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Figure 6 in Effects of aluminum, copper, and titanium nanoparticles on some blood parameters in Wistar rats

Figure 6. ALP levels in the serum of female rats orally exposed to Al2 O 3, TiO2, and CuO nanoparticles for 14 days. Details are given in Figure 1.

Figure 5 in Effects of aluminum, copper, and titanium nanoparticles on some blood parameters in Wistar rats

Figure 5. Total antioxidant levels in the serum of female rats orally exposed to Al2 O 3, TiO2, and CuO nanoparticles for 14 days. Details are given in Figure 1.

Figure 4 in Effects of aluminum, copper, and titanium nanoparticles on some blood parameters in Wistar rats

Figure 4. Total oxidant levels in the serum of female rats orally exposed to Al O, TiO, and CuO nanoparticles for 14 days. 2 3 2 Details are given in Figure 1.

Figure 3 in Effects of aluminum, copper, and titanium nanoparticles on some blood parameters in Wistar rats

Figure 3. The activity of Ca-ATPase in the erythrocytes of female rats orally exposed to Al O, TiO, and CuO nanoparticles for 14 2 3 2 days. Details are given in Figure 1.

Figure 1 in Effects of aluminum, copper, and titanium nanoparticles on some blood parameters in Wistar rats

Figure 1. The activity of Na,K-ATPase in the erythrocytes of female rats orally exposed to Al 2 O 3, TiO 2, and CuO nanoparticles for 14 days. Each point shows the mean of 6 rats and the standard errors. Statistical results and % alterations are given in the Table.

Figure 4 in Composition and structure of soil fauna community in the Dexing Copper Mine tailings pool after revegetation

Figure 4. One-way ANOVA of Shannon–Wiener diversity index (H'), Pielou index (Js), and Margalef index (D) across the different samples (mean ± SE). Means with different letters are significantly different as assessed by LSD test, α = 0.05.

Figure 3 in Composition and structure of soil fauna community in the Dexing Copper Mine tailings pool after revegetation

Figure 3. One-way ANOVA of the functional groups across the different samples (mean ± SE). Means with different letters are significantly different as assessed by LSD test, α = 0.05.

Figure 5 in Composition and structure of soil fauna community in the Dexing Copper Mine tailings pool after revegetation

Figure 5. Results of the principal components analysis (PCA) of the soil fauna data. The values are means (n = 3) with bidirectional error bars of axis 1 and 2. For all the PCA plots, the values on the x- and y-axes represent the percent variation explained by axis 1 and axis 2, respectively. I, II, III, and IV refer to the sample I, sample II, sample III, and sample IV, respectively.

Figure 2 in Composition and structure of soil fauna community in the Dexing Copper Mine tailings pool after revegetation

Figure 2. One-way ANOVA of the abundance (A) and taxonomic richness (B) across different samples (mean ± SE). Means with different letters are significantly different as assessed by LSD test, α = 0.05.

Figure 6 in Toxicity effects of copper on two species of marine diatoms microalgae and two species of dinoflagellates

Figure 6. Dynamics of organic carbon content, C (a), the Fv/Fm value (b) and the relative electronic transport rate, rETR on 3-ed day (c) in L. fissa at different copper ions concentrations: 1 – control, 2 – 3 µg·L-1, 3 – 5 µg·L-1, 4 – 10 µg·L-1, 5 – 50 µg·L-1, 6 – 100 µg·L-1, 7 – 200 µg· L-1. The average values of ± standard deviation are presented.

Figure 4 in Toxicity effects of copper on two species of marine diatoms microalgae and two species of dinoflagellates

Figure 4. Dynamics of organic carbon content, C (a, b), Fv/Fm (c) and relative electron transport rate, rETR on 3-ed day (d) in C. pelagica culture at different copper ions concentrations in small celled culture (a, c, d): 1 – control, 2 – 10 µg·L-1, 3 – 100 µg·L-1, 4 – 200 µg.L-1, 5 – 400 µg.L-1, 6 – 600 µg.L-1 and in large cell culture (b): 1 – control, 2 – 1 µg·L-1, 3 – 3 µg·L-1, 4 – 5 µg·L-1, 5 – 10 µg·L-1, 6 – 50 µg·L-1. The average values of ± standard deviation are presented.

Figure 2 in Toxicity effects of copper on two species of marine diatoms microalgae and two species of dinoflagellates

Figure 2. Relationship between microalgae optical density and organic carbon content (mg C· L-1) at a wavelength of 750 nm (OD750) in cultures: a – P. tricornutum, b – C. pelagica, c – P. nanum, d – L. fissa.

Figure 1 in Toxicity effects of copper on two species of marine diatoms microalgae and two species of dinoflagellates

Figure 1. View of microalgae cells under a light microscope: a – C. pelagica, b – P. tricornutum, c – L. fissa, d – P. nanum. The total magnification of the system is 400 times.

