Gases

Carbon dioxide (CO₂), a major greenhouse gas, contributes significantly to global warming and environmental degradation. Carbon Capture, Utilization, and Storage (CCUS) is a promising strategy to mitigate atmospheric CO₂ levels. One widely applied utilization approach involves injecting captured CO₂ into depleted oil reservoirs to enhance oil recovery—a technique known as CO₂-Enhanced Oil Recovery (CO₂-EOR). The effectiveness of CO₂-EOR largely depends on complex rock–fluid interactions, including mass transfer, wettability alteration, capillary pressure, and interfacial tension (IFT). Various factors, such as the presence of asphaltenes, salinity, pressure, temperature, and rock type, influence these interactions. This review explores the impact of these parameters on the IFT between CO₂ and oil/water systems, drawing on findings from both experimental studies and molecular dynamics (MD) simulations. The literature indicates that increased temperature, reduced pressure, lower salinity, and the presence of asphaltenes tend to reduce IFT at the oil–water interface. Similarly, elevated temperature and pressure, along with asphaltene content, also lower the surface tension between CO₂ and oil. Most MD simulations employ synthetic oil mixtures of various alkanes and use tools such as LAMMPS and GROMACS. Experimentally, the pendant drop method is most commonly used with crude oil and brine samples. Future research employing actual reservoir fluids and alternative measurement techniques may yield more accurate and representative IFT data, further advancing the application of CO₂-EOR.

Gasification of residual biomass has emerged as an efficient thermochemical conversion process, applicable to a wide range of uses, such as electricity generation; chemical manufacturing; and the production of liquid biofuels, BioSNG (biomass-based synthetic natural gas), and hydrogen. Thus, gasification of biomass residues not only constitutes an important contribution toward decarbonizing the economy but also promotes the efficient utilization of renewable resources. Although a variety of gasification technologies are available, there are no clear guidelines for selecting the type of gasifier appropriate depending on the feedstock and the desired downstream products. Herein, we propose a gasifier classification model based on an extensive literature review, combined with a multi-criteria decision-making approach. A comprehensive and up-to-date literature review was conducted to gain a thorough understanding of the current state of knowledge in biomass gasification. The different features of the different types of gasifiers, in the context of biomass gasification, are presented and compared. The gasifiers were reviewed and evaluated considering criteria such as processing capacity, syngas quality, process performance, feedstock flexibility, operational and capital costs, environmental impact, and specific equipment features. A multi-criteria classification methodology was evaluated for assessing biomass gasifiers. A case study of such methodology was a applied to determine the best gasifiers for BioSNG inclusion in the natural gas distribution system in a small-scale scenario. Validation was conducted by comparing the matrix findings with commercially implemented gasification projects worldwide.

This work presents a proof of concept including simulation and experimental validations of acoustic gas sensor prototypes for trace CO₂ detection up to 1 ppm. For the detection of lower gas concentrations especially, the dependency of acoustic resonances on the molecular weights and, consequently, the speed of sound of the gas mixture, is exploited. We explored two resonator types: a cylindrical acoustic resonator and a Helmholtz resonator intrinsic to the MEMS microphone’s geometry. Both systems utilized mass flow controllers (MFCs) for precise gas mixing and were also modeled in COMSOL Multiphysics 6.2 to simulate resonance shifts based on thermodynamic properties of binary gas mixtures, in this case, N₂-CO₂. We performed experimental tracking using Zurich Instruments MFIA, with high-resolution frequency shifts observed in µHz and mHz ranges in both setups. A compact and geometry-independent nature of MEMS-based Helmholtz tracking showed clear potential for scalable sensor designs. Multiple experimental trials confirmed the reproducibility and stability of both configurations, thus providing a robust basis for statistical validation and system reliability assessment. The good simulation experiment agreement, especially in frequency shift trends and gas density, supports the method’s viability for scalable environmental and industrial gas sensing applications. This resonance tracking system offers high sensitivity and flexibility, allowing selective detection of low CO₂ concentrations down to 1 ppm. By further exploiting both external and intrinsic acoustic resonances, the system enables highly sensitive, multi-modal sensing with minimal hardware modifications. At microscopic scales, gas detection is influenced by ambient factors like temperature and humidity, which are monitored here in a laboratory setting via NDIR sensors. A key challenge is that different gas mixtures with similar sound speeds can cause indistinguishable frequency shifts. To address this, machine learning-based multivariate gas analysis can be employed. This would, in addition to the acoustic properties of the gases as one of the variables, also consider other gas-specific variables such as absorption, molecular properties, and spectroscopic signatures, reducing cross-sensitivity and improving selectivity. This multivariate sensing approach holds potential for future application and validation with more critical gas species.