CO2 Solubility Research Articles

Solubility of carbon dioxide in Triton X-100 – Water – ethaline solvent mixtures at 298.15 K

Deep eutectic solvents (DESs) and liquid surfactants have been known as safe and low risk materials to prepare appropriate media for various physical and chemical processes. These compounds have demonstrated manifest properties while playing the solvent role. The solubility of carbon dioxide (CO2) were determined in aqueous solutions Triton X-100 at 298.15 K as a function of CO2 pressure. Also, ternary solvent mixtures including Triton X-100 as an additive in aqueous solutions of ethaline were evaluated for the capacity of CO2 capture. Triton X-100, DES, and water showed a three-component liquid–liquid equilibrium with a limited single-phase region. The determination of CO2 solubility in the single-phase region of ternary mixtures revealed that the Triton X-100 as an additive increases the solubility of CO2 in the aqueous solutions of ethaline. The effect of Triton X-100 on CO2 solubility in binary mixtures of ethaline and water appeared differently based on the concentration of Triton X-100.

CO2 solubility and amine volatility data for low-concentration solutions of MEA, AMP, PZ and CESAR-1 blend (AMP/PZ)

Water-wash section(s) effectively reduce amine compound emissions in amine-based carbon capture processes. Reliable predictions of the behavior of these emission control technologies depend on detailed understanding of the vapor-liquid equilibrium (VLE) of water wash system. However, existing vapor-liquid equilibrium data in the literature focus primarily on solvent-like amine concentrations in amine–CO2–H2O systems; hence, new low-concentration VLE data are needed. In this work, the CO2 solubility in low-concentration solutions of monoethanolamine (MEA), piperazine (PZ), 2-amino-2-methyl-1-propanol (AMP), and AMP/PZ blend (CESAR-1) was investigated for temperatures between 303 and 353 K at atmospheric pressure. Additionally, amine volatility measurements for CO2-loaded low-concentration solutions of MEA, PZ, AMP and CESAR-1 were performed in the range of 313–373 K and 8–96 kPa. Overall, the data show that the CO2 partial pressure increases with increasing CO2 loading and temperature. Similarly, the amine partial pressure increases with increasing pressure and temperature. The new experimental data were compared with two existing VLE models using electrolyte non-random two-liquid activity coefficient models to evaluate the ability of the latter to predict vapor-liquid equilibrium behavior. Both CO2 solubility and amine volatility predictions by the models tend to show significant deviations. The comparison highlight the need for such low-concentration data for all systems studied. This data shall serve to improve existing water-wash modeling tools.

Reverse electron transfer: Novel anaerobic methanogenesis pathway regulated through exogenous CO2 synergized with biochar

Acid accumulation and carbon emission are two major challenges in anaerobic digestion. Syntrophic consortia can employ reverse electron transfer (RET) to facilitate thermodynamically unfavorable redox reactions during acetogenesis. However, the potential mechanisms and regulatory methods of RET remain unclear. This study examines the regulatory mechanisms by which exogenous CO2 affects RET and demonstrates that biochar maximizes CO2 solubility at 25.8 mmol/L to enhance effects further. CO2 synergized with biochar significantly increases cumulative methane production and propionate degradation rate. From the bioenergetic perspective, CO2 decreases energy level to a maximum of −87 kJ/mol, strengthening the thermodynamic viability. The underlying mechanism can be attributed to RET promotion, as indicated by increased formate dehydrogenase and enrichment of H2/formate-producing bacteria with their partner Methanospirillum hungatei. Moreover, the 5 % 13CH4 and methane contribution result show that CO2 accomplishes directed methanogenesis. Overall, this investigation riches the roles of CO2 and biochar in AD surrounding RET.

