Novel amino-modified bamboo-derived biochar-supported nano-zero-valent iron (AMBBC-nZVI) composite for efficient Cr(VI) removal from aqueous solution.

Biochar-supported nano-zero-valent iron (BC-nZVI) composites have been extensively investigated for the treatment of Cr(VI)-containing wastewater. However, the inherent oxygen-containing groups with negative charges on BC exhibit electrostatic repulsion of the electronegative Cr(VI) species, limiting Cr(VI) removal. To overcome this limitation, this study prepared and used amino-modified bamboo-derived BC (AMBBC) as a supporting matrix to synthesize a novel AMBBC-nZVI composite. The amino groups (-NH2) on AMBBC were easily protonated and transformed into positively charged ions (-NH3+), which favored the attraction of Cr(VI) to AMBBC-nZVI, enhancing Cr(VI) removal. The experimental results demonstrated that the Cr(VI) removal efficiency of AMBBC-nZVI was 95.3%, and that of BBC-nZVI was 83.8% under the same conditions. The removal of Cr(VI) by AMBBC-nZVI followed the pseudo-second-order kinetic model and Langmuir isotherm model and was found to be a monolayer chemisorption process. Thermodynamic analysis revealed that the Cr(VI) removal process was spontaneous and endothermic. The mechanism analysis of Cr(VI) removal indicated that under an acidic condition, the -NH3+ groups on AMBBC adsorbed the electronegative Cr(VI) species via electrostatic interaction, promoting the attachment of Cr(VI) on AMBBC-nZVI; the adsorbed Cr(VI) was then reduced to Cr(III) by Fe0 and Fe(II), accompanied by the formation of Fe(III); moreover, AMBBC allowed the electron shuttle of nZVI to reduce Cr(VI); finally, the Cr(III) and Fe(III) species deposited on the surface of AMBBC-nZVI as Cr(III)-Fe(III) hydroxide coprecipitates.

Multi-task recurrent convolutional network with correlation loss for surgical video analysis.

Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis as well as very essential components in various applications in modern operating rooms. While these two analysis tasks are highly correlated in clinical practice as the surgical process is typically well-defined, most previous methods tackled them separately, without making full use of their relatedness. In this paper, we present a novel method by developing a multi-task recurrent convolutional network with correlation loss (MTRCNet-CL) to exploit their relatedness to simultaneously boost the performance of both tasks. Specifically, our proposed MTRCNet-CL model has an end-to-end architecture with two branches, which share earlier feature encoders to extract general visual features while holding respective higher layers targeting for specific tasks. Given that temporal information is crucial for phase recognition, long-short term memory (LSTM) is explored to model the sequential dependencies in the phase recognition branch. More importantly, a novel and effective correlation loss is designed to model the relatedness between tool presence and phase identification of each video frame, by minimizing the divergence of predictions from the two branches. Mutually leveraging both low-level feature sharing and high-level prediction correlating, our MTRCNet-CL method can encourage the interactions between the two tasks to a large extent, and hence can bring about benefits to each other. Extensive experiments on a large surgical video dataset (Cholec80) demonstrate outstanding performance of our proposed method, consistently exceeding the state-of-the-art methods by a large margin, e.g., 89.1% v.s. 81.0% for the mAP in tool presence detection and 87.4% v.s. 84.5% for F1 score in phase recognition.