Gas Concentration Research Articles

Clays to lightweight aggregates: Thermochemical modeling and industrial validation

The creation of thermochemical models to forecast the dependent variables governing the bloatability of lightweight aggregates (LWAs) is the goal of this work. Laser particle size analysis, XRD, XRF, IR, TGA-DTA, SEM-EDX, Laser imaging system, µ-CT, hot-stage microscope and Factsage (8.3) thermodynamic software were used for clays and LWAs characterization in the temperature range 900–1250°C. It was found that the LWAs bloating zone occurs at 1050–1250°C with a minimum bulk density (BD) of (0.74 g/cm3) at 1250°C. During the LWAs pyroplastic state, the crystallization of anorthite from the silicate melt lowers its viscosity due mainly to the alkalis’ uptake into the anorthite lattice. However, the silicate melt is still viscous enough to trigger the reduction of hematite and consequently guarantees the evolution of bloating gases. The simultaneous association of higher evolved gas with less melt content of higher viscosity would promote the LWAs bloatability. The higher the gas that accompanied with low melt of lower viscosity would loosen the LWAs their consistency and sphericity upon their sudden cooling. Based on the LWAs bulk density and lab experimental observations, the production window limits of successful LWAs are ≥40 wt% silicate melt of 103-107 Pa.s viscosity that accompanied with (0.50–4.00 wt%) gas content. This production window has been verified by Liapor industrial kiln feed. The LWAs pyroplastic and post-quenching characteristics are all statistically significant variables (<0.05) for prediction modeling. The forecasting models are statistically verified by the multi-collinearity testing that shows VIF values <10 indicating the absence of any multi-collinearity among the models’ explanatory variables.

Subsidence associated with dewatering and gas extraction from coal seams: Contribution of desorption-induced coal shrinkage

Several coal seams are contained within the formations of Great Artesian Basin (GAB), the largest natural underground water reservoir in Australia and the world. The extraction of coal seam gas (CSG) and its associated water from thousands of wells within the coal measures of the GAB has led to tens of millimetres of subsidence in CSG development areas. Since highly developed farming systems located in these areas rely on very low slope landforms, even this scale of subsidence has caused significant community concern about the potential for CSG extraction to impair farming operations and productivity through changes in land slope and drainage. Coal seam compaction associated with dewatering and gas extraction has two key components: poromechanical compaction and desorption-induced bulk shrinkage. The former results from pore pressure depletion (due to dewatering and gas extraction) and an increase of vertical effective stress, while the latter is induced by gas desorption from the coal matrix, leading to further deformation. While poromechanical compaction of fluid-bearing formations has been extensively addressed in the literature, relatively little research has been conducted on the role of coal shrinkage in CSG-induced subsidence. This paper introduces an innovative practical modelling approach for assessing CSG-induced subsidence at a subregional/regional scale. The approach utilises an analytical model for CSG-induced subsidence derived from constitutive stress–strain relations for poroelastic-sorptive media. This is a novel approach in the context of CSG-induced subsidence which considers both poromechanical compaction and desorption-induced shrinkage. A further distinction to previous work is the integration of the geomechanical-sorptive subsidence model with a numerical groundwater model. Based on this approach, this paper examines the effect of coal shrinkage on subsidence, and its proportions with respect to total compaction for one of the major coal measures in the Surat Basin. Input data are derived from three-dimensional geological, geomechanical, and groundwater models, as well as methane adsorption and desorption reports. Results show that (a) the impact of coal shrinkage on CSG-induced subsidence is likely to be significant in the study area and (b) the contribution of coal shrinkage to CSG-induced subsidence depends on gas content, Langmuir isotherm, shrinkage strain parameters, and the saturation state of coal. This study provides important insights into CSG-induced subsidence and lays the foundation for the development of robust, efficient, and localised predictive models to support environmental impact assessment and management.

