Synthesis of N,N'-alkylidene bisamides and Suzuki-Miyaura coupling reaction derivatives with Pd organometallic catalyst anchored to channels of mesoporous silica MCM-41.

At first, an organometallic catalyst namely, Pd-DPyE@MCM-41@MNP was prepared through magnetic (Fe3O4) nanoparticles-doped into channels of mesoporous silica MCM-41 and then, anchoring a novel complex composed of di(4-pyridyl)ethylene and palladium on the inner surface of the support. This immobilized catalyst was successfully identified via VSM, ICP-OES, TEM, FTIR, TGA, SEM, BET, XRD, EDX and elemental mapping analyses. After that, it was used as a versatile, heterogeneous, and magnetically reproducible catalyst in the generation of N,N'-alkylidene bisamides (1a-13a, 8-20 min, 90-98%, 50 °C, solvent-free) and Suzuki-Miyaura coupling (SMC) reaction derivatives (1b-26b, 10-140 min, 86-98%, 60 °C, PEG-400). The VSM plot of Pd-DPyE@MCM-41@MNP displays that this nanocatalyst can be easily recycled by applying an external magnetic field. In both synthetic paths, this nanocatalyst was reused at least seven times without palladium leaching and significantly reducing its catalytic performance. Also, stability and heterogeneous nature of catalyst were approved via ICP-OES technique and hot filtration test.

Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems.

Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this research paper, we investigate the efficacy of employing an Autoregressive Deep Neural Network (ARDNN) algorithm to predict the geomechanical bearing capacity in CCS systems through shear wave velocity prediction as an index for bearing capacity evaluation of deep rock formations. The model utilizes a dataset consisting of 23,000 data points to train and test the ARDNN algorithm. Its scalability, use of deep learning techniques, automatic feature extraction, adaptability to changes in data, and versatility in various prediction tasks make it an attractive option for accurate predictions. The results demonstrate exceptional performance, as evidenced by an R-squared value of 0.9906 and a mean squared error of 0.0438 for the test data compared to the measured data. This research has significant practical implications for effectively predicting geomechanical stability in CCS systems, thus mitigating potential risks associated with their operation.