Resultados de investigación

23 pages. -- PDF file includes: I. Literature Review on Electrical and Structural Properties of Ferroelectric Thin Film. -- II. Coercive Field in BaTiO3 Thin Films Grown on SrTiO3/Si Substrates. -- I. Structural Characterization of BaTiO3 Thin Films Grown at Different Pressures. -- II. Polarization-Electric Field Hysteresis Loops of BaTiO3 Films. -- III. Stoichiometry Characterization. -- IV. Study of Different Electrode Synthesis and Fabrication Processes. -- V. Scanning Transmission Electron Microscopy Studies. -- VI. Structural Characterization of 125-225 nm BaTiO3 Thin Films. -- VII. Reciprocal Space Mapping of 12.5-100 nm BaTiO3 Thin Films Grown at 60 mTorr. -- VIII. Polarization-Voltage/Electric Field Hysteresis Loops of BaTiO3 Thin Films. -- IX. Piezoresponse Force Microscopy Scans of 25-nm-thick BaTiO3 Thin Film. -- X. Fatigue and Retention Measurements. -- XI. Switching-Dynamics Measurements on 25 nm BaTiO3 Thin Films. -- XII. Lateral Size-Scaling on 50 nm and 100 nm BaTiO3 Thin Films. -- XIII. Structural Characterization of BaTiO3 Grown on SrTiO3/Si Substrates. -- XIV. Dielectric Constant-Voltage Measurement, Peer reviewed

14 pages. -- PDF file includes: Details of Theoretical calculations. -- Figure S1. (a) SEM images of the Bi2Se3 nanosheets. (b) XRD patterns of Bi2Se3 nanosheets. (c) HRTEM images of the Bi2Se3 nanosheets and its corresponding power spectrum. (d) EELS chemical composition maps obtained from the red squared area of the STEM micrograph. -- Figure S2. Bi 4f and Se 3d high-resolution XPS spectra. -- Figure S3. XRD pattern of I-Bi2Se3/S. -- Figure S4. TGA curve of I-Bi2Se3/S composite measured in N2 with a sulfur loading ratio of 70.2 wt%. -- Figure S5. Nitrogen adsorption-desorption isotherms of as synthesized I-Bi2Se3 and IBi2Se3/S composites. -- Figure S6. DFT calculation results of optimized geometrical configurations of the surface (110) of Bi2Se3 with LiPS (Li2S, Li2S2, Li2S4, Li2S6, Li2S8 and S8). -- Figure S7. DFT calculation results of optimized geometrical configurations of the surface (110) of I-Bi2Se3 with LiPS (Li2S, Li2S2, Li2S4, Li2S6, Li2S8 and S8). -- Figure S8. Optimized adsorption configuration of Li2S decomposition on Bi2Se3. -- Figure S9. First five cycles of CV curves of (a) I-Bi2Se3/S, (b) Bi2Se3/S and (c) Super P/S performed at a scan rate of 0.1 mV s−1. -- Figure S10. Differential CV curves of (a) I-Bi2Se3/S, (c) Bi2Se3/S and (e) Super P/S. The baseline voltage and current density are defined as the value before the redox peak, where the variation on current density is the smallest, named as dI/dV=0. -- Figure S11. CV curves of (a) Bi2Se3/S, (b) Super P/S and (c) Plot of CV peak current for peaks C1, C2, and A versus the square root of the scan rates. -- Figure S12. The CV curve of I-Bi2Se3 as electrode measured in symmetric coin cell using an electrolyte without Li2S6. -- Figure S13. (a) Charge, and (b) discharge profiles of I-Bi2Se3/S, Bi2Se3/S, and Super P/S electrodes showing the overpotentials for conversion between soluble LiPS and insoluble Li2S2/Li2S. -- Figure S14. Galvanostatic charge−discharge profiles of (a) Bi2Se3/S and (b) Super P/S at different current densities range from 0.1C to 4C. -- Figure S15. (a,b) EIS spectra of (a) Bi2Se3/S and (b) Super P/S coin cells before and after cycling. The solid line corresponding to the fitting result from the equivalent circuit (c) and (d), and the Rs, Rin, Rct, and Zw stand for the resistance of the electrolyte, insoluble Li2S2/Li2S layer, interfacial charge-transportation, and semi-infinite Warburg diffusion, respectively; and CPE stands for the corresponding capacitance. (e) Different resistances of three coin cells were obtained from the equivalent circuit. -- Figure S16. XRD patterns of electrode materials after 100 cycles at 1C. -- Figure S17. Galvanostatic charge/discharge profiles of I-Bi2Se3/S at 0.5C under a lean electrolyte condition with a high sulfur loading of 5.2 mg cm-2. -- Figure S18. (a) SEM image of the Li-anode after cycling; (b) EDX mapping image of Lianode showing sulfur signal after cycling. -- Figure S19. SEM image of the cathode material after cycling, EDX spectra and EDX elemental maps for S, Se, Bi and I. -- Figure S20. I-Bi2Se3 optimized configuration as calculated by DFT. The distance between I and Bi is 3.15 Å, which is similar values than the bond lengths in bulk BiI3. -- Table S1 Summary of the comparison of I-Bi2Se3 electrochemical performance as host cathode for LSBs with state-of-the-art Bi-based or Se-based materials., Peer reviewed

