RESUMO
Alkaline anion exchange membrane (AEM)-based fuel cells (AEMFCs) and water electrolyzers (AEMWEs) are vital for enabling the efficient and large-scale utilization of hydrogen energy. However, the performance of such energy devices is impeded by the relatively low conductivity of AEMs. The conventional trial-and-error approach to designing membrane structures has proven to be both inefficient and costly. To address this challenge, a fully connected neural network (FCNN) model is developed based on acid-catalyzed AEMs to analyze the relationship between structure and conductivity among 180,000 AEM variations. Under machine learning guidance, anilinium cation-type membranes are designed and synthesized. Molecular dynamics simulations and Mulliken charge population analysis validated that the presence of a large anilinium cation domain is a result of the inductive effect of N+ and benzene rings. The interconnected anilinium cation domains facilitated the formation of a continuous ion transport channel within the AEMs. Additionally, the incorporation of the benzyl electron-withdrawing group heightened the inductive effect, leading to high conductivity AEM variant as screened by the machine learning model. Furthermore, based on the highly active and low-cost monomers given by machine learning, the large-scale synthesis of anilinium-based AEMs confirms the potential for commercial applications.
RESUMO
Currently, adeno-associated virus (AAV) is one of the primary gene delivery vectors in gene therapy, facilitating long-term in vivo gene expression. Despite being imperative, it is incredibly challenging to precisely assess AAV particle distribution according to the sedimentation coefficient and identify impurities related to capsid structures. This study performed the systematic methodological validation of quantifying the AAV empty and full capsid ratio. This includes specificity, accuracy, precision, linearity, and parameter variables involving the sedimentation velocity analytical ultracentrifugation (SV-AUC) method. Specifically, SV-AUC differentiated among the empty, partial, full, and high sedimentation coefficient substance (HSCS) AAV particles while evaluating their sedimentation heterogeneity. The intermediate precision analysis of HE (high percentage of empty capsid) and HF (high percentage of full capsid) samples revealed that the specific species percentage, such as empty or full, was more significant than 50%. Moreover, the relative standard deviation (RSD) could be within 5%. Even for empty or partially less than 15%, the RSD could be within 10%. The accuracy recovery rates of empty capsid were between 103.9% and 108.7% across three different mixtures. When the measured percentage of specific species was more significant than 14%, the recovery rate was between 77.9% and 106.6%. Linearity analysis revealed an excellent linear correlation between the empty, partial, and full in the HE samples. The AAV samples with as low as 7.4 × 1011 cp/mL AAV could be accurately quantified with SV-AUC. The parameter variable analyses revealed that variations in cell alignment significantly affected the overall results. Still, the detection wavelength of 235 nm slightly influenced the empty, partial, and full percentages. Minor detection wavelength changes showed no impact on the sedimentation coefficient of these species. However, the temperature affected the measured sedimentation coefficient. These results validated the SV-AUC method to quantify AAV. This study provides solutions to AAV empty and full capsid ratio quantification challenges and the subsequent basis for calibrating the AAV empty capsid system suitability substance. Because of the AAV structure and potential variability complexity in detection, we jointly calibrated empty capsid system suitability substance with three laboratories to accurately detect the quantitative AAV empty and full capsid ratio. The empty capsid system suitability substance could be used as an external reference to measure the performance of the instrument. The results could be compared with multiple QC (quality control) laboratories based on the AAV vector and calibration accuracy. This is crucial for AUC to be used for QC release and promote gene therapy research worldwide.