Local CO2 reservoir layer promotes rapid and selective electrochemical CO2 reduction

Electrochemical CO2 reduction reaction in aqueous electrolytes is a promising route to produce added-value chemicals and decrease carbon emissions. However, even in Gas-Diffusion Electrode devices, low aqueous CO2 solubility limits catalysis rate and selectivity. Here, we demonstrate that when assembled over a heterogeneous electrocatalyst, a film of nitrile-modified Metal-Organic Framework (MOF) acts as a remarkable CO2-solvation layer that increases its local concentration by ~27-fold compared to bulk electrolyte, reaching 0.82 M. When mounted on a Bi catalyst in a Gas Diffusion Electrode, the MOF drastically improves CO2-to-HCOOH conversion, reaching above 90% selectivity and partial HCOOH currents of 166 mA/cm2 (at −0.9 V vs RHE). The MOF also facilitates catalysis through stabilization of reaction intermediates, as identified by operando infrared spectroscopy and Density Functional Theory. Hence, the presented strategy provides new molecular means to enhance heterogeneous electrochemical CO2 reduction reaction, leading it closer to the requirements for practical implementation.

Nanocellulose membrane with double-salt deep eutectic solvent for efficient carbon capture

Membrane-based gas separation technology is becoming increasingly attractive as a feasible alternative for carbon capture to alleviate global warming. Here, a double-salt deep eutectic solvent (DES) composed of organic salt (choline chloride) and inorganic salt (ZnCl2) is designed. The as-prepared DES exhibits a superb affinity and compatibility with nanocellulose because of the interactions between salt ions and cellulose carboxyl/hydroxyl groups. Through a solvent impregnation process, the DES was integrated into the nanocellulose gel precursor and used as a facilitated transport agent to form the functionalized nanocellulose membrane. The DES is tightly attached to the cellulose fibers with a high uniformity in the membrane matrix. Gas separation tests demonstrated that the DES@nanocellulose membranes presented a maximum CO2 permeability of 155.8 Barrer, with CO2/N2 and CO2/CH4 ideal selectivities of 43.6 and 48.4, respectively. Mixed gas separation tests were performed and the optimal membrane exhibited separation factors of 48.1 and 52.3 for CO2/N2 and CO2/CH4, respectively. The membranes provide a high solubility for CO2 due to the presence of abundant zinc ions, choline cations and carboxyl groups in the network, which can establish reversible coordination with CO2 and promote CO2 transport.

Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents.

Carbon dioxide (CO2) is a detrimental greenhouse gas and is the main contributor to global warming. In addressing this environmental challenge, a promising approach emerges through the utilization of deep eutectic solvents (DESs) as an ecofriendly and sustainable medium for effective CO2 capture. Chemically reactive DESs, which form chemical bonds with the CO2, are superior to nonreactive, physically based DESs for CO2 absorption. However, there are no accurate computational models that provide accurate predictions of the CO2 solubility in chemically reactive DESs. Here, we develop machine learning (ML) models to predict the solubility of CO2 in chemically reactive DESs. As training data, we collected 214 data points for the CO2 solubility in 149 different chemically reactive DESs at different temperatures, pressures, and DES molar ratios from published work. The physics-driven input features for the ML models include σ-profile descriptors that quantify the relative probability of a molecular surface segment having a certain screening charge density and were calculated with the first-principle quantum chemical method COSMO-RS. We show here that, although COSMO-RS does not explicitly calculate chemical reaction profiles, the COSMO-RS-derived σ-profile features can be used to predict bond formation. Of the models trained, an artificial neural network (ANN) provides the most accurate CO2 solubility prediction with an average absolute relative deviation of 2.94% on the testing sets. Overall, this work provides ML models that can predict CO2 solubility precisely and thus accelerate the design and application of chemically reactive DESs.

Comparison of the effect of topology type and linker composition of zeolitic imidazolate framework fillers on the performance of mixed matrix membranes in CO2/N2 separation

