Amino-decorated organosilica membranes for highly permeable CO2 capture

Nano-enabled gas separation membranes: Advancing sustainability in the energy-environment Nexus

Gas separation membranes serve as crucial to numerous industrial processes, including gas purification, energy production, and environmental protection. Recent advancements in nanomaterials have drastically revolutionized the process of developing tailored gas separation membranes, providing unreachable levels of control over the performance and characteristics of the membrane. The incorporation of cutting-edge nanomaterials into the composition of traditional polymer-based membranes has provided novel opportunities. This review critically analyses recent advancements, exploring the diverse types of nanomaterials employed, their synthesis techniques, and their integration into membrane matrices. The impact of nanomaterial incorporation on separation efficiency, selectivity, and structural integrity is evaluated across various gas separation scenarios. Furthermore, the underlying mechanisms behind nanomaterial-enhanced gas transport are examined, shedding light on the intricate interactions between nanoscale components and gas molecules. The review also discusses potential drawbacks and considerations associated with nanomaterial utilization in membrane development, including scalability and long-term stability. This review article highlights nanomaterials' significant impact in revolutionizing the field of selective gas separation membranes, offering the potential for innovation and future directions in this ever-evolving sector.

Prediction of CO2 solubility in Ionic liquids for CO2 capture using deep learning models

Ionic liquids (ILs) are highly effective for capturing carbon dioxide (CO2). The prediction of CO2 solubility in ILs is crucial for optimizing CO2 capture processes. This study investigates the use of deep learning models for CO2 solubility prediction in ILs with a comprehensive dataset of 10,116 CO2 solubility data in 164 kinds of ILs under different temperature and pressure conditions. Deep neural network models, including Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM), were developed to predict CO2 solubility in ILs. The ANN and LSTM models demonstrated robust test accuracy in predicting CO2 solubility, with coefficient of determination (R2) values of 0.986 and 0.985, respectively. Both model's computational efficiency and cost were investigated, and the ANN model achieved reliable accuracy with a significantly lower computational time (approximately 30 times faster) than the LSTM model. A global sensitivity analysis (GSA) was performed to assess the influence of process parameters and associated functional groups on CO2 solubility. The sensitivity analysis results provided insights into the relative importance of input attributes on output variables (CO2 solubility) in ILs. The findings highlight the significant potential of deep learning models for streamlining the screening process of ILs for CO2 capture applications.

Ultrapermeable 2D-channeled graphene-wrapped zeolite molecular sieving membranes for hydrogen separation

The efficient separation of hydrogen from methane and light hydrocarbons for clean energy applications remains a technical challenge in membrane science. To address this issue, we prepared a graphene-wrapped MFI (G-MFI) molecular-sieving membrane for the ultrafast separation of hydrogen from methane at a permeability reaching 5.8 × 106 barrers at a single gas selectivity of 245 and a mixed gas selectivity of 50. Our results set an upper bound for hydrogen separation. Efficient molecular sieving comes from the subnanoscale interfacial space between graphene and zeolite crystal faces according to molecular dynamic simulations. The hierarchical pore structure of the G-MFI membrane enabled rapid permeability, indicating a promising route for the ultrafast separation of hydrogen/methane and carbon dioxide/methane in view of energy-efficient industrial gas separation.

Network Structure Engineering of Organosilica Membranes for Enhanced CO2 Capture Performance

The membrane separation process for targeted CO2 capture application has attracted much attention due to the significant advantages of saving energy and reducing consumption. High-performance separation membranes are a key factor in the membrane separation system. In the present study, we conducted a detailed examination of the effect of calcination temperatures on the network structures of organosilica membranes. Bis(triethoxysilyl)acetylene (BTESA) was selected as a precursor for membrane fabrication via the sol-gel strategy. Calcination temperatures affected the silanol density and the membrane pore size, which was evidenced by the characterization of FT-IR, TG, N2 sorption, and molecular size dependent gas permeance. BTESA membrane fabricated at 500 °C showed a loose structure attributed to the decomposed acetylene bridges and featured an ultrahigh CO2 permeance around 15,531 GPU, but low CO2/N2 selectivity of 3.8. BTESA membrane calcined at 100 °C exhibited satisfactory CO2 permeance of 3434 GPU and the CO2/N2 selectivity of 22, displaying great potential for practical CO2 capture application.

Tailor-Made Modification of Commercial Ceramic Membranes for Environmental and Energy-Oriented Gas Separation Applications

Ceramic membranes have been considered as potential candidates for several gas separation processes of industrial interest, due to their increased thermal and chemical stability compared to polymeric ones. In the present study, commercial Hybrid Silica (HybSi^®) membranes have been evaluated and modified accordingly, to enhance their gas separation performance for targeted applications, including CO₂ removal from flue gas and H₂ recovery from hydrogen-containing natural gas streams. The developed membranes have been characterized before and after modification by relative permeability, single gas permeation, and equimolar separation tests of the respective gas mixtures. The modification procedures, involving in situ chemical vapor deposition and superficial functionalization, aim for precise control of the membranes’ pore size and surface chemistry. High performance membranes have been successfully developed, presenting an increase in H₂/CH₄ permselectivity from 12.8 to 45.6 at 250 °C. Ultimately, the modified HybSi^® membrane exhibits a promising separation performance at 250 °C, and 5 bar feed pressure, obtaining above 92% H₂ purity in the product stream combined with a notable H₂ recovery of 65%, which can be further improved if a vacuum is applied on the permeate side, leading to 94.3% H₂ purity and 69% H₂ recovery.

Multiple Amine-Contained POSS-Functionalized Organosilica Membranes for Gas Separation

A new polyhedral oligomeric silsesquioxane (POSS) designed with eight –(CH₂)₃–NH–(CH₂)₂–NH₂ groups (PNEN) at its apexes was used as nanocomposite uploading into 1,2-bis(triethoxysilyl)ethane (BTESE)-derived organosilica to prepare mixed matrix membranes (MMMs) for gas separation. The mixtures of BTESE-PNEN were uniform with particle size of around 31 nm, which is larger than that of pure BTESE sols. The characterization of thermogravimetric (TG) and gas permeance indicates good thermal stability. A similar amine-contained material of 3-aminopropyltriethoxysilane (APTES) was doped into BTESE to prepare hybrid membranes through a copolymerized strategy as comparison. The pore size of the BTESE-PNEN membrane evaluated through a modified gas-translation model was larger than that of the BTESE-APTES hybrid membrane at the same concentration of additions, which resulted in different separation performance. The low values of E_p(CO₂)-E_p(N₂) and E_p(N₂) for the BTESE-PNEN membrane at a low concentration of PNEN were close to those of copolymerized BTESE-APTES-related hybrid membranes, which illustrates a potential CO₂ separation performance by using a mixed matrix membrane strategy with multiple amine POSS as particles.