Computational Study of Carbon Dioxide Capture by Tertiary Amines.

The reaction mechanisms and corresponding structure-activity relationships of tertiary amines with respect to CO2 capture have been investigated using density functional theory (DFT) calculations. The reaction mechanism for CO2 capture via base-catalyzed hydration to form bicarbonate is proposed to proceed in a single step involving proton transfer and the formation of a carbon-oxygen bond. Based on the height of the reaction barriers, we suggest that amines containing side chains with the ethyl group, along with a single hydroxyl group, and cyclic structures, are especially active for CO2 capture. The activation barrier is shown to be a descriptor for predicting the experimental CO2 loading values. To enhance the prediction accuracy for CO2 loading, we employ the sure-independence screening and sparsifying operator (SISSO) method, which can scan a large pool of mathematical terms stemming from combining DFT-derived descriptors to select the superior ones. Thus, we can predict the CO2 loading with acceptable accuracy from the obtained mathematical expression. Since the computational workload of applying this expression is negligible, this facilitates high-throughput screening and accelerates the design of tertiary amines for CO2 capture.

Feature-Assisted Machine Learning for Predicting Band Gaps of Binary Semiconductors.

The band gap is a key parameter in semiconductor materials that is essential for advancing optoelectronic device development. Accurately predicting band gaps of materials at low cost is a significant challenge in materials science. Although many machine learning (ML) models for band gap prediction already exist, they often suffer from low interpretability and lack theoretical support from a physical perspective. In this study, we address these challenges by using a combination of traditional ML algorithms and the 'white-box' sure independence screening and sparsifying operator (SISSO) approach. Specifically, we enhance the interpretability and accuracy of band gap predictions for binary semiconductors by integrating the importance rankings of support vector regression (SVR), random forests (RF), and gradient boosting decision trees (GBDT) with SISSO models. Our model uses only the intrinsic features of the constituent elements and their band gaps calculated using the Perdew-Burke-Ernzerhof method, significantly reducing computational demands. We have applied our model to predict the band gaps of 1208 theoretically stable binary compounds. Importantly, the model highlights the critical role of electronegativity in determining material band gaps. This insight not only enriches our understanding of the physical principles underlying band gap prediction but also underscores the potential of our approach in guiding the synthesis of new and valuable semiconductor materials.

Wall Density-Controlled Thermal Conductive and Mechanical Properties of Three-Dimensional Vertically Aligned Boron Nitride Network-Based Polymeric Composites.

Polymeric composites with good thermal conductive and improved mechanical properties are in high demand in the thermal management materials. Construction of a three-dimensional (3D) structure has been proved to be an effective method to obtain polymeric composites with improved through-plane thermal conductivity (TC) for efficient thermal management of electronics. However, the TC enhancement of the obtained polymeric composites is limited, mainly due to poor control of the 3D thermal conductive network. Additionally, achieving high thermal conductive properties and enhanced mechanical properties simultaneously is of great challenge for polymeric composites. In this work, a 3D boron nitride framework (BNF) with a well-defined vertically aligned open structure and designed wall density fabricated by a unidirectional freezing technique was applied. The as-prepared BNF/polyethylene glycol (PBNF) composites exhibit enhanced through-plane TC, excellent thermal transfer capability (ΔTmax = 34 °C), and improved mechanical properties (Young's modulus enhancement up to 356%) simultaneously, making it attractive to thermal management applications. Strong correlation between the TC and mechanical properties of the PBNF composites and the wall density of the BNF scaffolds was found, providing opportunities to tune the TC and mechanical properties through the controlling of wall density. Furthermore, the models between TC and Young's modulus of PBNF composites were established by using the data-driven method "sure independence screening and sparsifying operator", which enables us to predict TC and Young's modulus of the polymeric composites for designing promising composite materials. The design principles and fabrication strategies proposed in this work could be important for developing advanced composite materials.