Mixed matrix membranes (MMMs) combine the high separation performance of porous materials with the processibility of polymers and so possess potential for carbon capture from CO2-containing gas streams. Zeolitic imidazolate frameworks (ZIFs) are promising candidates as molecular-sieve fillers in MMMs due to their tunability and ease of synthesis. We have compared four ZIFs, all as nanoparticles of similar sizes (ca. 400 nm), as MMM fillers, to investigate the effects of ZIF structure and chemistry on MMM performance of pure gas (CO2, N2) permeation under the same conditions. The chosen ZIFs include two that exhibit strong CO2 adsorption (hybrid ZIF-7/COK-17 and ZIF-94) and two that have higher pore volumes but weaker CO2 interactions (ZIF-8 and a hybrid ZIF-11/ZIF-71). The hybrid ZIF-7/COK-17 and ZIF-94 are structurally related to ZIF-7 (rhombohedral sod topology) and ZIF-8 (cubic sod), respectively, via partial or complete substitution of benzimidazole or 2-methylimidazole by 4,5-dichloroimidazole or 4-methyl-5-imidazolecarboxaldehyde, while the hybrid ZIF-11/ZIF-71 has the rho topology but the same composition as the ZIF-7/COK-17 hybrid. In the first part of the comparative study, MMMs based on two types of commercial polymers, Matrimid®5218 and PEBAX-MH1657, were prepared containing the ZIF-7/COK-17 hybrid and also with ZIF-94. ZIF-94 shows much better compatibility with the polymers, forming homogeneous dispersions at all loadings attempted (≤35 % wt%) whereas the hybrid shows inhomogeneity above 12 wt% in each case. At 12 wt% loading, both fillers show an increase in CO2 permeability at 1.2 bar and 293 K compared to the pure membrane (in PEBAX, this increases from 49.5 to 60 and 68 Barrer) which is the result of increased solubility compensating for decreased diffusivity, and this improvement in permeability continues to increase at the higher levels of loading possible with ZIF-94. ZIF-7/COK-17 in PEBAX show higher selectivity, achieving a calculated CO2/N2 selectivity up to 70. Further investigation of CO2 and N2 permeation on MMMs with the four ZIFs at 12 wt% in PEBAX-MH1657 showed a clear distinction between the ZIF-94 and ZIF-7/COK-17 MMMs (which show higher membrane solubilities but lower diffusivities) compared to ZIF-8 and ZIF-11/ZIF-71 MMMs. At the loading chosen, the CO2 permeability increase achieved by the four ZIFs over PEBAX-MH1657 increases in the order ZIF-11/-71, ZIF-7-COK-17 (ca. 60 Barrer) < ZIF-94 (68) < ZIF-8 (81), reflecting the complex interplay between CO2 solubility (increasing with interaction strength) and diffusivity (increasing with available cage and window size). The calculated CO2/N2 selectivity is highest for the hybrid ZIF-7/COK-17 membrane (70), which is attributed to molecular sieving effects in the rhombohedral sod structure.

Physical absorption and thermodynamic modeling of CO2 in new deep eutectic solvents

Due to the advantages of deep eutectic solvents (DESs), such as good CO2 absorption performance, excellent biodegradability, low cost, and simple synthesis, attention has been drawn to selecting DESs with high efficiency for capturing CO2 in a green and environmentally compatible context. In the present study, acetamide as the hydrogen bond acceptor (HBA) and diethylene glycol, 1,2-propanediol, and 1,3-propanediol as the hydrogen bond donors (HBD) for the synthesis of DESs. First, the density and viscosity of these prepared DESs were measured under standard ambient pressure and at temperatures ranging between 293 K and 343 K. Subsequently, the CO2 solubility in DESs was ascertained by the isovolumetric saturation experimental method. The results indicated that all synthesized DESs exhibited physical absorption of CO2 and the diethylene glycol-based DESs had the highest dissolvability for CO2. For the DES with diethylene glycol and 1,2-propanediol as HBD, the CO2 solubility increased as the molar ratio of HBD decreased. On the contrary, the amount of CO2 adsorbed by 1,3-propanediol-based DES increased with increasing 1,3-propanediol content. The semi-empirical Jou and Mather model allows for an efficient correlation to be made among experimentally determined CO2 solubilities, temperatures, and the CO2 partial pressures. Comparing the deviations between the experimental and fitted values, the average relative deviation is small. By estimating the Henry constants and Manuscript File Click here to view linked References comparing them with the literature, it was demonstrated that the synthesized DESs are competitive in absorbing CO2.

Multiscale modeling of CO2 capture in dicationic ionic liquids: Evaluating the influence of hydroxyl groups using DFT-IR, COSMO-RS, and MD simulation methods.