Study of spatiotemporal evolution of coupled airflow–gas–dust multi-field diffusion at low-gas tunnel

The increasing level of mechanization in coal mining means more dust and gas are generated during excavation operations in tunnels. The high concentrations of dust and gas severely affect production efficiency and the physical and mental health of workers. Here, Ansys Fluent simulations were performed to derive the spatiotemporal evolution of coupled airflow–dust–gas diffusion in a low-gas excavation face. The aim was to optimize pollution control by determining the optimal duct distance, L, from the working face in the excavation tunnel. Our results showed that the airflow field affects the coupled diffusion and transport of dust and gas. According to a comparison of the effects of different duct distances from the working face, when L = 6 m, the average dust concentration in the tunnel is low (257.6 mg/m3), and the average gas concentration in the tunnel is 0.28 %, which does not exceed the safety limit. Accordingly, the optimal distance of the duct for pollution control is 6 m. The results of field measurements supported the validity of the simulation. Our findings can be used to improve the air quality in tunnels, thereby keeping miners safe and the working area clean.

Capsules for the Diagnosis and Treatment of Gastrointestinal Motility Disorders- A Game Changer.

Over the last few decades, there have been remarkable strides in endoscopy and radiological imaging that have advanced gastroenterology. However, the management of neurogastroenterological disorders has lagged behind, in part handicapped by the use of catheter-based manometry that is both non-physiological and uncomfortable. The advent of capsule technology has been a game changer for both diagnostic and therapeutic applications. Here, we discuss several capsule devices that are available or under investigation. There are three technologies that are FDA approved. Wireless motility capsule measures pH and pressure and provides clinically impactful information regarding gastric, small intestine and colonic transit, without radiation that has been demonstrated to guide management of gastroparesis, dyspepsia and constipation. Wireless ambulatory pH monitoring capsule is currently the gold standard for assessing gastroesophageal acid reflux. In the therapeutics arena, an orally ingested vibrating capsule has been recently FDA approved for the treatment of chronic constipation, supported by a robust phase 3 clinical trial which showed significant improvement in constipation symptoms and quality of life. There are several capsules currently under investigation. Smart capsule bacterial detection system and Capscan® are capsules that can sample fluid in the small or large bowel and provide microbiome analysis for detection of small intestinal bacterial (SIBO) or fungal overgrowth (SIFO). Another investigational gas sensing capsule analyzing hydrogen, CO2, volatile fatty acids and capsule orientation, can measure regional gut transit time and luminal gas concentrations and assess gastroparesis, constipation or SIBO. Therapeutically, other vibrating capsules are in development. Innovations in capsule technology are poised to transform our ability to investigate gut function physiologically, and non-invasively deliver targeted treatment(s), thereby providing both accurate diagnostic information and luminally-directed, safe therapy.

Advancing hydrogen storage predictions in metal-organic frameworks: A comparative study of LightGBM and random forest models with data enhancement

The escalating consumption of fossil fuels has given rise to a substantial upsurge in greenhouse gas concentrations and global temperatures, which, in turn, has triggered severe climate-related consequences. The critical imperative to reduce CO2 emissions and combat global warming has spurred extensive investigations into clean energy alternatives, with hydrogen emerging as a compelling zero-emission energy source. As a pivotal component of clean energy strategies, hydrogen requires designing compact, lightweight, and efficient storage systems. This study focuses on the development and evaluation of machine learning models for predicting the efficiency of Metal-Organic Frameworks (MOFs) in hydrogen storage, a key aspect of advancing clean energy technologies. MOFs, a class of nanoporous materials, show remarkable potential for hydrogen storage due to their high surface area and porosity. However, selecting the most suitable MOF for this application from a vast array of possible structures is a daunting task. In this context, machine learning algorithms offer an efficient alternative for predicting MOF suitability by considering their structural and chemical properties. We used ensemble learning methods, specifically Light Gradient Boosting Machine (LightGBM) and Random Forest (RF), to predict hydrogen uptake of MOFs based on a dataset of 219 experimentally tested samples. Two modeling scenarios were considered: one using the entire dataset, and the other involving strategic data pre-processing, including outlier removal and feature engineering. The results demonstrate that the measures taken to refine the dataset significantly enhance the predictive performance of the developed models, reducing prediction errors and improving overall goodness of fit. Specifically, the Mean Absolute Error (MAE) values for both the LightGBM and random forest models were reduced from 0.48 and 0.94, respectively, to 0.16 for both models, and the coefficients of determination (R2) increased substantially from 0.84 and 0.72 to 0.95, in both cases. Moreover, feature importance analysis unveiled that pressure-related features make the most significant contributions to the formation of tree ensembles during the model training process. A parametric sensitivity analysis was conducted revealing that H2 uptake in MOFs is most sensitive to changes in adsorption enthalpy, followed by surface area and temperature, while showing lower sensitivity to variations in pressure, consistent with established literature. These results underscore the pivotal role of data enhancement methods in refining machine learning models and can be instrumental in accelerating the development and optimization of MOF materials for clean energy applications.

