Controlling factors and sources-specific ecological risks associated with toxic metals in core sediments from cascade reservoirs in Southwest China.

Toxic metals (TMs) in reservoir sediments pose significant risks to ecosystem security and human safety, yet their presence in the cascade reservoirs of the Lancang River remains understudied. This research examined TMs in core sediments from the Manwan (MW) and Dachaoshan (DCS) cascade reservoirs, aiming to elucidate contamination characteristics, controlling factors, and source-specific ecological risks. The study revealed that the concentrations of As, Cd, Cr, Cu, Hg, Ni, and Zn in the MW Reservoir (37.3, 0.54, 95.1, 44.0, 0.09, 44.8, and 135.7 mg/kg) were notably higher compared to the DCS Reservoir (14.6, 0.30, 82.6, 31.0, 0.08, 36.6, and 108.7 mg/kg). While both reservoirs demonstrated elevated contamination levels of Cd and Hg, the MW Reservoir also exhibited high levels of As, whereas the DCS Reservoir showed relatively high levels of Pb. Mining activities in upstream metal deposits significantly correlated Cd, Hg, and Zn in the MW Reservoir with sulfur. In both reservoir sediments, Cr and Ni displayed a greater affinity for iron oxides, while As, Cd, Cu, Hg, and Zn showed more affinity with manganese oxides. Ecological risk index (RI) values in half of the sediments from the MW Reservoir ranged from 300 to 600, denoting a significant ecological risk. Conversely, in the DCS Reservoir, 93.3 % of the sediments exhibited RI values between 150 and 300, signifying a moderate ecological risk. Source-oriented ecological risks highlighted the need for particular attention to Cd from anthropogenic sources in the MW Reservoir. These findings underscore the importance of implementing measures for TM contamination prevention and control, contributing to strategic planning for sustainable water resource management in the Lancang-Mekong River.

Bio-inspired affordance learning for 6-DoF robotic grasping: A transformer-based global feature encoding approach.

The 6-Degree-of-Freedom (6-DoF) robotic grasping is a fundamental task in robot manipulation, aimed at detecting graspable points and corresponding parameters in a 3D space, i.e affordance learning, and then a robot executes grasp actions with the detected affordances. Existing research works on affordance learning predominantly focus on learning local features directly for each grid in a voxel scene or each point in a point cloud scene, subsequently filtering the most promising candidate for execution. Contrarily, cognitive models of grasping highlight the significance of global descriptors, such as size, shape, and orientation, in grasping. These global descriptors indicate a grasp path closely tied to actions. Inspired by this, we propose a novel bio-inspired neural network that explicitly incorporates global feature encoding. In particular, our method utilizes a Truncated Signed Distance Function (TSDF) as input, and employs the recently proposed Transformer model to encode the global features of a scene directly. With the effective global representation, we then use deconvolution modules to decode multiple local features to generate graspable candidates. In addition, to integrate global and local features, we propose using a skip-connection module to merge lower-layer global features with higher-layer local features. Our approach, when tested on a recently proposed pile and packed grasping dataset for a decluttering task, surpassed state-of-the-art local feature learning methods by approximately 5% in terms of success and declutter rates. We also evaluated its running time and generalization ability, further demonstrating its superiority. We deployed our model on a Franka Panda robot arm, with real-world results aligning well with simulation data. This underscores our approach's effectiveness for generalization and real-world applications.

Droplet-Based Preparation of ZnO-nanostructure Array for Microfluidic Fluorescence Biodetection.

Nanostructure-enhanced biodetection is widely used for early diagnosis and treatment, which plays an essential role in improving the cure rates of cancer patients. ZnO nanostructure-based fluorescence immunoassay has been demonstrated to enable effective and sensitive detection of cancer biomarkers for their excellent biocompatibility, high electrical point, and unique fluorescence enhancement properties. Further optimization of such fluorescence detection technology is still in demand to meet the requirements of highly sensitive, multiplex detection, and user-friendly devices. Droplet microfluidics is a promising platform for high-throughput analysis of biological assays, and they have been intensively used in analytical chemistry and synthesis of nanoparticles. Here, we propose a simple droplet chip, where a static droplet array was successfully obtained for in situ growth of ZnO nanostructures with varied diameters by changing the entire growth time and replenishment interval. This device provides a novel and alternative approach for patterned growth of ZnO nanostructures and understanding the growth condition of ZnO nanostructures in static droplet, which offers some guidance toward the design of multiple fluorescence amplification platforms potentially for biosensing. As a demonstration, we used the patterned grown ZnO nanostructures for multiple detection of cancer biomarkers, achieving a low limit of detection as low as 138 fg/mL in the human α-fetoprotein assay and 218 fg/mL in the carcinoembryonic antigen assay with a large dynamic range of 8 orders. These results suggest that such multifunctional microfluidic devices may be useful tools for efficient fluorescence diagnostic assays.

Flow-induced fabrication of ZnO nanostructures in pillar-arrayed microchannels.

The emergence of microfluidic devices integrated with nanostructures enables highly efficient, flexible and controllable biosensing, among which zinc oxide (ZnO) nanostructure-based fluorescence detection has been demonstrated to be a promising methodology due to its high electrical point and unique fluorescence enhancement properties. The optimization of microfluidic synthesis of ZnO nanostructures for biosensing on chip has been in demand due to its low cost and high efficiency, but still the flow-induced growth of ZnO nanostructures is not extensively studied. Here, we report a simple and versatile strategy that could manipulate the local flow field by creating periodically arranged micropillars within a straight microchannel. We have explored the effects of perfusion speed and flow direction of seed solution, localized flow variation of growth solution and growth time on the morphology of nanostructures. This provided a comprehensive understanding which governs nanostructure fabrication controlled by flow. The results demonstrated that localized flow in microfluidic devices was essential for the initiation and growth of zinc oxide crystals, enabling precise control over their properties and morphology. Furthermore, a model protein was used to demonstrate the intrinsic fluorescence enhancement of ZnO nanostructures as an example to reveal the morphology-related enhancement properties.