A magnetic composite of silver/iron oxides/carbon nanotubes (Ag/Fe3O4/CNTs) was synthesized and used as an adsorbent for the preconcentration of mercury ions in water solutions at room temperature (25°C) in this study. The silver nanoparticles were supported on the magnetic CNTs. The modification enabled the composite had not only a high adsorption capacity for mercury ions (Hg2+) but also the magnetic isolation properties. A fast, sensitive, and simple method was successfully developed for the preconcentration and determination of trace amount of Hg2+ in water using the synthesized nanocomposite as adsorbent. The mercury concentration was determined by an atomic fluorescence spectrometer (AFS). The experimental conditions such as pH value, extraction temperature, extraction time, sample volume, eluent composition and concentration, sorbent amount, and coexisting ions were investigated for the optimization. A 500 mL of sample volume resulted in a preconcentration factor of 125. When a 200 mL of sample was employed, the limit of detection for Hg2+ was as low as 0.03 ng mL−1with relative standard deviation of 4.4% at 0.1 ng mL−1 (n = 7). The ease of synthesis and separation, the good adsorption capacity, and the satisfactory recovery will possibly make the composite an attractive adsorbent for the preconcentration of ultratrace Hg2+ in waters.
Soil washing is an efficient, rapid, and cost-effective remediation technique to dissolve target pollutants from contaminated soil. Here we studied the effects of leaching agents: hydrochloric acid (HCl), ethylenediamine tetraacetic acid disodium salt (Na2EDTA) and citric acid (CA), and reductants: hydroxylamine hydrochloride (NH2OH·HCl) and l-ascorbic acid (VC) on the leaching of Pb from synthetic iron oxide; the changes in mineralogy, morphology, and occurrence of Pb were shown by XRD, SEM, and sequential extraction analyses. Although the washing efficiency of Pb follows the trend HCl (44.24%) > Na2EDTA (39.04%) > CA (28.85%), the cooperation of the leaching agent with reductant further improves the efficiency. VC is more suitable as a reductant considering the higher washing efficiency by HCl-VC (98.6%) than HCl-NH2OH·HCl (88.8%). Moreover, increasing the temperature can promote the decomposition and dehydrogenation reaction of VC with more H+. Among the mixture agents, Na2EDTA + VC is the most effective agent to remediate the two kinds of contaminated soils owing to the formation of Fe(ii)-EDTA, a powerful reducing agent so that the efficiencies can reach up to 98.03% and 92.81%, respectively. As a result, these mixture agents have a great prospect to remediate Pb-contaminated soils.
Leaf chlorophyll content (LCC) is crucial for monitoring the physiological processes of crops. Many studies have utilized spectral features to develop regression models for accurate LCC estimation, enabling the quantitative assessment and evaluation of crop growth status. The selection of optimal spectral features and regression algorithms significantly affects the precision of LCC estimation. In this study, we compared and analyzed the optimal spectral features for LCC estimation, as well as the consistency of machine learning methods across different crop types, phenology periods, and sensors. First, we extracted various spectral features, including the original spectral features (OS), first-order derivative spectral features (FDS), original continuum-removed spectra (CR) along with their four related derivative spectral features, principal component variables derived from different spectral features, and highly correlated spectral features with LCC. These extracted spectral features were then employed to construct the LCC models using six common regression algorithms on different datasets. Finally, we analyzed the optimal combination of spectral features and regression algorithms for accurate LCC estimation considering various dimensions, such as crop type, phenological period, and sensor used in observation conditions. The results demonstrate that the combinations of the principal component variables of continuum-removed derivative reflectance with the top 10 correlations with LCC (PCA_CRDR_R) combined with Gaussian process regression (GPR) can be considered as the optimal choice for estimating LCC under diverse observation conditions at a canopy scale, and its R2 can reach 0.62 for sugar beet LCC estimation; thus providing valuable theoretical guidance for selecting appropriate spectral features for LCC estimation.
The sulfion oxidation reaction (SOR) could offer an energy‐efficient and tech‐economically favorable alternative to the oxygen evolution reaction (OER) for H2 production. Transition metal (TM) based catalysts have been considered promising candidates for SOR but suffer from limited activity due to the excessive bond strength from TM‐S2‐ d‐p orbit coupling. Herein, we propose a feasible strategy of screening direct d‐p orbit hybridization between TM and S2‐ by constructing the Turing structure composed of lamellar stacking carbon‐confined nickel nanosheets. The optimized p‐p orbit coupling between electron‐injected carbon and S2‐ enables exceptional catalytic activity and stability for sulfion degradation and energy‐efficient yet value‐added H2 production. Specifically, it achieves a current density of 500 mA cm‐2 at an ultralow potential of 0.67 V vs. RHE for alkaline SOR. Theoretical calculations indicate that the electron transfer from Ni imparts metallicity and a higher p‐band center to carbon shells, thereby contributing to optimized p‐p orbit hybridization and a thermodynamically favorable stepwise sulfion degradation. Practically, a two‐electrode flow cell achieves an industrial current density of 1 Acm‐2 at an unprecedented low voltage of 0.91 V while maintaining stability for over 300 hours, and exhibits high productivities of 3.83 and 0.32 kgh‐1m‐2 for sulfur and H2, respectively.
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