Feedback control of an experimental electrically-heated steam methane reformer

Steam methane reforming (SMR) is the most common industrial process to produce hydrogen (H2) from methane and water vapor. The SMR reactions are overall highly endothermic and traditionally, fossil fuels are burned to provide the necessary heat of reaction. However, it was found that electrifying the tubular SMR reactors using renewable electrons instead of conventional heating gives the opportunity of producing H2 with lower carbon emissions, lower reactor volumes, and higher carbon conversion. While current studies in this new technology focus on improving the catalysts or heating sources, a more efficient process control scheme can also significantly contribute to the understanding of potential challenges and opportunities of combining renewables and natural gas in the production of H2 at industrial scales. In order to further develop the electrically-heated SMR process and the associated control strategies, we have constructed an experimental Joule-heated SMR system at UCLA. The system contains a tubular reactor with a washcoated Ni/ZrO2 catalyst, two thermocouples connected to the top and bottom of the reactor, a power supply for providing electrical heating, and an on-line gas chromatograph (GC) for measuring outlet gas concentrations. The synthesis procedure of the catalyst, SMR data collection, and thermal considerations for catalyst degradation are described in this paper and are all accounted for experimentally when using a proportional integral (PI) controller to gradually increase the reactor temperature without harming the catalyst. Advanced control strategies, such as model predictive control (MPC), require a process model that uses measurement feedback to make predictions of the process time-evolution in order to optimize the control actions in real-time. We have previously developed a lumped-parameter modeling strategy for the SMR process. The MPC objective is to control the H2 production rate by manipulating the current flowing through the outer reactor shell. However, to use this model in an MPC, feedback values for all state variables should be provided by the sensors. In our experimental system, the on-line GC only gives discrete measurements with a long sampling period. To this end, the process model is incorporated into an extended Luenberger observer (ELO) that uses the reactor temperature and GC measurements to provide estimates of all the variables needed by the MPC. The ELO-based MPC system is then experimentally implemented on the process and it is demonstrated to be more efficient in terms of speed of the closed-loop response than the PI controller using delayed, measurement feedback by the GC.

Multifunctional gas safety monitoring and classification warning device

Due to the absence of automatic flameout and other essential functionalities in these devices, incidents such as gas leaks can easily lead to explosions. As a result, an autonomous safety monitoring and early warning device has been specifically developed for traditional gas stove equipment. This device is capable of activating a multi-tiered alarm and control mechanism when it reaches a predetermined threshold concentration. The main control of the Arduino Uno R3 microcontroller development board serves as the foundation for this device, which utilizes the MQ-5 gas sensor and 1602 liquid crystal display module to provide real-time gas concentration display. It also incorporates a sound and light alarm system that triggers when the concentration exceeds the predetermined threshold. Additionally, it employs a human infrared sensor and flame sensor as modules for fire detection and occupancy monitoring respectively. Any abnormal changes in these parameters will activate the sound and light alarm system. Equipped with an LED light and buzzer, this system provides real-time monitoring around gas stoves. Experimental results demonstrate excellent responsiveness under current conditions. Given its significant relevance in ensuring gas safety, this multifunctional gas safety monitoring device with classification early warning capabilities holds immense potential for market applications.

