Recently, the monitoring and cleaning of contaminants deposited on the surfaces of advanced optical components has attracted increasing attention. We proposed a scheme for monitoring organic ...contaminants using microfiber Bragg grating (MFBG). Since MFBG is covered by an organic contaminant film, the central reflection wavelength will be red-shifted. Theoretically, we numerically simulated the relationship between the offset of MFBG central reflection wavelength and the thickness of the organic contaminants, which indicates pronounced linearity. Moreover, the sensitivity can be further improved as the cladding diameter of MFBG decreases. Technically, MFBGs with diameters of <inline-formula> <tex-math notation="LaTeX">8.02~\mu </tex-math></inline-formula> m, <inline-formula> <tex-math notation="LaTeX">9.80~\mu </tex-math></inline-formula> m and <inline-formula> <tex-math notation="LaTeX">10.01~\mu </tex-math></inline-formula> m, were fabricated by etching fiber Bragg gratings (FBGs) with 10% mass concentration HF solution. Additionally, we experimentally demonstrated the effectiveness and immediacy of our scheme. When the diameter of MFBG is about <inline-formula> <tex-math notation="LaTeX">8~\mu </tex-math></inline-formula> m, the sensitivity is 3.5 pm/nm, which is consistent with the theoretical simulation results. According to the resolution of the grating demodulator, the minimum measurable surface mass density is 24.92 ng/mm 2 . As for the fiber with a diameter of <inline-formula> <tex-math notation="LaTeX">9.90~\mu </tex-math></inline-formula> m, the sensitivity is 0.5 pm/nm and the minimum measurable surface mass density is 174.44 ng/mm 2 . The results suggested a feasible route to realizing the effective monitoring of organic contaminants.
•A grape point cloud segmentation method was developed.•The optimal cutting point of the grape peduncle is solved and the pose is estimated.•The demonstrated performance indicated that it could be ...used on harvesting robots.
In-field object detection and pose estimation are challenging tasks in industrial harvesting scenarios. This study investigates a method for object detection and 6D pose estimation using a depth camera to prevent harvesting robots from collisions while harvesting grape clusters. First, the Mask Region Convolutional Neural Network (Mask R-CNN) is deployed to segment 2D images and output binary mask images of grape clusters. Second, the grape cluster point cloud is segmented based on the binary mask image and the mapping relationship between the image and point cloud to provide a high-quality point cloud through pre-processing. Third, the optimal cutting point is located by constructing the grape peduncle region of interest (RoI). Finally, the peduncle surface is fit based on the locally weighted scatterplot smoothing (LOWESS) algorithm, and the pose of the peduncle is estimated using geometric methods. The detection results from 238 test images show that the mean Precision (mP) is 86.0%, the mean Recall (mR) is 79.9%, the F1-score is 0.828, and mean Intersection Over Union (mIOU) for instance segmentation is 87.9%. The pose estimation results from 172 grape peduncles yield an error angle of 22.22 degrees ± 17.96 degrees in pose estimation. The detection and pose estimations for each grape cluster require approximately 1.786 s. The demonstrated performance of the proposed method indicates it can be applied to collision-free grape harvesting using robots in unstructured environments.
Reliable and robust fruit-detection algorithms in nonstructural environments are essential for the efficient use of harvesting robots. The pose of fruits is crucial to guide robots to approach target ...fruits for collision-free picking. To achieve accurate picking, this study investigates an approach to detect fruit and estimate its pose. First, the state-of-the-art mask region convolutional neural network (Mask R-CNN) is deployed to segment binocular images to output the mask image of the target fruit. Next, a grape point cloud extracted from the images was filtered and denoised to obtain an accurate grape point cloud. Finally, the accurate grape point cloud was used with the RANSAC algorithm for grape cylinder model fitting, and the axis of the cylinder model was used to estimate the pose of the grape. A dataset was acquired in a vineyard to evaluate the performance of the proposed approach in a nonstructural environment. The fruit detection results of 210 test images show that the average precision, recall, and intersection over union (
) are 89.53, 95.33, and 82.00%, respectively. The detection and point cloud segmentation for each grape took approximately 1.7 s. The demonstrated performance of the developed method indicates that it can be applied to grape-harvesting robots.
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•Fe anode achieved 101–743% higher of hydrogen productivity than other iron sources.•Fe anode led complete removal of phosphate within 2 d (vs. 0–39.4% in other groups).•More ...crystalline vivianite particles were observed in EF system assisted by Fe anode.•Homo-acetogens (78.0%), EAB (8.0%) and DIRB (2.3%) were enriched in Fe anode group.•Correlation and syntrophy between functional microbes and metabolites were explored.
