Nanoscale zero-valent iron (nZVI) particles have excellent capacity for in situ remediation of groundwater resources contaminated by a range of organic and inorganic contaminants. Chlorinated solvents are by far the most treated compounds. Studies at column, pilot, and field scales have reported successful decrease in contaminant concentration upon injection of nZVI suspensions in the contaminated zones. However, the field application is far from optimized, particularly for treatments at—or close to—the source, in the presence of residual nonaqueous liquid (NAPL). The knowledge gaps surrounding the processes that occur within the pores of the sediments hosting those contaminants at microscale limit our ability to design nanoremediation processes that are optimized at larger scales. This contribution provides a pore-scale picture of the nanoremediation process. Our results reveal how the distribution of the trapped contaminant evolves as a result of contaminant degradation and generation of gaseous products. We have used state-of-the-art four-dimensional (4D) imaging (time-resolved three-dimensional [3D]) experiments to understand the details of this degradation reaction at the micrometer scale. This contribution shows that the gas released (from the reduction reaction) remobilizes the trapped contaminant by overcoming the capillary forces. Our results show that the secondary sources of NAPL contaminations can be effectively treated by nZVI, not only by in situ degradation, but also through pore-scale remobilization (induced by the evolved gas phase). The produced gas reduces the water relative permeability to less than 1% and, therefore, significantly limits the extent of plume migration in the short term.

Commonly used methods to visualize the biological structure of brain tissues at subcellular resolution are confocal microscopy and two-photon microscopy. Both require slicing the sample into sections of a few tens of micrometers. The recent developments in X-ray microtomography enable three-dimensional imaging at sub-micrometer and isotropic resolution with larger biological samples. In this work, we developed and compared original microtomography methods and staining protocols to improve the contrast for in vitro mouse neuron imaging. Using Golgi's method to stain neurons randomly, we imaged the whole set of mouse brain structures. For specific and nonrandom neuron labeling, we conjugated 20 nm gold nanoparticles to antibodies used in the immunohistochemistry (IHC) method, using anti-NeuN to label specifically neuronal nuclei. We applied an original subtraction dualenergy method for microtomography in the vicinity of the Au L-III absorption edge and compared image reconstructions to confocal microscopy images acquired on the same samples. The results show the possibility to characterize the 3D entire brain structure of mice. They demonstrated a high contrast and neuron detection improvement by applying the dual-energy method coupled to IHC staining.

More sustainable technologies in agriculture are important not only for increasing crop yields, but also for reducing the use of agrochemicals and improving energy efficiency. Recent advances rely on computer vision systems that differentiate between crops, weeds, and soil. However, manual dataset capture and annotation is labor-intensive, expensive, and time-consuming. Agricultural robots provide many benefits in effectively performing repetitive tasks faster and more accurately than humans, and despite the many advantages of using robots in agriculture, the solutions are still often expensive. In this work, we designed and built a low-cost autonomous robot (DARob) in order to facilitate image acquisition in agricultural fields. The total cost to build the robot was estimated to be around $850. A low-cost robot to capture datasets in agriculture offers advantages such as affordability, efficiency, accuracy, security, and access to remote areas. Furthermore, we created a new dataset for the segmentation of plants and weeds in bean crops. In total, 228 RGB images with a resolution of 704 × 480 pixels were annotated containing 75.10% soil area, 17.30% crop area and 7.58% weed area. The benchmark results were provided by training the dataset using four different deep learning segmentation models.