Find research datasets worth reusing

opencc-by-4.0Feb 2019View details →

Ambient air ozone concentrations using metal-oxide low-cost sensors: Spain and Italy, summer 2017

Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2017 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179.

opencc-by-4.0Dec 2017View details →

Ambient air ozone concentrations using metal-oxide low-cost sensors: Spain and Italy, summer 2018

Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2018 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179.

opencc-by-4.0Nov 2021View details →

RDF version of the data from Anastasios G. Papadiamantis et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform (2020)

This is an RDFied version of the dataset published in Papadiamantis, A.G. et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform. Nanomaterials 2020, 10, 2017. The original dataset publication DOI: <a href="https://doi.org/10.3390/nano10102017">https://doi.org/10.3390/nano10102017</a> The Original publication authors: Papadiamantis, A.G.; Jänes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; Tämm, K.; Afantitis, A.

opencc-by-4.0Jun 2021View details →

Electrochemical and Spectroscopic Data supported by Computational Models for Exploring the Metal- and Ligand-Based Oxidation of Mackinawite Nanoparticles

Supporting information to our study, where under anaerobic conditions, ferrous iron reacts with sulfide producing FeS precipitate, which can then undergo a temperature, redox potential, and pH dependent maturation process resulting in the formation of oxidized mineral phases such as gregite or pyrite. The dataset provide information about the chemical speciation of iron-sulfide by cyclic voltammetry, Raman and X-ray absorption spectroscopic techniques. Nanoparticulate FeS was found to get oxidized to a Fe3+ containing FeS phase at -0.5 V vs. Ag/AgCl (pH = 7) and in a concomitant oxidation step, polysulfides are proposed to give a material described as Fe2+(1−3x)Fe3+(2x)S2-(1-y)(Sn2-)y. The thermodynamic differences between ligand- and metal-based oxidation processes from density functional theory can be used to describe one- and two-electron electronic and structural transformations. These findings together point to the existence of a previously unknown, metastable FeS phase located between FeS and greigite (Fe2+Fe3+2S2-4) along a metal oxidation path, and Fe2+S2- and pyrite (Fe2+S22-) along a ligand oxidation path, respectively.

opencc-by-4.0Aug 2023View details →

Influence of protein corona on cytotoxicity of metal oxide nanoparticles against human keratinocyte cell line (HaCaT)

The model identified, among the factors determining the cytotoxic properties of metal oxide nanoparticles against HaCaT cell lines, a number of variables related to the processes occurring on the surface of nanoparticles in a biological medium, including the ability to form protein corona. The selected descriptors describe both the electronic structure of the metal oxides that are the components of the nanoparticles, i.e. the ionization potential (IP_ActivM_SM_#1, IP_ActivM_SM_#2) and the initial nanoforms, i.e. the particle size (Primary size) and the percentage content of the metal oxide which is the main component of the nanoparticle (Purity_#1) ; characterize nanoparticles in the medium, i.e. the isoelectric point (PZZP_#2), stability (Stability), potential for dissolution (Dissolution), generation of reactive oxygen species (ROS production) and protein adsorption (Protein adsorption). The listed descriptors reflect the features that are discussed in the literature as potentially related to the toxic effect of nanoparticles.

opencc-by-4.0Aug 2023View details →

Identification of factors determining the process of aggregation/agglomeration of metal oxide nanoparticles in a biological medium

The model allows to identify factors determining the process of aggregation/agglomeration of metal oxide nanoparticles in a biological medium and to verify the importance of ion adsorption and protein adsorption in this process.  Model confirms the significant effect of protein adsorption on the hydrodynamic diameter of metal oxide particles in the biological medium, and does not confirm the significant effect of ion adsorption in this process. It’s an example of modeling the properties of nanoparticles, where apart from the descriptors describing the structure of nanoparticles, there are also parameters characterizing the medium.

opencc-by-4.0Sep 2023View details →

Dataset of the paper "Improving the Stability of Photodoped Metal Oxide Nanocrystals with Electron Donating Graphene Quantum Dots"

The dataset provides the data for the publication: "Improving the Stability of Photodoped Metal Oxide Nanocrystals with Electron Donating Graphene Quantum Dots"

opencc-by-4.0Nov 2023View details →

Tomography Data for: Three-dimensional Nanoscale Metal, Metal Oxide and Semiconductor Frameworks through DNA-programmable Assembly and Templating

