No abstract

Two nitrogen-rich porous organic polymers (POPs) were prepared via Schiff base chemistry. Carbonization of these POPs results in porous carbon nanohybrids which exhibit excellent catalytic activity toward the oxygen reduction reaction (ORR).

No abstract

Exploring high-performance electrocatalysts is of great importance for developing renewable energy conversion systems.

The oxygen reduction reaction (ORR), as one of the most critical but promising reactions for energy conversion, has attracted increasing research interest. Recent reports have evidenced that carbonization of heteroatoms doped porous organic polymers (POPs) is an effective approach toward highly efficient ORR electrocatalysts. We herein report a versatile ternary copolymerization strategy to synthesize stable POPs gel with tunable doping of heteroatoms (N, S, F) and Fe species, leading to significant enhancement in surface area and porosity. Carbonization of these POPs afford efficient ORR electrocatalyst with optimized composition, hierarchical porous structure and prominent catalytic activities in both alkaline and neutral conditions. The optimized catalyst (TF‐C‐900) exhibited an onset potential (Eonset) of 1.01 V and half‐wave potential (E1/2) of 0.88 V in 0.1 M KOH solution. These performance metrics are even comparable to those of the Pt/C (0.99 and 0.85). In addition, the TF‐C‐900 also showed superior stability and advantage of methanol tolerance, enabling them to be a competitive cathode electrocatalysts for alkaline fuel cell.

Hyperspectral images (HSIs) have high spatial resolution and spectral resolution, and using HSI as a change detection (CD) data source is crucial for detecting surface changes. However, there is a large amount of real noise in HSIs, and most deep learning-based CD methods require a large number of ground-truth labels for training, which is difficult and expensive to label manually. To reduce the dependence of CD on ground-truth labels and weaken the interference of noise on CD in HSIs, in this paper we propose a hyperspectral image change detection framework with self-supervised contrastive learning pre-trained model (CDSCL). CDSCL consists of two parts: self-supervised contrastive learning pre-trained model and CD classification network. The main contributions of this article are as follows: 1) a data augmentation strategy based on Gaussian noise is proposed to improve the ability of the model to extract variation information from HSIs with different random Gaussian noises; 2) based on Information Bottleneck (IB) theory, a progressive feature extraction module (PFEM) is developed to remove redundant or irrelevant details in changing information spectrum; 3) a contrastive loss function based on Pearson correlation coefficient and negative cosine correlation is designed to make the features extracted by the two branches of the siamese network close to each other. Experimental results on four real hyperspectral datasets demonstrate that the CD performance of CDSCL outperforms the most representative CD methods.

Change detection (CD), aims to detect the changing area of the same scene at different times, which is an important application of remote sensing images. As the key data source of CD, hyperspectral image (HSI) is widely used in CD technology because of its rich spectral-spatial information. However, how to mine the multi-level spatial information of dual-temporal hyperspectral images (HSIs) and focus on the features of the pixels to be classified individually remains a problem in the spatial attention mechanism (SAM). To make full use of the spectral-spatial information of HSIs, in this paper we propose a CNN framework with compact band weighting and multiscale spatial attention (CBW-MSSANet) for HSI pixel-level CD. The main contributions of this article are as follows: 1) a new method of pseudo-label training sample selection based on kmeans (KM) centroid distance is designed; 2) apply the compact band weighting (CBW) module to HSI CD to take full advantage of the spectral information of HSIs; 3) a multi-scale spatial attention (MSSA) module is developed for pixel-level CD, which can mine multi-level spatial information and pay more attention to the features of the pixels to be classified, and combine the spatial information of adjacent pixels to make it more conducive to pixel-level CD. Experimental results on four real HSI datasets demonstrated that the performance of MSSA surpasses the classical single-scale SAM, and CBW-MSSANet is superior to some representative CD methods.

It is of vital importance to boost the intrinsic activity and augment the active sites of expensive and scarce platinum-based catalysts for advancing a variety of electrochemical energy applications. We herein report a mild electrochemical bottom-up approach to deposit ultrafine, but stable, Pt8Ag4 alloy clusters on carbon nanotubes (CNTs) by elaborately designing bimetallic organic cluster precursors with four silver and eight platinum atoms coordinated with µ,σ-bridged ethynylpyridine ligands, i.e., [Ag4(C24H16N4Pt)8(BF4)4]. The Pt8Ag4 cluster/CNT hybrids present impressively high platinum mass activity that is threefold that of commercial Pt/C toward the hydrogen evolution reaction, as a result of the cooperative contributions from the Ag atoms that enhance the intrinsic activity and the CNT supports that increase the activity sites. The present work affords an attractive avenue for engineering and stabilizing Pt-based nanoclusters at the atomic level and represents a promising strategy for the development of high-efficiency and durable electrocatalysts.