Calibration-free infrared absorption spectroscopy using cantilever-enhanced photoacoustic detection of the optical power.

We report on sensitive tunable laser absorption spectroscopy using a multipass gas cell and a solid-state photoacoustic optical power detector. Unlike photoacoustic spectroscopy (PAS), this method readily allows a low gas pressure for high spectral selectivity and a free gas flow for continuous measurements. Our photoacoustic optical power detector has a large linear dynamic range and can be used at almost any optical wavelength, including the middle infrared and THz regions that are challenging to cover with traditional optical detectors. Furthermore, our approach allows for compensation of laser power drifts with a single detector. As a proof of concept, we have measured very weak CO2 absorption lines at 9.2 µm wavelength and achieved a normalized noise equivalent absorption (NNEA) of 2.35·10-9 Wcm-1Hz-1/2 with a low-power quantum cascade laser. The absolute value of the gas absorption coefficient is obtained directly from the Beer-Lambert law, making the technique calibration-free.

Atmospheric Trace Gas Oxidizers Contribute to Soil Carbon Fixation Driven by Key Soil Conditions in Terrestrial Ecosystems.

Microbial oxidizers of trace gases such as hydrogen (H2) and carbon monoxide (CO) are widely distributed in soil microbial communities and play a vital role in modulating biogeochemical cycles. However, the contribution of trace gas oxidizers to soil carbon fixation and the driving environmental factors remain unclear, especially on large scales. Here, we utilized biogeochemical and genome-resolved metagenomic profiling, assisted by machine learning analysis, to estimate the contributions of trace gas oxidizers to soil carbon fixation and to predict the key environmental factors driving this process in soils from five distinct ecosystems. The results showed that phylogenetically and physiologically diverse H2 and CO oxidizers and chemosynthetic carbon-fixing microbes are present in the soil in different terrestrial ecosystems. The large-scale variations in soil carbon fixation were highly positively correlated with both the abundance and the activity of H2 and CO oxidizers (p < 0.05-0.001). Furthermore, soil pH and moisture-induced shifts in the abundance of H2 and CO oxidizers partially explained the variation in soil carbon fixation (55%). The contributions of trace gas oxidizers to soil carbon fixation in the different terrestrial ecosystems were estimated to range from 1.1% to 35.0%. The estimated rate of trace gas carbon fixation varied from 0.04 to 1.56 mg kg-1 d-1. These findings reveal that atmospheric trace gas oxidizers may contribute to soil carbon fixation driven by key soil environmental factors, highlighting the non-negligible contribution of these microbes to terrestrial carbon cycling.

A Review on Photoacoustic Spectroscopy Techniques for Gas Sensing.

The rapid growth of industry and the global drive for modernization have led to an increase in gas emissions, which present significant environmental and health risks. As a result, there is a growing need for precise and sensitive gas-monitoring technologies. This review delves into the progress made regarding photoacoustic gas sensors, with a specific focus on the vital components of acoustic cells and acoustic detectors. This review highlights photoacoustic spectroscopy (PAS) as an optical detection technique, lauding its high sensitivity, selectivity, and capability to detect a wide range of gaseous species. The principles of photoacoustic gas sensors are outlined, emphasizing the use of modulated light absorption to generate heat and subsequently detect gas pressure as acoustic pressure. Additionally, this review provides an overview of recent advancements in photoacoustic gas sensor components while also discussing the applications, challenges, and limitations of these sensors. It also includes a comparative analysis of photoacoustic gas sensors and other types of gas sensors, along with potential future research directions and opportunities. The main aim of this review is to advance the understanding and development of photoacoustic gas detection technology.

Highly sensitive and miniaturized microcone-curved resonant photoacoustic cavity for trace gas detection.

This paper proposes a novel microcone-curved resonant photoacoustic cell (MCR-PAC) for highly sensitive trace gas detection. The MCR-PAC features with microcone-curved resonant region and cylindrical buffer chamber, which dominates the photoacoustic signal amplification. By introducing the hyperbolic eccentricity as a new optimization dimension, the quality factor of the MCR-PAC is remarkably strengthened to enhance the acoustic pressure amplitude. At an eccentricity value of 5, the volume of the photoacoustic resonant cavity is approximately 0.23 cm3. Targeting trace acetylene, the system achieves a minimum detection limit of 1.41 ppb with an integration time of 290 s, corresponding normalized noise equivalent absorption coefficient is 1.88×10-9 W·cm-1·Hz-1/2. Compared to the traditional T-type PAC, the overall performance of MCR-PAC has been enhanced nearly fourfold. With its compact millimeter-scale dimensions and high sensitivity, the MCR-PAC demonstrates extensive potential for application in environmental monitoring and breath diagnostics.