Gas concentration and refractive index sensor based on plasmonic induced absorption in metal-insulator-metal waveguide coupled with arc resonators structure

In this paper, the plasmonic induced absorption (PIA) effect is numerically studied in a plasmonic metal-insulator-metal (MIM) waveguide coupled with disk and arc-shaped resonators. The PIA spectrum characteristics and magnetic field distributions are investigated by the Finite difference-time domain method (FDTD) method, the PIA effect originated from the coupling of two resonance modes excited by the disk resonator and the arc-shaped resonator. The coupling mode theory (CMT) is used to fit the PIA transmission spectrum, and the calculated result fits well with the simulated spectrum. A double PIA spectrum can be observed by introducing another arc-shaped resonator both on the same or the opposite side. Three different structures are designed to achieve the PIA effect, and we found the PIA response can be easily tuned by altering the structure’s parameters. Three structures are designed and compared, the PIA spectra exhibit high refractive index (RI) sensing with maximum sensitivity of 867.2nmRIU−1. Besides, the proposed structure can also be used as a gas concentration (GC) sensor when the polyhexamethylene biguanide (PHMB) is introduced, a maximum sensitivity of 142.1 pm/ppm is achieved for the proposed structure II, the maximum figure of merit (FOM) and Q value are 57.8 and 65.9, respectively. Therefore, the results represented in this paper will be beneficial in the fields of high-performance refractive index and gas sensors.

Occurrence and Potential for Coalbed Methane Extraction in the Depocenter Area of the Upper Silesian Coal Basin (Poland) in the Context of Selected Geological Factors

Coalbed methane (CBM) is the only unconventional gas in Poland with estimated recoverable resources. The prospects for developing deep CBM have been explored in recent years by drilling deep exploration wells within the depocenter of the Upper Silesian Coal Basin. The purpose of this study is to analyze the occurrence and potential for CBM extraction in this area of the basin, which can be considered prospective due to the confirmed presence of significant amounts of gas and thick coal seams at depths &gt; 1500 m. The study examined the vertical and horizontal variability of the gas content in the studied area, the coal rank in the seams, thermal conditions, and coal reservoir parameters. The gas content in the seams, reaching more than 18 m3/t coaldaf at a depth of 2840 m, and indicative estimated gas resources of 9 billion m3 were found. The high gas content is accompanied by positive thermal and coal rank anomalies. The permeability and methane saturation of the coal seams are low, and therefore, potential methane production may prove problematic. However, the development of CBM extraction technologies involving directional drilling with artificial fracturing may encourage gas production testing in the study area.

Tunable optical parametric oscillator based on ZnGeP2 crystal for greenhouse gas remote sensing systems

This work is devoted to the development of a compact source of coherent radiation with frequency-energy characteristics and a spectral generation range that allows remote determination of background concentrations of greenhouse gases in the atmosphere. The aim of this work was to create a frequency parametric converter based on ZGP, pumped by Ho:YAG laser radiation. For use as a source in a mobile lidar for remote determination of the concentration of greenhouse gases in the atmosphere. In the course of the research, a layout of an Optical parametric oscillator OPO based on a ZGP crystal with Ho:YAG laser radiation pumping was developed. The system’s continuous failure-free operation time was 1.5 h at a pulse repetition rate of 10 kHz and a pulse energy of the generated radiation of 0.08 mJ. The tuning range of the OPO was from 3.3 to 5 μm when using a Lyot filter. The losses from the average generation power when the Lyot filter was introduced into the resonator were 30%. At the same time, it was possible to achieve a linewidth of the generated radiation of 0.7 nm. The divergence of the generated radiation did not exceed 1.5 mrad.The absorption spectrum of gases CO2, CH4, N2O, CO in a gas cell was simulated for the entire generation range of the ZnGeP2-based OPO. As a result of the simulation, the most intense absorption lines of gases CO2, CH4, N2O, CO in the OPO tuning range were revealed, the central wavelengths of the absorption lines and their spectral width were determined.

