Seawater electrolysis for fuels and chemicals production: fundamentals, achievements, and perspectives.

Seawater electrolysis for the production of fuels and chemicals involved in onshore and offshore plants powered by renewable energies offers a promising avenue and unique advantages for energy and environmental sustainability. Nevertheless, seawater electrolysis presents long-term challenges and issues, such as complex composition, potential side reactions, deposition of and poisoning by microorganisms and metal ions, as well as corrosion, thus hindering the rapid development of seawater electrolysis technology. This review focuses on the production of value-added fuels (hydrogen and beyond) and fine chemicals through seawater electrolysis, as a promising step towards sustainable energy development and carbon neutrality. The principle of seawater electrolysis and related challenges are first introduced, and the redox reaction mechanisms of fuels and chemicals are summarized. Strategies for operating anodes and cathodes including the development and application of chloride- and impurity-resistant electrocatalysts/membranes are reviewed. We comprehensively summarize the production of fuels and chemicals (hydrogen, carbon monoxide, sulfur, ammonia, etc.) at the cathode and anode via seawater electrolysis, and propose other potential strategies for co-producing fine chemicals, even sophisticated and electronic chemicals. Seawater electrolysis can drive the oxidation and upgrading of industrial pollutants or natural organics into value-added chemicals or degrade them into harmless substances, which would be meaningful for environmental protection. Finally, the perspective and prospects are outlined to address the challenges and expand the application of seawater electrolysis.

Microscopic-Level Insights into P-O-Induced Strong Electronic Coupling Over Nickel Phosphide with Efficient Benzyl Alcohol Electrooxidation.

Electrooxidation of biomass into fine chemicals coupled with energy-saving hydrogen production for a zero-carbon economy holds great promise. Advanced anode catalysts determine the cell voltage and electrocatalytic efficiency greatly, further the rational design and optimization of their active site coordination remains a challenge. Herein, a phosphorus-oxygen terminals-rich species (Ni2 P-O-300) via an anion-assisted pyrolysis strategy is reported to induce strong electronic coupling and high valence state of active nickel sites over nickel phosphide. This ultimately facilitates the rapid yet in-situ formation of high-valence nickel with a high reaction activity under electrochemical conditions, and exhibits a low potential of 1.33 V vs. RHE at 10 mA cm-2 , exceeding most of reported transition metal-based catalysts. Advanced spectroscopy, theoretical calculations, and experiments reveal that the functional P-O species can induce the favorable local bonding configurations for electronic coupling, promoting the electron transfer from Ni to P and the adsorption of benzyl alcohol (BA). Finally, the hydrogen production efficiency and kinetic constant of BA electrooxidation by Ni2 P-O-300 are increased by 9- and 2.8- fold compared with the phosphorus-oxygen terminals-deficient catalysts (Ni2 P-O-500). This provides an anion-assisted pyrolysis strategy to modulate the electronic environment of the Ni site, enabling a guideline for Ni-based energy/catalysis systems.

Animatable Implicit Neural Representations for Creating Realistic Avatars From Videos.

This paper addresses the challenge of reconstructing an animatable human model from a multi-view video. Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images. However, they represent the deformation field as translational vector field or SE(3) field, which makes the optimization highly under-constrained. Moreover, these representations cannot be explicitly controlled by input motions. Instead, we introduce blend weight fields to produce the deformation fields. Based on the skeleton-driven deformation, blend weight fields are used with 3D human skeletons to generate observation-to-canonical and canonical-to-observation correspondences. Since 3D human skeletons are more observable, they can regularize the learning of deformation fields. Moreover, the blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model. To improve the quality of human modeling, we further represent the human geometry as a signed distance field in the canonical space. Additionally, a neural point displacement field is introduced to enhance the capability of the blend weight field on modeling detailed human motions. Experiments show that our approach significantly outperforms recent human modeling methods.

Fast and Robust Multi-Person 3D Pose Estimation and Tracking From Multiple Views.

This paper addresses the problem of reconstructing 3D poses of multiple people from a few calibrated camera views. The main challenge of this problem is to find the cross-view correspondences among noisy and incomplete 2D pose predictions. Most previous methods address this challenge by directly reasoning in 3D using a pictorial structure model, which is inefficient due to the huge state space. We propose a fast and robust approach to solve this problem. Our key idea is to use a multi-way matching algorithm to cluster the detected 2D poses in all views. Each resulting cluster encodes 2D poses of the same person across different views and consistent correspondences across the keypoints, from which the 3D pose of each person can be effectively inferred. The proposed convex optimization based multi-way matching algorithm is efficient and robust against missing and false detections, without knowing the number of people in the scene. Moreover, we propose to combine geometric and appearance cues for cross-view matching. Finally, an efficient tracking method is proposed to track the detected 3D poses across the multi-view video. The proposed approach achieves the state-of-the-art performance on the Campus and Shelf datasets, while being efficient for real-time applications.