Sub-ppb level HCN photoacoustic sensor employing dual-tube resonator enhanced clamp-type tuning fork and U-net neural network noise filter.

Hydrogen cyanide (HCN) is a toxic industrial chemical, necessitating low-level detection capabilities for safety and environmental monitoring. This study introduces a novel approach for detecting hydrogen cyanide (HCN) using a clamp-type custom quartz tuning fork (QTF) integrated with a dual-tube acoustic micro-resonator (AmR) for enhanced photoacoustic gas sensing. The design and optimization of the AmR geometry were guided by theoretical simulation and experimental validation, resulting in a robust on-beam QEPAS (Quartz-Enhanced Photoacoustic Spectroscopy) configuration. To boost the QEPAS sensitivity, an Erbium-Doped Fiber Amplifier (EDFA) was incorporated, amplifying the laser power by approximately 286 times. Additionally, a transformer-based U-shaped neural network, a machine learning filter, was employed to refine the photoacoustic signal and reduce background noise effectively. This combination yielded a significantly low detection limit for HCN at 0.89 parts per billion (ppb) with a rapid response time of 1 second, marking a substantial advancement in optical gas sensing technologies. Key modifications to the QTF and innovative use of AmR lengths were validated under various experimental conditions, affirming the system's capabilities for real-time, high-sensitivity environmental monitoring and industrial safety applications. This work not only demonstrates significant enhancements in QEPAS but also highlights the potential for further technological advancements in portable gas detection systems.

Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization.

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.

Volatile Organic Compound Metabolism on Early Earth.

Biogenic volatile organic compounds (VOCs) constitute a significant portion of gas-phase metabolites in modern ecosystems and have unique roles in moderating atmospheric oxidative capacity, solar radiation balance, and aerosol formation. It has been theorized that VOCs may account for observed geological and evolutionary phenomena during the Archaean, but the direct contribution of biology to early non-methane VOC cycling remains unexplored. Here, we provide an assessment of all potential VOCs metabolized by the last universal common ancestor (LUCA). We identify enzyme functions linked to LUCA orthologous protein groups across eight literature sources and estimate the volatility of all associated substrates to identify ancient volatile metabolites. We hone in on volatile metabolites with confirmed modern emissions that exist in conserved metabolic pathways and produce a curated list of the most likely LUCA VOCs. We introduce volatile organic metabolites associated with early life and discuss their potential influence on early carbon cycling and atmospheric chemistry.

Effects of chemically-reductive trace gas contaminants on non-thermal plasma inactivation of an airborne virus.

Transmission of airborne infectious diseases poses great risk for public health and socio-economic stability, thus, there is a need for an effective control method targeting the spread and transmission of pathogenic aerosols. The existence of chemically-reductive trace air contaminants in animal agriculture may affect the oxidation inactivation process of pathogens. In this study, we report how the presence of such gasses impacts the effectiveness of using non-thermal plasma (NTP) within a packed-bed dielectric barrier discharge reactor to inactivate MS2 bacteriophage. Inactivation of the aerosolized bacteriophage is determined by the combination of viability and polymerase chain reaction assays. Using a plasma power source with a voltage of 20 kV and frequency of 350 Hz, after differentiating and excluding the physical removal effects of viral aerosols potentially caused by plasma, the baseline inactivation of MS2 aerosol in air has been determined based on an overall air flow rate of 200 Liters per minute and plasma discharge power of 1.8 W. When either ammonia or hydrogen sulfide gas is introduced into the airstream at a concentration of 1 part per million, the NTP virus inactivation efficiency is reduced to around 0.5-log from the 1-log baseline inactivation in air alone. Higher concentrations of those gasses will not further inhibit the effectiveness of plasma inactivation.

Prevalence of trace gas-oxidizing soil bacteria increases with radial distance from Polloquere hot spring within a high-elevation Andean cold desert.