Electro-fermentation (EF) is a promising technology for extracting valuable metabolites from waste biomass, while its effectiveness for phosphorus recovery has received little attention. In this study, we investigated the impact of different iron sources, i.e., FeCl3, FeOOH, Fe2O3, zero-valent iron (ZVI), a built-in Fe anode, and stainless-steel mesh (SSM), on concurrent biohydrogen and vivianite (Fe3(PO4)2·8H2O) recovery from sludge fermentation liquid (SFL) in an EF system. Results indicated that the Fe anode group achieved the highest hydrogen productivity of 17.7 mmol/g COD at 5 d, which was 101–743% higher than that of other iron sources. The utilization efficiency of short-chain fatty acids (SCFAs) peaked at 79.1% within 5 d, which was 1.2 folds higher than that of Control (without iron addition). Moreover, the phosphate removal efficiency reached 100% within 2 d and 5 d in Fe anode and SSM group, respectively, while the other groups achieved only 0–39.4% removal. SEM and XRD analyses demonstrated the existence of vivianite particles in the recovered products in the groups of FeCl3, FeOOH and Fe anode. Electrochemically active bacteria (EAB), e.g., Geobacter, Comamonas, and Desulfovibrio, accounted for 3.6–60.2% of all groups. Geobacter, Azospira, Comamonas, and Desulfovibrio, which are also considered dissimilatory iron reducing bacteria (DIRB), were enriched to 0.1–40.7% in all groups. Acetoanbacterium and Acetoaerobium, typical homo-acetogens, were enriched to 78.0% in the Fe anode group, while was only 9.0–29.3% in other groups. Correlation and molecular ecological network (MEN) analyses of the functional microbial consortia further indicated the intertrophic interaction. This study provides a theoretical basis for synchronous hydrogen and phosphorus recovery from WAS in the further industrial implementation.
Core–shell nanostructured Au–Fe2O3@SiO2 nanoreactors with 3–4 nm Au–Fe2O3 cores and microporous SiO2 shells were prepared by a water-in-oil (W/O) microemulsion method. In comparison with Au@SiO2, the ...obtained Au–Fe2O3@SiO2 nanoreactors showed a significant improvement in CO oxidation due to the strong interactions between Au and Fe2O3 in the cores.
Core-shell nanostructured Au-Fe
2
O
3
@SiO
2
nanoreactors with 3-4 nm Au-Fe
2
O
3
cores and microporous SiO
2
shells were prepared by a water-in-oil (W/O) microemulsion method. In comparison with ...Au@SiO
2
, the obtained Au-Fe
2
O
3
@SiO
2
nanoreactors showed a significant improvement in CO oxidation due to the strong interactions between Au and Fe
2
O
3
in the cores.
The combination of the Au-Fe
2
O
3
phase and core-shell structure helps in achieving high activity and good thermal stability.
Core–shell nanostructured Au–Fe 2 O 3 @SiO 2 nanoreactors with 3–4 nm Au–Fe 2 O 3 cores and microporous SiO 2 shells were prepared by a water-in-oil (W/O) microemulsion method. In comparison with ...Au@SiO 2 , the obtained Au–Fe 2 O 3 @SiO 2 nanoreactors showed a significant improvement in CO oxidation due to the strong interactions between Au and Fe 2 O 3 in the cores.
Pollutant distribution remains poorly understood when traffic tidal flows (TTFs) happen. By conducting computational fluid dynamic (CFD) simulations, the research efforts first focus on how ...different-side traffic-produced flow and turbulence (TPFT) affect in-canyon airflow and corresponding pollutant dispersion. Second, the composite effects of non-uniform traffic emission and TPFT are investigated. Finally, the influences of TTFs while varying different street canyon geometry and approaching wind condition is explored.
The results demonstrate that the turbulent diffusion terms enhanced by the traffic movement contribute to the pollutant dispersion around traffic lanes. Besides, both-side TPFTs push leeward pollutants towards traffic flow “downstream” due to the unidirectional advection terms along the traffic direction. Simultaneously considering the non-uniform traffic emission and TPFT, either leeward- or windward-congested TTFs has a higher concentration at the pedestrian level close to the congested traffic lane. Besides, the TTF with windward congestion has a higher volume-average concentration of the whole street canyon. With varying building separation, street canyon aspect ratio, and incoming wind direction, the TTFs still result in a larger pollutant accumulation above the pedestrian level, which is nearby the congested traffic flow.
•Both leeward and windward TPFTs greatly reduce the windward concentration.•Both TPFTs push leeward pollutants towards traffic flow “downstream”.•Leeward/windward-congested TTFs have higher leeward/windward concentrations.•Varying canyon geometry and wind direction do not affect the influence of TTFs.
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