Measurement report: Cloud and environmental properties associated with aggregated shallow marine cumulus and cumulus congestus

Abstract. Mesoscale organization of marine convective clouds into linear or clustered states is prevalent across the tropical and subtropical oceans, and its investigation served as a guiding focus for a series of process study flights conducted as part of the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) during summer 2020, 2021, and 2022. These select ACTIVATE flights involved a novel strategy for coordinating two aircraft, with respective remote sensing and in situ sampling payloads, to probe regions of organized shallow convection for several hours. The main purpose of this measurement report is to summarize the aircraft sampling approach, describe the characteristics and evolution of the cases, and provide an overview of the datasets that can serve as a starting point for more detailed modeling and analysis studies. Six flights are described, involving a total of 80 dropsonde profiles that capture the environment surrounding clustered shallow convection. The flights include detailed observations of the vertical structure of cloud systems, comprising up to 20 in situ sampling levels. Four cases involved deepening convection rooted in the marine boundary layer that developed vertically to 2–5 km with varying precipitation amounts, while two cases captured more complex and developed cumulus congestus systems extending above 5 km. In addition to the thermodynamic and dynamic characterization afforded by dropsonde and in situ measurements, the datasets include cloud and aerosol microphysics, trace gas concentrations, aerosol and droplet composition, and cloud and aerosol remote sensing from high-spectral-resolution lidar and polarimetry.

A sensitive methane gas sensor based on sagnac interferometer with a cryptophane-A film-coated birefringent photonic crystal fiber : design and FEM simulation

As being sensitive to methane gas, cryptophane-A is widely used in fiber optic methane gas sensors. In order to further improve the sensitivity, a Sagnac interference (SI) was constructed with a cryptophane-A film coated birefringent photonic crystal fiber (PCF) to realize the methane gas sensing. Cryptophane-A film absorbs the methane gas and as a result its refractive index decreases linearly with the increasing of methane gas concentration. Simulation results utilizing the finite element method demonstrate that sensitivity reaches 124.4 nm/% when the methane concentration ranges from 0% to 3.5%. The sensor is relatively simple to prepare and can achieve high sensitivity, which has potential application in the field of monitoring methane gas leakage.

Gas concentration prediction based on ED-SLSTM model under the framework of Trend Prediction-Time Point Prediction

Gas concentration prediction based on ED-SLSTM model under the framework of Trend Prediction-Time Point Prediction

Economics of enhanced methane oxidation relative to carbon dioxide removal

Mitigating short-term global warming is imperative, and a key strategy involves reducing atmospheric methane (CH4) due to its high radiative forcing and short lifespan. This objective can be achieved through methods such as oxidising methane at its source or implementing enhanced oxidation techniques to reduce atmospheric CH4 concentrations. In this study, we use a range of metrics to analyse both the impact and value of enhanced CH4 oxidation relative to carbon dioxide (CO2) removal on global temperature. We apply these metrics to a select group of model studies of thermal-catalytic, photocatalytic, biological and capture-based oxidation processes under different greenhouse gas (GHG) concentrations. Using a target cost of €220-1000/tCO2 for CO2 removal, our findings indicate that metrics valuing enhanced oxidation techniques based on their contribution to mitigating the long-term level of warming show these techniques are uncompetitive with CO2 removal. However, when using metrics that value enhanced oxidation of CH4 based on its impact on the immediate rate of warming, photocatalytic methods may be competitive with CO2 removal, whereas biofiltration, thermal-catalytic oxidation and capture-based units remain uncompetitive. We conclude that if the policy goal is to target the immediate rate of warming, it may be more valuable to incentivise CO2 removal and enhanced oxidation of methane under separate GHG targets.