This work employs a combination of density functional theory-infrared (IR), conductor-like screening model for real solvents (COSMO-RS), and molecular dynamic (MD) methods to investigate the impact of hydroxyl functional groups on CO2 capture within dicationic ionic liquids (DILs). The COSMO-RS reveals that hydroxyl groups in DILs reduce the macroscopic solubility of CO2 but improve the selectivity of CO2 over CO, H2, and CH4 gases. Quantum methods in the gas phase and MD simulations in the liquid phase were conducted to delve deeper into the underlying mechanisms. The IR spectrum analysis confirms red shifts in CO2's asymmetric stretching mode and blue shifts in the CR-HR bond of the dication, indicating CO2-DIL interactions and the weakening of the anion-cation interactions caused by the presence of CO2. The results show that the positioning of anions around hydroxyl groups and HR atoms in rings inhibits the proximity of CO2 molecules, causing the hydrogen atoms within methylene groups to accumulate CO2. van der Waals forces were found to dominate the interaction between ions and CO2. The addition of hydroxyl groups strengthens the electrostatic interactions and hydrogen bonds between dications and anions. The stronger interaction energy between ions in [C5(mim)2-(C2)2(OH)2][NTf2]2 limits the displacement of CO2 molecules within this DIL compared to [C5(mim)2-(C4)2][NTf2]2. Compared to [C5(mim)2-(C4)2][NTf2]2, [C5(mim)2-(C2)2(OH)2][NTf2]2 exhibits stronger ion-ion interactions, higher density, and reduced free volume, resulting in a reduction in CO2 capture. These results provide significant insights into the intermolecular interactions and vibrational properties of CO2 in DIL complexes, emphasizing their significance in developing efficient and sustainable strategies for CO2 capture.

Amine functionalized supported ionic liquid membranes (SILMs) for CO2/N2 separation

Supported ionic liquid membranes (SILMs), containing aprotic N-heterocyclic anion ionic liquids (AHA ILs) in an inorganic inert support, exhibit CO2/N2 permselectivity values as high as 640 at 35.0 °C and 0.03 bar CO2, which represents conditions similar to post-combustion carbon capture (PCCC) from a natural gas power plant. A Fickian model fit to the experimental data estimates CO2 permeability at direct air capture (DAC) conditions of 10,400 barrer and a CO2/N2 permselectivity of 4000 for the best performing IL, triethyl(octyl)phosphonium 4-bromopyrazolide ([P2228][4-BrPyra]). The most important criterion for high selectivity is a large equilibrium constant for binding between the IL and CO2, which results in high CO2 solubility. ILs with smaller molar volumes and with no fluoroalkyl chains enhance N2 rejection. Low viscosity and high IL molar density also enhance CO2/N2 permselectivity and CO2 permeance.

Investigation of effective CO2 capture by ternary deep eutectic solvents based on superbase

The absorption and separation of CO2 is one of the main strategies to reduce carbon emissions and mitigate global climate change. In this context, a series of ternary superbase/betaine-based deep eutectic solvents (DESs) were synthesized to capture CO2, and their density, viscosity and thermal stability were investigated. The effects of temperature, pressure, different hydrogen bond donors (HBDs) and alkalinity of the superbase on their CO2 affinity were evaluated by the isovolumetric saturation method. The uptake of CO2 by the DES synthesized from betaine, 1,2-propanediol and 1,8-diazabicyclo(5.4.0)undec-7-ene reached a maximum of 1.5071 mol·kg−1 at 303.15 K and a pressure of 0.187 MPa. The simultaneous chemical and physical adsorption of CO2 by the ternary DESs was verified by FTIR spectroscopy, and the absorption mechanism was also proposed. The reaction equilibrium thermodynamic model (RETM) was improved to obtain the “Active DES” model, which was correlated with the experimentally determined CO2 solubility. The reaction equilibrium constants and Henry's law constants were obtained for each ternary system. The ternary superbase/betaine DESs proposed in this paper are green, economical and high-performance absorbers for capturing CO2.

