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A Pd-catalyzed regioselective H/D exchange at the α-position of pyridines was achieved by employing secondary phosphine oxide as an internal base. The proposed five-membered structure enabled the reaction to overcome its conventional ortho-directing feature, allowing the efficient deuteration of pyridines and quinolines at adjacent sites of N-atoms. [ABSTRACT FROM AUTHOR]

Three-dimensional swirling flame flow fields are often limited by factors such as system complexity and operational difficulty, resulting in relatively low achievable spatial resolution in experimental measurements. Providing high-quality visual data is crucial for optimizing the design of combustion chambers. This paper proposes a three-dimensional high-sampling super-resolution reconstruction method based on a physically consistent diffusion model to enhance combustion diagnosis capabilities. When basic diffusion models are used for super-resolution reconstruction, they may introduce artifacts or blurring. This can disrupt the inherent physical connections among flames, adversely affecting the reconstruction of flame details. Therefore, we have introduced a physically consistent encoder designed to process flame swirling data. This encoder allows the model to delve deeper into the intrinsic flow structure of the flame data, capturing flame resolution features across various scales and levels. It improves the accuracy of texture detail reconstruction in areas of intense combustion. During the training process, we have incorporated structural similarity loss into the loss function to assist the model in generating detailed and consistent edge combustion feature within the flame flow structure. These methods ensuring high fidelity and visual quality in the reconstructed flame. With the total voxel number 8× and 64× super-resolution tasks of the three-dimensional temperature fields of the swirling flames, the experimental results have shown that the method not only yields higher peak signal-to-noise ratio (PSNR) values and lower mean absolute error (MAE) compared to the baseline methods but also results in a more realistic visual representation of flame details. [ABSTRACT FROM AUTHOR]

This article presents a spatiotemporal super-resolution (SR) reconstruction model for two common flame types, a swirling and then a jet flame, using double generative adversarial network (GAN) architectures. The approach develops two sets of generator and discriminator networks to learn topographic and temporal features and infer high spatiotemporal resolution turbulent flame structure from supplied low-resolution counterparts at two time points. In this work, numerically simulated 3D turbulent swirling and jet flame structures were used as training data to update the model parameters of the GAN networks. The effectiveness of our model was then thoroughly evaluated in comparison to other traditional interpolation methods. An upscaling factor of 2 in space, which corresponded to an 8-fold increase in the total voxel number and a double time frame acceleration, was used to verify the model's ability on a swirling flame. The results demonstrate that the assessment metrics, peak signal-to-noise ratio (PSNR), overall error (ER), and structural similarity index (SSIM), with average values of 35.27 dB, 1.7%, and 0.985, respectively, in the spatiotemporal SR results, can reach acceptable accuracy. As a second verification to highlight the present model's potential universal applicability to flame data of diverse types and shapes, we applied the model to a turbulent jet flame and had equal success. This work provides a different method for acquiring high-resolution 3D structure and further boosting repeat rate, demonstrating the potential of deep learning technology for combustion diagnosis. [ABSTRACT FROM AUTHOR]