Ni-catalyzed carbon–carbon bonds cleavage of mixed polyolefin plastics waste

The inert carbon–carbon(C–C) bonds cleavage is a main bottleneck in the chemical upcycling of recalcitrant polyolefin plastics waste. Here we develop an efficient strategy to catalyze the complete cleavage of C–C bonds in mixed polyolefin plastics over non-noble metal catalysts under mild conditions. The nickelbased catalyst involving Ni_(2)Al_(3) phase enables the direct transformation of mixed polyolefin plastics into natural gas, and the gas carbon yield reaches up to 89.6%. Reaction pathway investigation reveals that natural gas comes from the stepwise catalytic cleavage of C–C bonds in polypropylene, and the catalyst prefers catalytic cleavage of terminal C–C bond in the side-chain with the low energy barrier.Additionally, our developed approach is evaluated by the technical economic analysis for an economically competitive production process.

Ordered cone-structured tin directly grown on carbon paper as efficient electrocatalyst for CO_(2) electrochemical reduction to formate

The conversion of carbon dioxide to chemicals by the electrochemical reactions(ERC)is an efficient solution to the current energy crisis and excess CO_(2) emissions.It is still a great challenge and of significance to synthesize a highly selective,efficient,and non-noble metal electrocatalyst that facilitates the ERC reaction.A novel triton X-100(C_(14)H_(22)O(C_(2)H_(4)O)n)assisted electrodeposition method was developed to synthesize the ordered cone-structured tin(OCSn)electrocatalysts with controllable morphology and structure.The results suggest that Triton X-100 plays an important role in directing the structure of the Sn electrocatalysts during the electrodeposition process.The OCSn synthesized at 60 m A cm^(-2) achieves the best performances.It selectively catalyzes the ERC on the onset potential about 110 m V lower than Sn synthesized without Triton X-100.In 0.5 M Na HCO_(3),high faradaic efficiency(92%)for formate product on OCSn has been achieved.More prominently,the catalyst presents excellent stability,showing no performance deterioration during 30 h electrolysis.This work provides an efficient,green,and scalable synthesis method of the electrocatalyst for CO_(2) reduction to formate.

Adversarial Neural Collaborative Filtering with Embedding Dimension Correlations

Recently,convolutional neural networks(CNNs)have achieved excellent performance for the recommendation system by extracting deep features and building collaborative filtering models.However,CNNs have been verified susceptible to adversarial examples.This is because adversarial samples are subtle non-random disturbances,which indicates that machine learning models produce incorrect outputs.Therefore,we propose a novel model of Adversarial Neural Collaborative Filtering with Embedding Dimension Correlations,named ANCF in short,to address the adversarial problem of CNN-based recommendation system.In particular,the proposed ANCF model adopts the matrix factorization to train the adversarial personalized ranking in the prediction layer.This is because matrix factorization supposes that the linear interaction of the latent factors,which are captured between the user and the item,can describe the observable feedback,thus the proposed ANCF model can learn more complicated representation of their latent factors to improve the performance of recommendation.In addition,the ANCF model utilizes the outer product instead of the inner product or concatenation to learn explicitly pairwise embedding dimensional correlations and obtain the interaction map from which CNNs can utilize its strengths to learn high-order correlations.As a result,the proposed ANCF model can improve the robustness performance by the adversarial personalized ranking,and obtain more information by encoding correlations between different embedding layers.Experimental results carried out on three public datasets demonstrate that the ANCF model outperforms other existing recommendation models.

An Underwater Biomimetic Robot that can Swim,Bipedal Walk and Grasp

In developing and exploring extreme and harsh underwater environments,underwater robots can effectively replace humans to complete tasks.To meet the requirements of underwater flexible motion and comprehensive subsea operation,a novel octopus-inspired robot with eight soft limbs was designed and developed.This robot possesses the capabilities of underwater bipedal walking,multi-arm swimming,and grasping objects.To closely interact with the underwater seabed environment and minimize disturbance,the robot employs a cable-driven flexible arm for its walking in underwater floor through a bipedal walking mode.The multi-arm swimming offers a means of three-dimensional spatial movement,allowing the robot to swiftly explore and navigate over large areas,thereby enhancing its flexibility.Furthermore,the robot’s walking arm enables it to grasp and transport objects underwater,thereby enhancing its practicality in underwater environments.A simplified motion models and gait generation strategies were proposed for two modes of robot locomotion:swimming and walking,inspired by the movement characteristics of octopus-inspired multi-arm swimming and bipedal walking.Through experimental verification,the robot’s average speed of underwater bipedal walking reaches 7.26 cm/s,while the horizontal movement speed for multi-arm swimming is 8.6 cm/s.