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.

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.

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.

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.

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.

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.

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.