The effect of chain rigidity and microstructure on gas separation performance of the cardo-based polyimides

Phenylethynyl-Terminated Imide Oligomer-Based Thermoset Resins

Phenylethynyl-terminated imide (PETI) oligomers are highly valued for their diverse applications in films, moldings, adhesives, and composite material matrices. PETIs can be synthesized at varying molecular weights, enabling the fine-tuning of their properties to meet specific application requirements. Upon thermal curing, these oligomers form super-rigid network structures that enhance solvent resistance, increase glass-transition temperatures, and improve elastic moduli. Their low molecular weights and melt viscosities further facilitate processing, making them particularly suitable for composites and adhesive bonding. This review examines recent advancements in developing ultra-high-temperature PETIs, focusing on their structure-processing-properties relationships. It begins with an overview of the historical background and key physicochemical characteristics of PETIs, followed by a detailed discussion of PETIs synthesized from monomers featuring noncoplanar configurations (including kink and cardo structures), fluorinated groups, flexible linkages, and liquid crystalline mesogenic structures. The review concludes by addressing current challenges in this research field and exploring potential future directions.

Predicting and screening high-performance polyimide membranes using negative correlation based deep ensemble methods

Polyimide polymer membranes have become critical materials in gas separation and storage applications due to their high selectivity and excellent permeability. However, with over 107 known types of polyimides, relying solely on experimental research means potential high-performance candidates are likely to be overlooked. This study employs a deep learning method optimized by negative correlation ensemble techniques to predict the gas permeability and selectivity of polyimide structures, enabling rapid and efficient material screening. We propose a deep neural network model based on negative correlation deep ensemble methods (DNN-NCL), using Morgan molecular fingerprints as input. The DNN-NCL model achieves an R2 value of approximately 0.95 on the test set, which is a 4% improvement over recent model performance, and effectively mitigates overfitting with a maximum discrepancy of less than 0.03 between the training and test sets. High-throughput screening of over 8 million hypothetical polymers identified hundreds of promising candidates for gas separation membranes, with 14 structures exceeding the Robeson upper bound for CO2/N2 separation. Visualization of high-throughput predictions shows that although the Robeson upper bound was never explicitly used as a model constraint, the majority of predictions are compressed below this limit, demonstrating the deep learning model's ability to reflect real-world physical conditions. Reverse analysis of model predictions using SHAP analysis achieved interpretability of the deep learning model's predictions and identified three key functional groups deemed important by the deep neural network for gas permeability: carbonyl, thiophene, and ester groups. This established a bridge between the structure and properties of polyimide materials. Additionally, we confirmed that two polyimide structures predicted by the model to have excellent CO2/N2 selectivity, namely 6-methylpyrimidin-5-amine and 1,4,5,6-tetrahydropyrimidin-2-amine, have been experimentally validated in previous studies. This research demonstrates the feasibility of using deep learning methods to explore the vast chemical space of polyimides, providing a powerful tool for discovering high-performance gas separation membranes.

Tailoring the Micropore Structure of 6FDA-Based Network Polyimide Membranes for Advanced Gas Separation by Decarboxylation

The 6FDA-based network PI has attracted significant attention for gas separation. A facile strategy to tailor the micropore structure within the network PI membrane prepared by the in situ crosslinking method is extremely significant for achieving an advanced gas separation performance. In this work, the 4,4'-diamino-2,2'-biphenyldicarboxylic acid (DCB) or 3,5-diaminobenzoic acid (DABA) comonomer was incorporated into the 6FDA-TAPA network polyimide (PI) precursor via copolymerization. The molar content and the type of carboxylic-functionalized diamine were varied in order to easily tune the resulting network PI precursor structure. Then, these network PIs containing carboxyl groups underwent further decarboxylation crosslinking during the following heat treatment. Properties involving thermal stabilities, solubility, d-spacing, microporosity, and mechanical properties were investigated. Due to the decarboxylation crosslinking, the d-spacing and the BET surface areas of the thermally treated membranes were increased. Moreover, the content of DCB (or DABA) played a key role in determining the overall gas separation performance of the thermally treated membranes. For instance, after the heating treatment at 450 °C, 6FDA-DCB:TAPA (3:2) showed a large increment of about ~532% for CO₂ gas permeability (~266.6 Barrer) coupled with a decent CO₂/N₂ selectivity~23.6. This study demonstrates that incorporating the carboxyl-containing functional unit into the PI backbone to induce decarboxylation offers a practical approach with which to tailor the micropore structure and corresponding gas transport properties of 6FDA-based network PIs prepared by the in situ crosslinking method.