can detect hydrogen, acetone, toluene, and formaldehyde with the lowest measured limits of detection (LODs) as 40 parts-per-trillion (ppt) and the theoretical LODs of ∼7 ppt, under the continuous heating (CH) mode, owing to the nanotubular architecture with large sensing area and excellent surface catalytic activity. Intriguingly, unlike the conventional electronic noses that use arrays of gas sensors for gas discrimination, we discovered that when driven by the pulse heating (PH) mode, a single MINA sensor possesses discrimination capability of multiple gases through a transient feature extraction method. These above features of our MINA sensors make them highly attractive for distributed low-power sensor networks and battery-powered mobile sensing systems for chemical/environmental safety and healthcare applications.

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Sulfur dioxide (SO2) is an important precursor for the formation of atmospheric sulfate aerosol and acid rain. We present an instrument using Broadband Cavity-Enhanced Absorption Spectroscopy (BBCEAS) for the measurement of SO2 with a minimum limit of detection of 0.75 ppbv (3-σ) using the spectral range 305.5-312 nm and an averaging time of 5 min. The instrument consists of high-reflectivity mirrors (0.9985 at 310 nm) and a deep UV light source (Light Emitting Diode). The effective absorption path length of the instrument is 610 m with a 0.966 m base length. Published reference absorption cross sections were used to fit and retrieve the SO2 concentrations and were compared to fluorescence standard measurements for SO2. The comparison was well correlated, R2 = 0.9998 with a correlation slope of 1.04. Interferences for fluorescence measurements were tested and the BBCEAS showed no interference, while ambient measurements responded similarly to standard measurement techniques.

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For this invited manuscript, an in-plane single-quartz-enhanced dual spectroscopy (IP-SQEDS)-based trace gas sensor was demonstrated for the first time. A single quartz tuning fork (QTF) was employed to combine in-plane quartz-enhanced photoacoustic spectroscopy (IP-QEPAS) with light-induced thermoelastic spectroscopy (LITES) techniques. Water vapor (H2O) was chosen as the target gas. Compared to traditional QEPAS, IP-SQEDS not only allowed for simple structures, but also obtained nearly three times signal amplitude enhancement.

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The detection of harmful trace gases, such as formaldehyde (HCHO), is a technical challenge in the current gas sensor field. The weak electrical signal caused by trace amounts of gases is difficult to be detected and susceptible to other gases. Based on the amplification effect of a field-effect transistor (FET), a carbon-based FET-type gas sensor with a gas-sensing gate is proposed for HCHO detection at the ppb level. Semiconducting carbon nanotubes (s-CNTs) and a catalytic metal are chosen as channel and gate materials, respectively, for the FET-type gas sensor, which makes full use of the respective advantages of the channel transport layer and the sensitive gate layer. The as-prepared carbon-based FET-type gas sensor exhibits a low detection limit toward HCHO up to 20 ppb under room temperature (RT), which can be improved to 10 ppb by a further heating strategy. It also exhibits a remarkable elevated recovery rate from 80 to 97% with almost no baseline drift (2%) compared to the RT condition, revealing excellent reproducibility, stability, and recovery. The role of sensitive function in the FET-type gas sensor is performed by means of an independent gas-sensing gate, that is, the independence of the sensitive gate and the electron transmission channel is the main reason for its high sensitivity detection. We hope our work can provide an instructive approach for designing high-performance formaldehyde sensor chips with on-chip integration potential.

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This invited paper demonstrated an ultra-highly sensitive ammonia (NH3) sensor based on the light-induced thermoelastic spectroscopy (LITES) technique for the first time. A quartz tuning fork (QTF) with a resonance frequency of 32.768 kHz was employed as a detector. A fiber-coupled, continuous wave (CW), distributed feedback (DFB) diode laser emitting at 1530.33 nm was chosen as the excitation source. Wavelength modulation spectroscopy (WMS) and second-harmonic (2f) detection techniques were applied to reduce the background noise. In a one scan period, a 2f signal of the two absorption lines located at 6534.6 cm-1 and 6533.4 cm-1 were acquired simultaneously. The 2f signal amplitude at the two absorption lines was proved to be proportional to the concentration, respectively, by changing the concentration of NH3 in the analyte. The calculated R-square values of the linear fit are equal to ~0.99. The wavelength modulation depth was optimized to be 13.38 mA, and a minimum detection limit (MDL) of ~5.85 ppm was achieved for the reported NH3 sensor.