High-elevation arid regions harbor microbial communities reliant on metabolic niches and flexibility to survive under biologically stressful conditions, including nutrient limitation that necessitates the utilization of atmospheric trace gases as electron donors. Geothermal springs present "oases" of microbial activity, diversity, and abundance by delivering water and substrates, including reduced gases. However, it is unknown whether these springs exhibit a gradient of effects, increasing their impact on trace gas-oxidizers in the surrounding soils. We assessed whether proximity to Polloquere, a high-altitude geothermal spring in an Andean salt flat, alters the diversity and metabolic structure of nearby soil bacterial populations compared to the surrounding cold desert. Recovered DNA and metagenomic analyses indicate that the spring represents an oasis for microbes in this challenging environment, supporting greater biomass with more diverse metabolic functions in proximal soils that declines sharply with radial distance from the spring. Despite the sharp decrease in biomass, potential rates of atmospheric hydrogen (H2) and carbon monoxide (CO) uptake increase away from the spring. Kinetic estimates suggest this activity is due to high-affinity trace gas consumption, likely as a survival strategy for energy/carbon acquisition. These results demonstrate that Polloquere regulates a gradient of diverse microbial communities and metabolisms, culminating in increased activity of trace gas-oxidizers as the influence of the spring yields to that of the regional salt flat environment. This suggests the spring holds local importance within the context of the broader salt flat and potentially represents a model ecosystem for other geothermal systems in high-altitude desert environments.

Sub-PPB Detection with Gas-Phase Multiphoton Electron Extraction Spectroscopy under Ambient Conditions.

Multiphoton electron extraction spectroscopy (MEES) is an advanced analytical technique that has demonstrated exceptional sensitivity and specificity for detecting molecular traces on solid and liquid surfaces. Building upon the solid-state MEES foundations, this study introduces the first application of MEES in the gas phase (gas-phase MEES), specifically designed for quantitative detection of gas traces at sub-part per billion (sub-PPB) concentrations under ambient atmospheric conditions. Our experimental setup utilizes resonant multiphoton ionization processes using ns laser pulses under a high electrical field. The generated photoelectron charges are recorded as a function of the laser's wavelength. This research showcases the high sensitivity of gas-phase MEES, achieving high spectral resolution with resonant peak widths less than 0.02 nm FWHM. We present results from quantitative analysis of benzene and aniline, two industrially and environmentally significant compounds, demonstrating linear responses in the sub-PPM and sub-PPB ranges. The enhanced sensitivity and resolution of gas-phase MEES offer a powerful approach to trace gas analysis, with potential applications in environmental monitoring, industrial safety, security screening, and medical diagnostics. This study confirms the advantages of gas-phase MEES over many traditional optical spectroscopic methods and demonstrates its potential in direct gas-trace sensing in ambient atmosphere.

Deep Learning for Gas Sensing via Infrared Spectroscopy.

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.

High sensitivity and ultra-low concentration range photoacoustic spectroscopy based on trapezoid compound ellipsoid resonant photoacoustic cell and partial least square.

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.

A mini-resonant photoacoustic sensor based on a sphere-cylinder coupled acoustic resonator for high-sensitivity trace gas sensing.

This paper reports a mini-resonant photoacoustic sensor for high-sensitivity trace gas sensing. The sensor primarily contains a sphere-cylinder coupled acoustic resonator, a cylindrical buffer chamber, and a fiber-optic acoustic sensor. The acoustic field distributions of this mini-resonant photoacoustic sensor and the conventional T-type resonant photoacoustic sensor have been carefully evaluated, showing that the first-order resonance frequency of the present mini-resonant photoacoustic sensor is reduced by nearly a half compared to that of the T-type resonant photoacoustic sensor. The volume of the developed photoacoustic cavity is only about 0.8 cm3. Trace methane is selected as the target analytical gas and a detection limit of 101 parts-per-billion at 100-s integration time has been achieved, corresponding to a normalized noise equivalent absorption (NNEA) coefficient of 1.04 × 10-8 W·cm-1·Hz-1/2. The developed mini-resonant photoacoustic sensor provides potential for high-sensitivity trace gas sensing in narrow spaces.

Carbon amendments in soil microcosms induce uneven response on H2 oxidation activity and microbial community composition.

High-affinity H2-oxidizing bacteria (HA-HOB) thriving in soil are responsible for the most important sink of atmospheric H2. Their activity increases with soil organic carbon content, but the incidence of different carbohydrate fractions on the process has received little attention. Here we tested the hypothesis that carbon amendments impact HA-HOB activity and diversity differentially depending on their recalcitrance and their concentration. Carbon sources (sucrose, starch, cellulose) and application doses (0, 0.1, 1, 3, 5% Ceq soildw-1) were manipulated in soil microcosms. Only 0.1% Ceq soildw-1 cellulose treatment stimulated the HA-HOB activity. Sucrose amendments induced the most significant changes, with an abatement of 50% activity at 1% Ceq soildw-1. This was accompanied with a loss of bacterial and fungal alpha diversity and a reduction of high-affinity group 1 h/5 [NiFe]-hydrogenase gene (hhyL) abundance. A quantitative classification framework was elaborated to assign carbon preference traits to 16S rRNA gene, ITS and hhyL genotypes. The response was uneven at the taxonomic level, making carbon preference a difficult trait to predict. Overall, the results suggest that HA-HOB activity is more susceptible to be stimulated by low doses of recalcitrant carbon, while labile carbon-rich environment is an unfavorable niche for HA-HOB, inducing catabolic repression of hydrogenase.