This is data collected at the 3-ID Hard X-ray Nanoprobe beamline. This repository supports the following research article: Data provided is the aligned dataset and reconstruction using a FISTA algorithm. Angles Collected  -90 to +45 at 1 degree steps. Three-dimensional Nanoscale Metal, Metal Oxide and Semiconductor Frameworks through DNA-programmable Assembly and TemplatingBy Aaron Michelson.

opencc-by-4.0Nov 2021View details →

Datasets to Poly(ethylene oxide)-based Electrolytes for Solid-State Potassium Metal Batteries with Prussian Blue Positive Electrode

This dataset provides the raw data to the manuscript "Poly(ethylene oxide)-based Electrolytes for Solid-State Potassium Metal Batteries with Prussian Blue Positive Electrode" published in ACS Appl. Polym. Mater. (DOI: <a href="https://doi.org/10.1021/acsapm.2c00014">10.1021/acsapm.2c00014</a> ) / <a href="https://doi.org/10.1021/acsapm.2c00014">https://doi.org/10.1021/acsapm.2c00014</a> Specifically, the following measurements are provided: Electrochemical cell tests of liquid and solid electrolytes ("CYCLING_" & Ratecapability test) Solid electrolyte characterization: Differential Scanning Calorimetry ("DSC_") Electrochemical Impedance Spectroscopy ("EIS_") Rheological measurements ("RHEO_") X-ray diffraction data ("XRD_")

opencc-by-4.0Jul 2022View details →

Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors

The "Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors" can be used to test machine learning approaches on their capability of interpreting sensor patterns of commercially available MOS gas sensors, i.e., SGP40 (Sensirion AG, Stäfa, Switzerland), to predict multiple different gas concentrations and the relative humidity. Furthermore, the dataset can be used to test the transferability between sensors.  The dataset was recorded with the help of a custom-built gas mixing apparatus (GMA). The GMA allows applying well-known gas mixtures to multiple gas sensors. For this experiment, three SGP40  with four sub-sensors each were exposed to 900 different unique gas mixtures (UGMs) consisting of ten different gases. In detail, the dataset consists of eight volatile organic compounds (VOCs) (acetic acid, acetone, ethanol, ethyl acetate, formaldehyde, isopropanol, toluene, and xylene), two background gases (carbon monoxide and hydrogen), and the relative humidity at 20 °C. During exposure, the sensors are operated in a temperature-cycled operation. The temperature cycle consists of alternating high and low-temperature phases. The high-temperature phases are set at 400 °C and have a duration of 5 seconds, while the low-temperature steps increase in 25 °C steps from 100 °C-375 °C, where each step has a duration of 7 seconds. The only exception is sub-sensor 4, where the temperature is only alternated between 250 °C and 300 °C. The total duration of the temperature cycle is 144 seconds, and during this time, the logarithmic sensor resistance is read out at 10 Hz. Each gas mixture was recorded for ten temperature cycles to ensure that stable gas mixtures were applied to the sensor. Only stable samples 6 (not always),7,8, and 9 were used for further evaluation. The 900 UGMs can be separated into three parts, and for each part, the mixtures were generated based on Latin hypercube sampling and the ranges specified in Table 1. <table> <caption>Tabel 1: Uniform distributed ranges for all gasses within the gas mixtures</caption> <tbody> <tr> <td> </td> <td>UGM 1-200</td> <td>UGM 201-500</td> <td>UGM501-900</td> </tr> <tr> <td>Carbon monoxide</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> </tr> <tr> <td>Hydrogen</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> </tr> <tr> <td>Relative humidity</td> <td>25 - 80 %</td> <td>25 - 80 %</td> <td>25 - 80 %</td> </tr> <tr> <td>Acetic acid</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Acetone</td> <td>3 - 50 ppb</td> <td>3 - 150 ppb</td> <td>3 - 500 ppb</td> </tr> <tr> <td>Ethanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Ethyl acetate</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Formaldehyde</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 300 ppb</td> </tr> <tr> <td>Isopropanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Toluene</td> <td>1 - 75 ppb</td> <td>1 - 75 ppb</td> <td>1 - 250 ppb</td> </tr> <tr> <td>Xylene</td> <td>2 - 150 ppb</td> <td>2 - 150 ppb</td> <td>2 - 500 ppb</td> </tr> </tbody> </table> To be able to use this dataset for transfer learning, the dataset consists of three different SPG40; two are from the same batch (sensor A and sensor B), and sensor C is from a different batch.  The dataset consists of the sensors' data and a target for evaluation. The data is already split into training and Validation and is stored in cells for each sensor:   sensorA_train  sensorA_test  sensorB_train  sensorB_test  sensorC_train  sensorC_test  Each sensor cell contains four arrays, one for each sub-sensor within one SGP40. The number of rows in the arrays represents the number of observations (693 for test and 2401 for training), and the number of columns represents the number of samples per observation (1440). The targets, i.e., the concentrations of each gas, are given in the target_train and targe_test structs. Since the data were recorded simultaneously, those structs can be used as targets for all sensors. The ten different gases, relative humidity, and TVOCsens are actual targets, while the range parameter represents the specific unique gas mixture ID. Although this is a mat file, it can be opened as an hdf5 file.