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Combining the merits of non-contact measurement and high sensitivity, the quartz-enhanced photothermal spectroscopy (QEPTS) technique is suitable for measuring acid gases such as hydrogen chloride (HCl). In this invited paper, we report, for the first time, on an ultra-highly sensitive HCl sensor based on the QEPTS technique. A continuous wave, distributed feedback (CW-DFB) fiber-coupled diode laser with emission wavelength of 1.74 µm was used as the excitation source. A certified mixture of 500 ppm HCl:N2 was adapted as the analyte. Wavelength modulation spectroscopy was used to simplify the data processing. The wavelength modulation depth was optimized. The relationships between the second harmonic (2f) amplitude of HCl-QEPTS signal and the laser power as well as HCl concentration were investigated. An Allan variance analysis was performed to prove that this sensor had good stability and high sensitivity. The proposed HCl-QEPTS sensor can achieve a minimum detection limit (MDL) of ~17 parts per billion (ppb) with an integration time of 130 s. Further improvement of such an HCl-QEPTS sensor performance was proposed.

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The qualitative and quantitative analysis to trace gas in exhaled human breath has become a promising technique in biomedical applications such as disease diagnosis and health status monitoring. This paper describes an application of a high spectral resolution optical feedback cavity enhanced absorption spectroscopy (OF-CEAS) for ammonia detection in exhaled human breath, and the main interference of gases such as CO2 and H2O are approximately eliminated at the same time. With appropriate optical feedback, a fibered distributed feedback (DFB) diode laser emitting at 1531.6 nm is locked to the resonance of a V-shaped cavity with a free spectral range (FSR) of 300 MHz and a finesse of 14,610. A minimum detectable absorption coefficient of αmin = 2.3 × 10-9 cm-1 is achieved in a single scan within 5 s, yielding a detection limit of 17 ppb for NH3 in breath gas at low pressure, and this stable system allows the detection limit down to 4.5 ppb when the spectra to be averaged over 16 laser scans. Different from typical CEAS with a static cavity, which is limited by the FSR in frequency space, the attainable spectral resolution of our experimental setup can be up to 0.002 cm-1 owing to the simultaneous laser frequency tuning and cavity dither. Hence, the absorption line profile is more accurate, which is most suitable for low-pressure trace gas detection. This work has great potential for accurate selectivity and high sensitivity applications in human breath analysis and atmosphere sciences.

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Lock-in amplification (LIA) is an effective approach for recovery of weak signal buried in noise. Determination of the input signal amplitude in a classical dual-phase LIA is based on incoherent detection which leads to a biased estimation at low signal-to-noise ratio. This article presents, for the first time to our knowledge, a new architecture of LIA involving phase estimation with a linear-circular regression for coherent detection. The proposed phase delay estimate, between the input signal and a reference, is defined as the maximum-likelihood of a set of observations distributed according to a von Mises distribution. In our implementation this maximum is obtained with a Newton Raphson algorithm. We show that the proposed LIA architecture provides an unbiased estimate of the input signal amplitude. Theoretical simulations with synthetic data demonstrate that the classical LIA estimates are biased for SNR of the input signal lower than -20 dB, while the proposed LIA is able to accurately recover the weak signal amplitude. The novel approach is applied to an optical sensor for accurate measurement of NO 2 concentrations at the sub-ppbv level in the atmosphere. Side-by-side intercomparison measurements with a commercial LIA (SR830, Stanford Research Inc., Sunnyvale, CA, USA ) demonstrate that the proposed LIA has an identical performance in terms of measurement accuracy and precision but with simplified hardware architecture.

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Trace gas monitoring plays an important role in many areas of life sciences ranging from agrotechnology, microbiology, molecular biology, physiology, and phytopathology. In plants, many processes can be followed by their low-concentration gas emission, for compounds such as ethylene, nitric oxide, ethanol or other volatile organic compounds (VOCs). For this, numerous gas-sensing devices are currently available based on various methods. Among them are the online trace gas detection methods; these have attracted much interest in recent years. Laser-based infrared spectroscopy and proton transfer reaction mass spectrometry are the two most widely used methods, thanks to their high sensitivity at the single part per billion level and their response time of seconds. This paper starts with a short description of each method and presents performances within a wide variety of biological applications. Using these methods, the dynamics of trace gases for ethylene, nitric oxide and other VOCs released by plants under different conditions are recorded and analysed under natural conditions. In this way many hypotheses can be tested, revealing the role of the key elements in signalling and action mechanisms in plants.