Nanoporous Ti3C2O2 monolayer towards high permeance and selectivity for CO2/N2 separation: CO2-MXene interaction tuning

MXene, as a new gas separation membrane, has good stability and suitable pore topological structures to mitigate the greenhouse effect caused by excessive CO2 emission. However, the large quadrupole and polarizability of CO2 make it a strong interaction with MXene, thus leading to a strong solubility and weak diffusivity of CO2. Here, we design nanoporous Ti3C2O2 monolayers (YmXn) with exposed C atoms that could tune CO2-Ti3C2O2 interaction so as to improve gas separation performance by density functional theory and molecular dynamics simulation. The scope of “Cut-off” size is obtained by analyzing the gas separation state from un-permeation to permeation. Results reveal that nanoporous Ti3C2O2 membranes achieve both outstanding CO2 selectivity and permeance. The best-performance Y3X3-Ti possesses CO2 permeance of 1.01 × 10-4 and 2.56 × 10-5 mol s−1 m−2 Pa−1 for equimolar gas mixtures and flue gases, respectively, and both the CO2/N2 selectivity reach 100 %. The gas separation mechanism is dominated by diffusion selectivity rather than solution selectivity in nanoporous Ti3C2O2. The dynamic processes of gas permeation, including the center of mass, mean square displacement, and radial distribution function of the gases, are precisely investigated. The electrostatic repulsion between the C atoms in the channel of the membrane and CO2 follows the sequence Y3X3-C > Y2X4 > Y3X3-Ti, which is the key to regulating gas diffusion. Results of this work highlight that tuning gas-membrane interaction and designing “Cut-off” size are effective strategies to achieve excellent CO2 separation performance, and shed new light on the intrinsic mechanism in high-performance gas separation membranes.

Developing statistical and machine learning models for predicting CO2 solubility in live crude oils

CO2 solubility in crude oil primarily affects the viscosity and swelling capacity of oil and determines the displacement efficiency of CO2 flooding during the enhanced oil recovery process. Dissolution of CO2 into crude oil also ensures safe and permanent storage of CO2 in depleted formations via solubility trapping. This study employed statistical and machine learning algorithms to develop precise and stable predictive models for estimating CO2 solubility in crude oils with dissolved gas. Recognizing the limitations of existing experimental data on CO2 solubility for live oils (crude oils with dissolved gas), we introduced a novel approach employing statistical and machine learning techniques for developing accurate and robust predictive models. Our methodology combined regularization, k-fold cross-validation, and statistical significance tests to ensure the robustness of the models, practices not considered in the previous studies. A linear regression model with L2 regularization is developed as the base model and compared with a 2nd order polynomial model and an ensemble feed-forward neural network (FNN) model integrated with gradient boosting (GB). The incorporation of GB into the neural network capitalizes on the additive sequence of FNN models, thereby enhancing the accuracy and reliability of predictions. Moreover, an improved user-accessible and easily implementable correlation is proposed to estimate CO2 solubility in live crude oils at diverse operating conditions as a function of reservoir temperature, saturation pressure, bubble-point pressure, oil molecular weight, and specific gravity. The performances of the developed model and the correlation are compared against the literature models and correlations. The FNN model with an overall R2 score of 0.9978, average absolute relative deviation (AARD) of 1.54 %, and root mean squared error (RMSE) of 0.0083 outperformed the previously published models and correlations. The new correlation, while being relatively simpler than previously published correlations, showed similar predictive performance with an overall R2 score of 0.9817, AARD of 4.98 %, and RMSE of 0.0225. The model and the correlation can be used to estimate CO2 solubility in live crude oils with 0.67 to 0.97 specific gravity for reservoir temperatures and pressures in the range of 28 to 124 °C and 3.23 to 32.76 MPa, respectively.

Measurement and prediction of CO2 solubility in organic electrolytes for high pressure CO2 reduction

The electrochemical CO2 reduction reaction (CO2RR) to organic products is becoming increasingly important. Here, organic electrolytes play a crucial role, since adding salt to organic solvent allows an increase in the current density, and together with supercritical CO2 organic electrolytes suppress hydrogen evolution during the CO2RR. Since the selectivity to organic products depends on the CO2 content, CO2 solubility was investigated in organic electrolytes at different salt concentrations, pressures (up to 150 bar), and temperatures (25 °C and 70 °C). These organic electrolytes included the mixtures NaI-methanol, KSCN-methanol, (C2H5)4NBF4-acetonitrile, and (C4H9)4NBF4-acetonitrile. Afterwards, electrolyte Perturbed-Chain Statistical Associating Fluid Theory (ePC-SAFT) was applied to predict the CO2 solubility in these organic electrolytes. The results showed that i) CO2 solubility decreased with increasing temperature and salt concentration, ii) dissolving CO2 may cause a precipitation of salts, and that iii) ePC-SAFT allowed to qualitatively model the CO2 solubility in organic electrolytes

