Boosting Electrocatalytic Carbon Dioxide Reduction via Self-Relaxation of Asymmetric Coordination in Fe-Based Single Atom Catalyst.

Addressing the limitations arising from the consistent catalytic behavior observed for various intermediates during the electrochemical carbon dioxide reduction reaction (CO RR) poses a significant challenge in the optimization of catalytic activity. In this study, we aimed to address this challenge by constructing an asymmetric coordination Fe single atom catalyst (SCA) with a dynamically evolved structure. Our catalyst, consisting of a Fe atom coordinated with one S atom and three N atoms (Fe-S N ), exhibited exceptional selectivity (CO Faradaic efficiency of 99.

Atomic Dispersed Hetero-Pairs for Enhanced Electrocatalytic CO Reduction.

Electrochemical carbon dioxide reduction reaction (CORR) involves a variety of intermediates with highly correlated reaction and ad-desorption energies, hindering optimization of the catalytic activity. For example, increasing the binding of the *COOH to the active site will generally increase the *CO desorption energy. Breaking this relationship may be expected to dramatically improve the intrinsic activity of CORR, but remains an unsolved challenge.

A cognitive inquiry into similarities and differences between translation and paraphrase: Evidence from eye movement data.

Intralingual translation has long been peripheral to empirical studies of translation. Considering its many similarities with interlingual translation, also described as translation proper, we adopted eye-tracking technology to investigate the cognitive process during translation and paraphrase, an exemplification of intralingual translation. Twenty-four postgraduate students were required to perform four types of tasks (Chinese paraphrase, English-Chinese translation, English paraphrase, Chinese-English translation) for source texts (ST) of different genres.

A Modified Brain MR Image Segmentation and Bias Field Estimation Model Based on Local and Global Information.

Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias) in brain magnetic resonance (MR) image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image.