18 pages. -- PDF file includes: materials and methods: Production of biotinylated detection antibodies (bd-MAb). -- Figure S-1. MP functionalization with c-MAb. -- Figure S-2. S-2. Scheme and protocol of the reference sandwich ELISA for Pf-LDH detection. -- Figure S-3. Two-step magneto-immunoassay for Pf-LDH detection. -- Figure S-4. Single-step magneto-immunoassay for Pf-LDH detection: comparison of colorimetric and fluorescent detection. -- Figure S-5. Performance of the 9 membranes studied. -- Figure S-6. Optimization of MP washing on-chip. -- Figure S-7. Optimization of the paper-based detection strategy. -- Figure S-8. Detection of malaria in positive blood samples using a commercial RDT. -- Table S-1. Examples of magneto immunoassays reported before for malaria diagnosis. -- Table S-2. Characteristics of the 9 paper-like membranes tested. -- Table S-3. Summary of results obtained in malaria-positive clinical samples using the paperbased fluorescence POC and reference ELISA, microscopy and RDT methodologies. -- Table S-4. Estimated production cost for each paper-based magneto-immunoassay test., Peer reviewed

4 pages. -- PDF includes: Interaction between AuNPs and CCM supplements. -- Figure SI-1A shows the results for the exposition of AuNPs to a solution of PhR. -- Figure SI1-D shows the UV-vis profile over time of AuNP exposed to PS (100 U/mL (~170 μM) penicillin, 172 μM streptomycin)., Sodium citrate-stabilized gold nanoparticles (AuNPs) are destabilized when dispersed in cell culture media (CCMs). This may promote their aggregation and subsequent sedimentation, or under the proper conditions, their interaction with dispersed proteins can lead to the formation of a NP-stabilizing protein corona. CCMs are ionic solutions that contain growth substances which are typically supplemented, in addition to serum, with different substances such as dyes, antioxidants, and antibiotics. In this study, the impact of phenol red, penicillin–streptomycin, l-glutamine, and β-mercaptoethanol on the formation of the NP–protein corona in CCMs was investigated. Similar protein coronas were obtained except in the presence of antibiotics. Under these conditions, the protein corona took more time to be formed, and its density and composition were altered, as indicated by UV–vis spectroscopy, Z potential, dynamic light scattering, and liquid chromatography–mass spectrometry analyses. As a consequence of these modifications, a significantly different AuNP cellular uptake was measured, showing that NP uptake increased as did the NP aggregate formation. AuNP uptake studies performed in the presence of clathrin- and caveolin-mediated endocytosis inhibitors showed that neither clathrin receptors nor lipid rafts were significantly involved in the internalization mechanism. These results suggest that in these conditions, NP aggregation is the main mechanism responsible for their cellular uptake., Peer reviewed