Clumpy star formation and an obscured nuclear starburst in the luminous dusty z = 4 galaxy GN20 seen by MIRI/JWST

Dusty star-forming galaxies emit most of their light at far-infrared to millimeter wavelengths as their star formation is highly obscured. Far-infrared and millimeter observations have revealed their dust, neutral and molecular gas properties. The sensitivity of JWST at rest-frame optical and near-infrared wavelengths now allows the study of the stellar and ionized gas content. We investigate the spatially resolved distribution and kinematics of the ionized gas in GN20, a dusty star-forming galaxy at z = 4.0548. We present deep MIRI/MRS integral field spectroscopy of the near-infrared rest-frame emission of GN20. We detect spatially resolved Paα, out to a radius of 6 kpc, distributed in a clumpy morphology. The star formation rate derived from Paα (144 ± 9 M⊙ yr−1) is only 7.7 ± 0.5% of the infrared star formation rate (1860 ± 90 M⊙ yr−1). We attribute this to very high extinction (AV = 17.2 ± 0.4 mag, or AV, mixed = 44 ± 3 mag), especially in the nucleus of GN20, where only faint Paα is detected, suggesting a deeply buried starburst. We identify four, spatially unresolved, clumps in the Paα emission. Based on the double peaked Paα profile, we find that each clump consists of at least two sub-clumps. We find mass upper limits consistent with them being formed in a gravitationally unstable gaseous disk. The ultraviolet bright region of GN20 does not have any detected Paα emission, suggesting an age of more than 10 Myr for this region of the galaxy. From the rotation profile of Paα, we conclude that the gas kinematics are rotationally dominated and the vrot/σm = 3.8 ± 1.4 is similar to low-redshift luminous infrared galaxies. From the Paα kinematics, we cannot distinguish between a rotational profile of a large disk and a late stage merger mimicking a disk. We speculate that GN20 is in the late stage of a major merger, where the clumps in a large gas-rich disk are created by the major merger, while the central starburst is driven by the merger event.

Distinguishing methane from other hydrocarbons using machine learning and atmospheric pressure plasma optical emission spectroscopy.

Abstract The ability to detect gas molecule and assign a concentration offers an inventive solution in the field of plasma integrated with Machine Learning (ML). The most important finding of this work is firstly, to develop an algorithm for gas-molecule identification using three different hydrocarbons (CH4, C2H2, C2H6) and secondly, organise a model for detecting gas concentration (classification). For this reason, initially eight different gases evaluated. The study confirms the present of the unique emission lines as a gas indicator, i.e., a wavelength peak related to hydrocarbons identified via increasing in CxHy concentration. By means of unique Variable Important in Projection (VIP), hydrocarbons can be distinguished. Our proposed Chemometric analysis strategy examined on &gt;1000 samples and results development of suitable techniques that are sufficiently rapid, accurate and innovative. This demonstrates the potential for real-time, portable, and continuous monitoring of trace gases with potential applications in medical, environmental, and industrial gas sensing.&amp;#xD;

Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables.

Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model's fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies.

New H I observations toward the NGC 5055 Galaxy Group with FAST

Abstract We report a new high-sensitivity H\,{\sc i} mapping observation of the NGC~5055 galaxy group over an area of $1.5^\circ\times0.75^\circ$ with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Our observation reveals that the warped H\,{\sc i} disk of NGC~5055 is more extended than what previously observed by WSRT, out to $ 23.9$ arcmin (61.7 kpc). &amp;#xD;The total \HI\ mass of NGC~5055 is determined to be $\rm\sim 1.1\times10^{10}\,M_\odot$.&amp;#xD;We identified three H\,{\sc i} clouds with H\,{\sc i} masses of the order of $\rm \sim 10^7\,M_\odot$ at the southeastern edge of the H\,{\sc i} disk, as well as a candidate high-velocity cloud with an H\,{\sc i} mass of \textcolor{blue}{$\rm (1.2\pm0.5) \times10^6\,M_\odot$} to the north of NGC~5055.&amp;#xD;The HI content of UGCA 337 is robustly detected for the first time by the FAST observations. It has a narrow HI linewidth of \textcolor{blue}{$W_{50}=17.4\pm3.8$ km s$^{-1}$} with a total \HI\ mass of \textcolor{blue}{($\rm 3.5\pm0.3)\times10^6\,M_\odot$}. Comparing the gas content and g-r color of UGCA~337 with typical low-mass dwarf galaxies, UGCA~337 appears relatively gas-poor despite its blue colour. This suggests that UGCA~337 may have undergone gas stripping in the past. &amp;#xD;We also analyzed the possible origin of the diffuse \HI\ clouds located at the outskirts of NGC~5055, and speculate that they might be the remnant features of a merger event in the past.