Integrated near-infrared fiber-optic photoacoustic sensing demodulator for ultra-high sensitivity gas detection.

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.

An Artificial Neural Network to Eliminate the Detrimental Spectral Shift on Mid-Infrared Gas Spectroscopy.

We demonstrate the successful implementation of an artificial neural network (ANN) to eliminate detrimental spectral shifts imposed in the measurement of laser absorption spectrometers (LASs). Since LASs rely on the analysis of the spectral characteristics of biological and chemical molecules, their accuracy and precision is especially prone to the presence of unwanted spectral shift in the measured molecular absorption spectrum over the reference spectrum. In this paper, an ANN was applied to a scanning grating-based mid-infrared trace gas sensing system, which suffers from temperature-induced spectral shifts. Using the HITRAN database, we generated synthetic gas absorbance spectra with random spectral shifts for training and validation. The ANN was trained with these synthetic spectra to identify the occurrence of spectral shifts. Our experimental verification unambiguously proves that such an ANN can be an excellent tool to accurately retrieve the gas concentration from imprecise or distorted spectra of gas absorption. Due to the global shift of the measured gas absorption spectrum, the accuracy of the retrieved gas concentration using a typical least-mean-squares fitting algorithm was considerably degraded by 40.3%. However, when the gas concentration of the same measurement dataset was predicted by the proposed multilayer perceptron network, the sensing accuracy significantly improved by reducing the error to less than ±1% while preserving the sensing sensitivity.

Wafer-Scale Field-Effect Transistor-Type Sensor Using a Carbon Nanotube Film as a Channel for Ppb-Level Hydrogen Sulfide Detection.

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.

Assessment of vibrational-translational relaxation dynamics of in a wet-nitrogen matrix through QEPAS.

Here we report on a study of the non-radiative relaxation dynamic of 12CH4 and 13CH4 in wet nitrogen-based matrixes by using the quartz-enhanced photoacoustic spectroscopy (QEPAS) technique. The dependence of the QEPAS signal on pressure at fixed matrix composition and on H2O concentration at fixed pressure was investigated. We demonstrated that QEPAS measurements can be used to retrieve both the effective relaxation rate in the matrix, and the V-T relaxation rate associated to collisions with nitrogen and water vapor. No significant differences in measured relaxation rates were observed between the two isotopologues.

Methodology and applications of acousto-electric analogy in photoacoustic cell design for trace gas analysis.

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.

A Miniaturized 3D-Printed Quartz-Enhanced Photoacoustic Spectroscopy Sensor for Methane Detection with a High-Power Diode Laser.

In this invited paper, a highly sensitive methane (CH4) trace gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) technique using a high-power diode laser and a miniaturized 3D-printed acoustic detection unit (ADU) is demonstrated for the first time. A high-power diode laser emitting at 6057.10 cm-1 (1650.96 nm), with the optical power up to 38 mW, was selected as the excitation source to provide a strong excitation. A 3D-printed ADU, including the optical and photoacoustic detection elements, had a dimension of 42 mm, 27 mm, and 8 mm in length, width, and height, respectively. The total weight of this 3D-printed ADU, including all elements, was 6 g. A quartz tuning fork (QTF) with a resonant frequency and Q factor of 32.749 kHz and 10,598, respectively, was used as an acoustic transducer. The performance of the high-power diode laser-based CH4-QEPAS sensor, with 3D-printed ADU, was investigated in detail. The optimum laser wavelength modulation depth was found to be 0.302 cm-1. The concentration response of this CH4-QEPAS sensor was researched when the CH4 gas sample, with different concentration samples, was adopted. The obtained results showed that this CH4-QEPAS sensor had an outstanding linear concentration response. The minimum detection limit (MDL) was found to be 14.93 ppm. The normalized noise equivalent absorption (NNEA) coefficient was obtained as 2.20 × 10-7 cm-1W/Hz-1/2. A highly sensitive CH4-QEPAS sensor, with a small volume and light weight of ADU, is advantageous for the real applications. It can be portable and carried on some platforms, such as an unmanned aerial vehicle (UAV) and a balloon.