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

[Description of methods used for collection/generation of data] For the study, environmental data were measured by sensors located in both the water column and the benthic compartment (at 4 m and 8 m, respectively). Temperature, salinity and dissolved oxygen (DO) from the water column were measured from October 2019 to October 2021 by a sensor attached to the mooring line. Data were recorded with a CT SBE37 (Conductivity, Temperature) sensor (SBE37SMP-ODO-RS232, Sea-Bird Scientific©) coupled with an SBE 63 (Sea-Bird Scientific©) dissolved oxygen (DO) sensor with accuracies of ± 0.002 °C for temperature, ± 0.002 mS cm-1 for conductivity and ± 2 % for DO. Measurements were taken with a resolution of 0.0001 ºC for temperature, 0.0001 mS cm−-1 for conductivity and 0.2 µmol kg-1 for DO. Multiparametric Hydrolab HL4 probes (OTT HydroMet) were deployed during 8 different periods covering all seasons following the procedure by Hendriks et al. (2021). Accuracy for the multiparametric probe sensors is ± 0.10 ºC for temperature and ± 0.5 % of reading + 0.001 mS cm−1 for conductivity, with resolutions of 0.01 ºC and 0.001 mS cm-−1, respectively. The DO sensor presents an accuracy of ± 0.1 mg L−1 for values lower than 8 mg L−1, and ± 0.2 mg L−1 for values higher than 8 mg L−1, and a resolution of 0.01 mg L−1. Two benthic chambers were installed during May and July 2021 using a design previously described in Barrón et al. (2006). MiniDOT sensors (PME, Inc. ©) were used for temperature and DO measurements every 15 minutes, with accuracies of ± 0.1 ºC and ± 5 %, respectively. DO sensor data were validated against water samples analysed with the Winkler method.. Three chamber replicates were installed during each deployment. Wind speed (m s−1) values at Cabrera NP Station were obtained from data provided by the Organismo Autónomo de Parques Nacionales (OAPN, Spain). For the benthic chambers, night respiration was estimated from changes in DO between one hour after sunset and one hour before sunrise. The same procedure was followed for the calculation of the net community production (NCP) during daylight hours, and the two values were summed for GPP. NCP was used along with the total meadow area coverage and residence time of water in Sta. María Bay to determine the total O2 exported by the meadow to the water column. For the metabolic rate calculation, only oxygen data from the first 24 hours were used., [Methods for processing the data] Seasonal variations in the metabolic rates were analysed with a one-way ANOVA test using the Statistics and Machine Learning ToolboxTM in Matlab® (https://mathworks.com). For this purpose, daily metabolic rates from water column sensors and multiparametric sensors were grouped by season . The same statistical analysis was performed to analyse disparities between sensors. Since benthic chamber data were only available for one day in May and one day in July, differences between deployments were tested using a Student t-test., readme provides background information for csv datafiles. Csv datafiles are processed data of oxygen concentrations used as input for the model, with a frequency of 10 minutes for hydrolab (HL) measurements and hourly for the CT measurements, and a frequency of 15 minutes for MiniDot measurements., The endemic angiosperm Posidonia oceanica plays a remarkable role as marine habitat and ecosystem service provider in shallow waters in the Mediterranean Basin through their vertical growth, oxygenation of the water column and as a carbon sink storing allochthonous carbon and biomass underneath the meadows. Here we assess the capacity of a pristine meadow at 8m depth in the PMNT Cabrera (Mallorca, Spain) to oxygenate the water column in the coastal area through monitoring of oxygen concentrations and subsequent evaluation of the metabolic rates from these profiles in the benthic as well as pelagic compartment. Here we report dissolved oxygen (DO) measurements from a CT at 4m dept with continuous (hourly) measurements from October 2019 to October 2021 as well as DO measurements from multiparameteric sensors in the meadow (8m depth) during some weeks in the same period and 2 evalautions of DO with closed incubations., Spanish Ministry of Science (SumaEco, RTI2018–095441-B-C21), the Government of the Balearic Islands through la Consellería d'Innovació, Recerca i Turisme (Projecte de recerca científica i tecnològica SEPPO, PRD2018/18), the Posi-COIN Project from the 2018 BBVA Foundation “Ayudas a equipos de investigación científica” call. STARTER research project funded by the 2021 call of the Càtedra de la Mar, Iberostar Foundation. This work is a contribution to CSIC's Thematic Interdisciplinary Platform PTI OCEANS+. The present research was carried out within the framework of the activities of the Spanish Government through the "Maria de Maeztu Centre of Excellence" accreditation to IMEDEA (CSIC-UIB) (CEX2021-001198)., With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2021-001198)., Peer reviewed

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2