Multiarray Gas Sensors Using Ternary Combined Ti3C2Tx MXene-Based Nanocomposites.

This paper reports chemiresistive multiarray gas sensors through the synthesized ternary nanocomposites, using a one-pot method to integrate two-dimensional MXene (Ti3C2Tx) with Ti-doped WO3 (Ti-WO3/Ti3C2Tx) and Ti3C2Tx with Pd-doped SnO2 (Pd-SnO2/Ti3C2Tx). The gas sensors based on Ti-WO3/Ti3C2Tx and Pd-SnO2/Ti3C2Tx exhibit exceptional sensitivity, particularly in detecting 70% at 1 ppm acetone and 91.1% at 1 ppm of H2S. Notably, our sensors demonstrate a remarkable sensing performance in the low-ppb range for acetone and H2S. Specifically, the Ti-WO3/Ti3C2Tx sensor demonstrates a detection limit of 0.035 ppb for acetone, and the Pd-SnO2/Ti3C2Tx sensor shows 0.116 ppb for H2S. Simultaneous measurements with Ti-WO3/Ti3C2Tx- and Pd-SnO2/Ti3C2Tx-based sensors enable the evaluation of both the concentration and type of unknown target gases, such as acetone or H2S. Furthermore, density functional theory calculations are performed to clarify the role of Ti and Pd doping in enhancing the performance of Ti-WO3/Ti3C2Tx and Pd-SnO2/Ti3C2Tx nanocomposites. Theoretical simulations contribute to a deeper understanding of the doping effects, providing essential insights into the mechanisms underlying the enhanced gas response of the gas sensors. Overall, this work provides valuable insights into the gas-sensing mechanisms and introduces a novel approach for high-performance multiarray gas sensing.

Elevation-dependent warming: observations, models, and energetic mechanisms

Abstract. Observational data and numerical models suggest that, under climate change, elevated land surfaces warm faster than non-elevated ones. Proposed drivers of this “elevation-dependent warming” (EDW) include surface albedo and water vapour feedbacks, the temperature dependence of longwave emission, and aerosols. Yet the relative importance of each proposed mechanism both regionally and at large scales is unclear, highlighting an incomplete physical understanding of EDW. Here we expand on previous regional studies and use gridded observations, atmospheric reanalysis, and a range of climate model simulations to investigate EDW over the historical period across the tropics and subtropics (40° S to 40° N). Observations, reanalysis, and fully coupled models exhibit annual mean warming trends (1959–2014), binned by surface elevation, which are larger over elevated surfaces and broadly consistent across datasets. EDW varies by season, with stronger observed signals in local winter and autumn. Analysis of large ensembles of single-forcing simulations (1959–2005) suggests historical EDW is likely a forced response of the climate system rather than an artefact of internal variability and is primarily driven by increasing greenhouse gas concentrations. To gain quantitative insight into the mechanisms contributing to large-scale EDW, a forcing–feedback framework based on top-of-atmosphere energy balance is applied to the fully coupled models. This framework identifies the Planck and surface albedo feedbacks as being robust drivers of EDW (i.e. enhancing warming over elevated surfaces), with energy transport by the atmospheric circulation also playing an important role. In contrast, water vapour and cloud feedbacks along with weaker radiative forcing in elevated regions oppose EDW. Implications of the results for understanding future EDW are discussed.