Prediction and Interpretability Study of the Glass Transition Temperature of Polyimide Based on Machine Learning with Quantitative Structure-Property Relationship (Tg-QSPR).

The glass transition temperature (Tg) is a crucial characteristic of polyimides (PIs). Developing a Tg predictive model using machine learning methodologies can facilitate the design of PI structures and expedite the development process. In this investigation, a data set comprising 1257 PIs was assembled, with Tg values determined using differential scanning calorimetry. 210 molecular descriptors were computed using RDKit, and subsequently, six distinct feature selection methodologies were employed to refine the descriptor set. Quantitative structure-property relationship models targeting Tg (Tg-QSPR) were then constructed using five ensemble learning algorithms and one deep learning algorithm. These models exhibited high predictive accuracy and robustness, with the CATBoost model demonstrating the highest accuracy, achieving a coefficient of determination of 0.823 for the test set, a mean absolute error of 20.1 °C, and a root-mean-square error of 29.0 °C. The study identified the NumRotatableBonds descriptor as particularly influential on Tg, showing a negative correlation with the property. Additionally, the model's accuracy was validated using ten new PI films not included in the original data set, resulting in absolute errors ranging from 2.5 to 26.9 °C and absolute percentage error rates of 1.0-12.8%. These findings underscore the importance of utilizing extensive and diverse data sets for predictive modeling to enhance accuracy and stability. Furthermore, exploring the interpretability of the model and experimentally validating newly synthesized PIs have augmented the practical utility of the model. Under the guidance of the Tg-QSPR models, it will be possible to accelerate the performance prediction and structural design of PIs in the future.

Recent Study Advances in Flexible Sensors Based on Polyimides.

With the demand for healthy life and the great advancement of flexible electronics, flexible sensors are playing an irreplaceably important role in healthcare monitoring, wearable devices, clinic treatment, and so on. In particular, the design and application of polyimide (PI)-based sensors are emerging swiftly. However, the tremendous potential of PI in sensors is not deeply understood. This review focuses on recent studies in advanced applications of PI in flexible sensors, including PI nanofibers prepared by electrospinning as flexible substrates, PI aerogels as friction layers in triboelectric nanogenerator (TENG), PI films as sensitive layers based on fiber Bragg grating (FBG) in relative humidity (RH) sensors, photosensitive PI (PSPI) as sacrificial layers, and more. The simple laser-induced graphene (LIG) technique is also introduced in the application of PI graphitization to graphene. Finally, the prospect of PIs in the field of electronics is proposed in the review.

Case Report: Rare Pulmonary Sclerosing Pneumocytoma: Large, Multiple, Metastatic, and Fatal.

Pulmonary sclerosing pneumocytoma (PSP) is a rare benign or low-grade malignant tumor, but it has the potential to present with multiple lesions, lymph node metastasis, extra-pulmonary metastasis, recurrence and even cause death. Herein, a case of PSP that was huge, presented with multiple lesions and had lymph node as well as extrapulmonary metastases (liver, abdominal cavity, bones) is reported for the first time. This patient was also the first one to die of respiratory and circulatory failure caused by the PSP tumor and its metastases which compressed the mediastinal tissue.