Cobalt/Iron Bimetallic Biochar Composites for Lead(II) Adsorption: Mechanism and Remediation Performance.

The performance of nano-zero-valent iron for heavy metal remediation can be enhanced via incorporation into bimetallic carbon composites. However, few economical and green approaches are available for preparing bimetallic composite materials. In this study, novel Co/Fe bimetallic biochar composites (BC@Co/Fe-X, where X = 5 or 10 represents the CoCl2 concentration of 0.05 or 0.1 mol L-1) were prepared for the adsorption of Pb2+. The effect of the concentration of cross-linked metal ions on Pb2+ adsorption was investigated, with the composite prepared using 0.05 mol L-1 Co2+ (BC@Co/Fe-5) exhibiting the highest adsorption performance. Various factors, including the adsorption period, Pb2+ concentration, and pH, affected the adsorption of Pb2+ by BC@Co/Fe-5. Further characterisation of BC@Co/Fe-5 before and after Pb2+ adsorption using methods such as X-ray diffraction and X-ray photoelectron spectroscopy suggested that the Pb2+ adsorption mechanism involved (i) Pb2+ reduction to Pb0 by Co/Fe, (ii) Co/Fe corrosion to generate Fe2+ and fix Pb2+ in the form of PbO, and (iii) Pb2+ adsorption by Co/Fe biochar. Notably, BC@Co/Fe-5 exhibited excellent remediation performance in simulated Pb2+-contaminated water and soil with good recyclability.

Stable Median Centre Clustering for Unsupervised Domain Adaptation Person Re-Identification.

The current unsupervised domain adaptation person re-identification (re-ID) method aims to solve the domain shift problem and applies prior knowledge learned from labelled data in the source domain to unlabelled data in the target domain for person re-ID. At present, the unsupervised domain adaptation person re-ID method based on pseudolabels has obtained state-of-the-art performance. This method obtains pseudolabels via a clustering algorithm and uses these pseudolabels to optimize a CNN model. Although it achieves optimal performance, the model cannot be further optimized due to the existence of noisy labels in the clustering process. In this paper, we propose a stable median centre clustering (SMCC) for the unsupervised domain adaptation person re-ID method. SMCC adaptively mines credible samples for optimization purposes and reduces the impact of label noise and outliers on training to improve the performance of the resulting model. In particular, we use the intracluster distance confidence measure of the sample and its K-reciprocal nearest neighbour cluster proportion in the clustering process to select credible samples and assign different weights according to the intracluster sample distance confidence of samples to measure the distances between different clusters, thereby making the clustering results more robust. The experiments show that our SMCC method can select credible and stable samples for training and improve performance of the unsupervised domain adaptation model. Our code is available at https://github.com/sunburst792/SMCC-method/tree/master.