opencc-by-4.0Jan 2018View details →

Data used in article 'Tuning Charge Carrier Dynamics and Surface Passivation in Organolead Halide Perovskites with Capping Ligands and Metal Oxide Interfaces'

Data underlying the article 'Tuning Charge Carrier Dynamics and Surface Passivation in Organolead Halide Perovskites with Capping Ligands and Metal Oxide Interfaces' published in Advanced Optical Materials.

opencc-by-4.0Jun 2020View details →

Figure 3 in Multiple metals and agricultural use affects oxidative stress biomarkers in freshwater Aegla crabs

Figure 3. Biomarkers grouped by hydrographic basin (Suzana River basin, Ligeirinho-Leãozinho River basin, Dourado River basin). Different letters indicate significant differences (p <0.05), as compared by one-way ANOVA plus Tukey post-test (between basins).

opencc-by-4.0Jun 2020View details →

Figure 2 in Multiple metals and agricultural use affects oxidative stress biomarkers in freshwater Aegla crabs

Figure 2. Biplot of PCA ordination for the metal concentration in sediment of the three studied basins. Suzana River basin (S); Ligeirinho-Leãozinho River basin (L); Dourado River basin (D).

opencc-by-4.0Jun 2020View details →

Figure 1 in Multiple metals and agricultural use affects oxidative stress biomarkers in freshwater Aegla crabs

Figure 1. Map of the sampling sites. (A) Suzana River basin (between 27°35'38" and 27°36'16"S; 52°11'11" and 52°13'41"W); (B) Ligeirinho-Leãozinho River basin (between 27°40'15" and 27°36'16"S; 52°16'03" and 52°13'41"W); (C) Dourado River basin (between 27°33'59" and 27°37'13"S; 52°17'46" and 52°19'36"W). The circles (•) indicate the sampled streams. The numbered boxes indicate the areas of land uses analysis.

Figure 5 in Determination of oxidative, genotoxic, and histopathologic effects of metal pollution on the fish fauna inhabiting Karasu River, Turkey

Figure 5. Pathological damages in A. mossulensis A) separation from the granular layer (black arrow), hyperemia (orange arrows) and infiltration (red arrow) in the cerebellum, B) separation in pia mater (black arrow), congestion and damage in vein (orange arrow), degeneration in periventricular layer (green arrow) and axonopathy (circle) C) dilatation (orange arrow) and vacuolization (yellow arrows) in the cortex H & E.

Figure 4 in Determination of oxidative, genotoxic, and histopathologic effects of metal pollution on the fish fauna inhabiting Karasu River, Turkey

Figure 4. Pathological damages in brain of C. capoeta A) vascular dilatation (orange arrow), degeneration (red arrow) and dilatation (black arrow), B) vacuolization (yellow arrows) and infiltration (blue arrow) in the Purkinje layer, C) vascular dilatation with congestion (orange arrow) H & E.

Figure 1 in Determination of oxidative, genotoxic, and histopathologic effects of metal pollution on the fish fauna inhabiting Karasu River, Turkey

Figure 1. Sampling stations; Station 1: Dumlu, Station 2: Ilıca, Station 3: Aşkale I, Station 4: Aşkale II.

Figure 2 in Determination of oxidative, genotoxic, and histopathologic effects of metal pollution on the fish fauna inhabiting Karasu River, Turkey

Figure 2. The erythrocytic nuclear abnormalities determined in the three fish species A) micronucleus (arrow) in C. capoeta, B) notched nucleus (arrow) in C. capoeta, C) binucleated nucleus (arrow) in A. mossulensis, D) kidney shaped nucleus (black arrows) and bud nucleus (red arrow) in A. mossulensis, E) lobed nucleus (arrow) in S. cephalus, F) Bud nucleus (arrow) in S. cephalus Giemsa.