Research progress of diagnostic and therapeutic value of carbon dioxide-derived indicators in patients with sepsis

Effectively assessing oxygen delivery and demand is one of the key targets for fluid resuscitation in sepsis. Clinical signs and symptoms, blood lactic acid levels, and mixed venous oxygen saturation (SvO2) or central venous oxygen saturation (ScvO2) all have their limitations. In recent years, these limitations have been overcome through the use of derived indicators from carbon dioxide (CO2) such as mixed veno-arterial carbon dioxide partial pressure difference (Pv-aCO2, PCO2 gap, or ΔPCO2), the ratio of mixed veno-arterial carbon dioxide partial pressure difference to arterial-mixed venous oxygen content difference (Pv-aCO2/Ca-vO2). Pv-aCO2, PCO2 gap or ΔPCO2 is not a purely anaerobic metabolism indicator as it is influenced by oxygen consumption. However, it reliably indicates whether blood flow is sufficient to carry CO2 from peripheral tissues to the lungs for clearance, thus reflecting the adequacy of cardiac output and metabolism. The Pv-aCO2/Ca-vO2 may serve as a marker of hypoxia. SvO2 and ScvO2 represent venous oxygen saturation, reflecting tissue oxygen utilization. When oxygen delivery decreases but tissues still require more oxygen, oxygen extraction rate usually increases to meet tissue demands, resulting in decreased SvO2 and ScvO2. But in some cases, even if the oxygen delivery rate and tissue utilization rate of oxygen are reduced, it may still lead to a decrease in SvO2 and ScvO2. Sepsis is a classic example where tissue oxygen utilization decreases due to factors such as microcirculatory dysfunction, even when oxygen delivery is sufficient, leading to decrease in SvO2 and ScvO2. Additionally, the solubility of CO2 in plasma is approximately 20 times that of oxygen. Therefore, during sepsis or septic shock, derived variables of CO2 may serve as sensitive markers for monitoring tissue perfusion and microcirculatory hemodynamics. Its main advantage over blood lactic acid is its ability to rapidly change and provide real-time monitoring of tissue hypoxia. This review aims to demonstrate the principles of CO2-derived variables in sepsis, assess the available techniques for evaluating CO2-derived variables during the sepsis process, and discuss their clinical relevance.

Numerical investigation of carbon dioxide capture using nanofluids via machine learning

Using nanofluids to capture CO2 is a promising method of reducing emissions. The goal of this study was to develop models that could forecast how well water-based nanofluids would absorb CO2. Several machine learning models, such as Decision Tree, Random Forest, eXtreme Gradient Boosting, and K-Nearest Neighbors, underwent training using a total of 1306 experimental datasets. These datasets contained information on the CO2 solubility in aqueous solutions for different types of nanoparticles. In terms of Average Absolute Relative Deviation (AARD%), Mean Absolute Error (MAE), Relative Absolute Error, Mean Squared Error, and Correlation Coefficient, the predictive performance on separate test data was assessed. The XGBoost model demonstrates a higher degree of accuracy in simulating the absorption capacity of aqueous nanofluid compared to other models. This is evident through the AARD value of 2.8%, MSE of 0.00084, MAE of 0.012 and an R value of 0.992. The research provides insight into the utilization of different machine learning algorithms for simulating the absorption of CO2 by nanofluids. By employing accurate data-driven models, the efficiency of nanofluid-based CO2 capture processes can be enhanced through improvements in their design and operating conditions.

Quantitative analysis method for absorption mechanism and energy consumption of CO2 capture by amino acid ionic liquids

