Constructing FeNiPt@C Trifunctional Catalyst by High Spin-Induced Water Oxidation Activity for Zn-Air Battery and Anion Exchange Membrane Water Electrolyzer.

Developing cost-efficient trifunctional catalysts capable of facilitating hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and oxygen reduction reaction (ORR) activity is essential for the progression of energy devices. Engineering these catalysts to optimize their active sites and integrate them into a cohesive system presents a significant challenge. This study introduces a nanoflower (NFs)-like carbon-encapsulated FeNiPt nanoalloy catalyst (FeNiPt@C NFs), synthesized by substituting Co2+ ions with high-spin Fe2+ ions in Hofmann-type metal-organic framework, followed by carbonization and pickling processes. The FeNiPt@C NFs catalyst, characterized by its nitrogen-doped carbon-encapsulated metal alloy structure and phase-segregated FeNiPt alloy with slight surface oxidization, exhibits excellent trifunctional catalytic performance. This is evidenced by its activities in HER (-25 mV at 10 mA cm-2), ORR (half-wave potential of 0.93 V), and OER (294 mV at 10 mA cm-2), with the enhanced water oxidation activity attributed to the high-spin state of the Fe element. Consequently, the Zn-air battery and anion exchange membrane water electrolyzer assembled by FeNiPt@C NFs catalyst demonstrate remarkable power density (168 mW cm-2) and industrial-scale current density (698 mA cm-2 at 1.85 V), respectively. This innovative integration of multifunctional catalytic sites paves the way for the advancement of sustainable energy systems.

Interfacial engineering of Co5.47N/Mo5N6 nanosheets with rich active sites synergistically accelerates water dissociation kinetics for Pt-like hydrogen evolution.

The development of highly efficient hydrogen evolution electrocatalysts with platinum-like activity requires precise control of active sites through interface engineering strategies. In this study, a heterostructured Co5.47N/Mo5N6 catalyst (CoMoNx) on carbon cloth (CC) was synthesized using a combination of dip-etching and vapor nitridation methods. The rough nanosheet surface of the catalyst with uniformly distributed elements exposes a large active surface area and provides abundant interface sites that serve as additional active sites. The CoMoNx was found to exhibit exceptional hydrogen evolution reaction (HER) activity with a low overpotential of 44 mV at 10 mA cm-2 and exceptional stability of 100 h in 1.0 M KOH. The CoMoNx(-)||RuO2(+) system requires only 1.81 V cell voltage to reach a current density of 200 mA cm-2, surpassing the majority of previously reported electrolyzers. Density functional theory (DFT) calculations reveal that the strong synergy between Co5.47N and Mo5N6 at the interface can significantly reduce the water dissociation energy barrier, thereby improving the kinetics of hydrogen evolution. Furthermore, the rough nanosheet architecture of the CoMoNx catalyst with abundant interstitial spaces and multi-channels enhances charge transport and reaction intermediate transportation, synergistically improving the performance of the HER for water splitting.

Context-Dependent Random Walk Graph Kernels and Tree Pattern Graph Matching Kernels with Applications to Action Recognition.

Graphs are effective tools for modeling complex data. Setting out from two basic substructures, random walks and trees, we propose a new family of context-dependent random walk graph kernels and a new family of tree pattern graph matching kernels. In our context-dependent graph kernels, context information is incorporated into primary random walk groups. A multiple kernel learning algorithm with a proposed l1,2-norm regularization is applied to combine context-dependent graph kernels of different orders. This improves the similarity measurement between graphs. In our tree-pattern graph matching kernel, a quadratic optimization with a sparse constraint is proposed to select the correctly matched tree-pattern groups. This augments the discriminative power of the tree-pattern graph matching. We apply the proposed kernels to human action recognition, where each action is represented by two graphs which record the spatiotemporal relations between local feature vectors. Experimental comparisons with state-of-the-art algorithms on several benchmark datasets demonstrate the effectiveness of the proposed kernels for recognizing human actions. It is shown that our kernel based on tree-pattern groups, which have more complex structures and exploit more local topologies of graphs than random walks, yields more accurate results but requires more runtime than the context-dependent walk graph kernel.

Boiling treatment of ABS and PS plastics for flotation separation.

A new physical method, namely boiling treatment, was developed to aid flotation separation of acrylonitrile-butadiene-styrene (ABS) and polystyrene (PS) plastics. Boiling treatment was shown to be effective in producing a hydrophilic surface on ABS plastic. Fourier Transform Infrared analysis was conducted to investigate the mechanism of boiling treatment of ABS. Surface rearrangement of polymer may be responsible for surface change of boiling treated ABS, and the selective influence of boiling treatment on the floatability of boiling treated plastics may be attributed to the difference in the molecular mobility of polymer chains. The effects of flotation time, frother concentration and particle size on flotation behavior of simple plastic were investigated. Based on flotation behavior of simple plastic, flotation separation of boiling treatment ABS and PS with different particle sizes was achieved efficiently. The purity of ABS and PS was up to 99.78% and 95.80%, respectively; the recovery of ABS and PS was up to 95.81% and 99.82%, respectively. Boiling treatment promotes the industrial application of plastics flotation and facilitates plastic recycling.