Figure 3 in Toxicity effects of copper on two species of marine diatoms microalgae and two species of dinoflagellates

Figure 3. Dynamics of organic carbon content, C (a, b), Fv/Fm (c, d) and the relative electronic transport rate, rETR on the 3rd day (e, f) in P. tricornutum at different copper ions concentrations: 1 – control, 2 – 1 µg·L-1, 3 – 5 µg·L-1, 4 – 10 µg·L-1, 5 – 50 µg·L-1, 6 – 100 µg·L-1, 7 – 200 µg·L-1; a, c, e – initial biomass of the culture is 0.2 mg C L-1, b, d, f – 1.0 mg C·L-1. The average values of ± standard deviation are presented.

Figure 5 in Toxicity effects of copper on two species of marine diatoms microalgae and two species of dinoflagellates

Figure 5. Dynamics of organic carbon content, C (a, b), Fv/Fm value (c, d) and relative electronic transport rate, rETR on 3-ed day (e, f) in P. nanum with an initial biomass of 0.5 mg C·L-1 (a, c, e) at copper ions concentrations: 1 – control, 2 – 1 µg·L-1, 3 – 3 µg·L-1, 4 – 5 µg·L-1, 5 – 10 µg·L-1, 6 – 50 µg· L-1, 7 – 100 µg·L-1 and with an initial biomass of 1.5 mg C.L-1 (b, d, f) at copper concentrations: 1 – control, 2 – 10 µg·L-1, 3 – 40 µg·L-1, 4 – 60 µg·L-1, 5 – 100 µg·L-1, 6 – 200 µg·L-1. The average values of ± standard deviation are presented.

opencc-by-4.0Sep 2018View details →

Fig. 1 in Evaluation of copper hydroxide as a repellent and feeding deterrent for Cuban brown snail (Mollusca: Gastropoda: Pleurodontidae)

Fig. 1. Mean number (± SE) of Zachrysia provisoria snails accessing the lid of their container over a 7-d period when the interior walls of the container were coated with copper sulfate residue, or treated only with tap water (control). Days with statistically significant (P <0.05) differences in snail behavior are marked with an asterisk (*).

opencc-by-sa-4.0Oct 2024View details →

Dataset of "Identifying Active Oxygen and Copper Species in Cu/CeO2 Catalysts using Raman and UV-Vis Modulation Excitation Spectroscopy"

This zip file contains the dataset of the publication "Identifying Active Oxygen and Copper Species in Cu/CeO2 Catalysts using Raman and UV-Vis Modulation Excitation Spectroscopy" (<a href="https://doi.org/10.1021/acs.jpcc.4c05826">https://doi.org/10.1021/acs.jpcc.4c05826</a>). The authors are Henrik Hoyer and Christan Hess*. The zip file includes the data for figures 2 and 3 in the manuscript and the data for figures S1-S12 in the SI. For more detailed information on the dataset, please refer to the description file.  *email: christian.hess@tu-darmstadt.de

opencc-by-4.0Oct 2024View details →

Open Copper Site MOF (OCS-MOF) Database

This dataset represents a curated collection of metal-organic frameworks (MOFs) with open copper sites (OCS) specifically selected for their potential in CO2/CH4 separation, which is essential for biogas upgrading. The OCS-MOFs were sourced and refined from the ARC-MOF database, following a comprehensive screening process to ensure novelty, validity, and applicability. The structures underwent rigorous quality control, including elimination of duplicates using pymatgen and MOFid, validation of physical and chemical integrity using MOFChecker, and assessment of pore accessibility via Zeo++. The dataset serves as the foundation for the multi-scale molecular simulations and machine learning models presented in our accompanying research. These models employ OCS-specialized force fields and diversity-transferable machine learning approaches to unveil structure-function relationships in MOFs, providing valuable insights into pore geometry and local chemical environments that optimize CO2 adsorption capacity and selectivity. For the accompanying machine learning pipline code, and tabulated data, please visit the GitHub repository here. https://github.com/xiaoyu961031/OCS-MOFs If you use this in your work, please cite our related publication: Xiaoyu Wu, Rui Zheng and Jianwen Jiang*. Leveraging Cross-Diversity Machine Learning to Unveil Metal-Organic Frameworks with Open Copper Sites for Biogas Upgrading. Journal of Chemical Theory and Computation, 2025. DOI:10.1021/acs.jctc.4c01478

opencc-by-nc-sa-4.0Jul 2021View details →

pXRF investigation of copper objects from the Congo Basin (Museum of Anthropology and Archaeology (MAA), University of Cambridge)

This dataset contains the data related to the pXRF analyses conducted on copper objects from the Congo Basin curated at the Museum of Anthropology and Archaeology (MAA), University of Cambridge in December 2020. The study has been done in the framework of the H2020-MSCA-IF-2019 Project Number 890896 ‘An Archaeology of Exchange Networks in Central Africa. The Cases of the Copperbelt and Niari Basin Copper Deposits’ (ArCAN). <a href="http://cordis.europa.eu/project/id/890896">https://cordis.europa.eu/project/id/890896</a> All information about the dataset can be found in the readme file.