Amino acid ionic liquids (AAILs) are attractive candidates for CO2 capture due to their high solubility for CO2. Quantitatively analyzing the interaction of amino groups in AAILs with CO2 and evaluating the energy consumption of CO2 capture is critical to the molecular design of ideal AAILs. In this work, a thermodynamic model based on the cubic plus association equation of state was proposed to calculate the contribution from the chemical reactions between various stoichiometric ratios of CO2 and AAIL (Nm:n,m:n is stoichiometric ratio) on CO2 solubility. The results show that the larger contribution to the CO2 solubility in [Glu], [Tyr] and [Gln]-IL with strong polar groups is derived from association scheme N1:2, while the N1:1 association interaction is the dominant contribution of [Gly], [Ala], [Val] and [Pro]-IL with hydrogen atom or short alkyl side chains, and [VBIm]-IL with multiple amino groups. The N2:1 association interaction is primary between CO2 and [Cho][His] or [Cho][Arg]. Besides, the contributions of these association effects are greater than that of dispersion and repulsion to CO2 solubility. Then, the solvent cyclic capacity and regeneration energy of AAILs in the pressure swing and temperature swing CO2 capture system were estimated. More amino groups result in higher regeneration energy consumption and do not necessarily increase solvent cyclic capacity. Among the studied AAILs, [Cho][Gly] which exhibits high cyclic capacity and low energy consumption has great potential in CO2 capture solvent.

Enhanced CO2 uptake of the coastal ocean is dominated by biological carbon fixation

Observational reconstructions indicate a contemporary increase in coastal ocean CO2 uptake. However, the mechanisms and their relative importance in driving this globally intensifying absorption remain unclear. Here we integrate coastal carbon dynamics in a global model via regional grid refinement and enhanced process representation. We find that the increasing coastal CO2 sink is primarily driven by biological responses to climate-induced changes in circulation (36%) and increasing riverine nutrient loads (23%), together exceeding the ocean CO2 solubility pump (41%). The riverine impact is mediated by enhanced export of organic carbon across the shelf break, thereby adding to the carbon enrichment of the open ocean. The contribution of biological carbon fixation increases as the seawater capacity to hold CO2 decreases under continuous climate change and ocean acidification. Our seamless coastal ocean integration advances carbon cycle model realism, which is relevant for addressing impacts of climate change mitigation efforts.

Modeling gas–liquid equilibrium of carbon dioxide in mixtures of (1-butyl-3-methyl-imidazolium acetate + 1-(2-aminoethyl) piperazine + water) and (1-butyl-3-methyl-imidazolium acetate + bis (3-aminopropyl) amine + water) by electrolyte non-random two liquid (e-NRTL) model

Targeting the control in emissions of post-combustion carbon dioxide capture from large point sources, a simulation approach towards the estimation of important thermodynamic property parameters useful in designing of carbon capture and sequestration technologies is being proposed. In the current work, simulation is carried out for modeling the gas – liquid equilibrium of CO2 in two aqueous mixtures of ionic liquid 1-butyl-3-methyl imidazolium acetate ([bmim][Ac]) and amines viz. ([bmim][Ac] + 1-(2-aminoethyl) piperazine) (AEP) and ([bmim][Ac] + bis (3-aminopropyl) amine) (APA). The experimental CO2 solubilities were taken from our previously published work and is regressed with electrolyte–Non-Random Two Liquid (e-NRTL) model for the liquid phase while Redlich-Kwong (RK) equations of state (EOS) was used to model the gas phase. Various thermodynamic parameters of importance and concentration of species in the mixtures were regressed in the model using Aspen Plus 14.

Unravelling the main mechanism responsible for nocturnal CO2 uptake by dryland soils

Soil respiration, or CO2 efflux from soil, is a crucial component of the terrestrial carbon cycle in climate models. Contrastingly, many dryland soils absorb atmospheric CO2 at night, but the exact mechanisms driving this uptake are actively debated. Here we used a mechanistic model with heuristic approaches to unravel the underlying processes of the observed patterns of soil-atmosphere CO2 fluxes. We show that the temperature drop during nighttime is the main driver of CO2 uptake by increasing CO2 solubility and local water pH of a thin water film on soil particle surfaces, providing favourable conditions for carbonate precipitation. Our data demonstrate that the nocturnal inorganic carbon absorption is a common soil process, but often offset by biological CO2 production. The uptake rates can be impacted by different successional stages of biocrusts that consume or produce CO2 and modify the pH of the soil water film, which can be maintained by non-rainfall water inputs, such as pore space condensation. Annual estimates of nocturnal carbon uptake, based on in situ continuous measurements at the soil level in drylands are still very scarce, but fluxes of up to several tens of g C m−2 y−1 have been reported, potentially accounting for a considerable fraction of the global residual terrestrial carbon sink.