Resultados de investigación

You can cite all versions by using the DOI 10.5281/zenodo.14051487. This DOI represents all versions, and will always resolve to the latest one., (selected variables from file version data_sediment_20231019) These datasets support the scientific article " White is a new shade of blue carbon: traditional salt production ponds are net carbon sinks" (submitted). It contains detailed data on the data used to estimate carbon sequestration rates and stocks in a saltpond in South Portugal. The datasets are accompanied by analysis code, available at GitHub repository, allowing for reproducibility and further exploration of the data. The data is composed by 3 datasets with the following variables: data_cores.csv. Contains properties related to the sampling of the sediment cores. core_id [character] - unique core identification code used in the field. tank_code [character] - tank identification where the core were sampled. replicate [character] - replicate number of the core in each pond. core_depth [numeric] - depth sampled with the core (in centimeters). sample_length_lab [numeric] - length of the sampled core measured in the laboratory (in centimeters). compaction_correction_factor [numeric] - fraction of the sample depth interval reduced due to compaction. It is calculated by dividing the core length by the core depth. compaction [numeric] - core compaction in percentage (%). It is calculated as 100*(1 - compaction_correction_factor). data_samples.csv. Contains properties of the sediment samples. tank_code [character] - tank identification where the core were sampled. replicate [character] - replicate number of the core in each pond. core_id [character] - unique core identification code used in the field. sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample). depth_middle [numeric] - middle depth of a sampling increment, calculating as the average of depth_min and depth_max (in centimeters). Corrected for compaction. depth_min [numeric] - minimum depth of a sampling increment, corrected for compaction (in centimeters). Corrected for compaction. depth_max [numeric] - maximum depth of a sampling increment, corrected for compaction (in centimeters). Corrected for compaction. sample_volume [numeric] - volume of the sediment sample, corrected for compaction (in cubic centimeters). Corrected for compaction. sample_dw [numeric] - dry mass of the sample (in grams of dry weight). percentage_organic_matter [numeric] - mass of organic matter relative to sample dry mass, obtained by loss-on-ignition (in percentage of dry weight). percentage_total_carbon [numeric] - mass of total carbon relative to sample dry mass, obtained by CHN analysis (as a percentage of dry weight). percentage_inorganic_carbon [numeric] - mass of inorganic carbon relative to sample dry mass, obtained by CHN analysis in samples after being subjected to LOI (as a percentage of dry weight). percentage_organic_carbon [numeric] - mass of organic carbon relative to sample dry mass, obtained by the difference of the percentage total organic carbon and percentage inorganic carbon contents (as a percentage of dry weight). dry_bulk_density [numeric] - dry mass per unit volume of the sample. This is calculated as the sample_dw divided by the sample_dw (in grams of dry weight per cubic centimeter). data_rates.csv. Contains calculation of the etiamted sedimentation rate. core_id [character] - unique core identification code used in the field. tank_code [character] - tank identification where the core were sampled. replicate [character] - replicate number of the core in each pond. compaction_correction_factor [numeric] - fraction of the sample depth interval reduced due to compaction. It is calculated by dividing the core length by the core depth. om_layer [numeric] - depth of the organic matter (OM)-rich layer (corrected for compaction), in centimeters. sample_length_lab [numeric] - length of the sampled core measured in the laboratory (in centimeters). age [numeric] - estimated minimum age of the pond, in years. sar [numeric] - sediment accumulation rate for the OM-rich layer, in millimieters per year. depth49 [numeric] - depth of the sediment with an age of 49 years, which is the time since the last main reconfiguration of the ponds, in centimeters., Peer reviewed

You can cite all versions by using the DOI 10.5281/zenodo.14051487. This DOI represents all versions, and will always resolve to the latest one., These datasets support the scientific article " White is a new shade of blue carbon: traditional salt production ponds are net carbon sinks" (submitted). It contains detailed data on the data used to estimate sedimentary organic carbon sequestration rates, organic carbon stocks, and carbon dioxide and methane fluxes in a saltpond in South Portugal. The datasets are accompanied by analysis code, available at GitHub repository, allowing for reproducibility and further exploration of the data. The data is composed by 7 datasets with the following variables: data_cores.csv. Contains properties related to the sampling of the sediment cores. core_id [character] - unique core identification code used in the field. tank_code [character] - tank identification where the core were sampled. replicate [character] - replicate number of the core in each pond. core_depth [numeric] - depth sampled with the core (in centimeters). sample_length_lab [numeric] - length of the sampled core measured in the laboratory (in centimeters). compaction_correction_factor [numeric] - fraction of the sample depth interval reduced due to compaction. It is calculated by dividing the core length by the core depth. compaction [numeric] - core compaction in percentage (%). It is calculated as 100*(1 - compaction_correction_factor). data_samples.csv. Contains properties of the sediment samples. tank_code [character] - tank identification where the core were sampled. replicate [character] - replicate number of the core in each pond. core_id [character] - unique core identification code used in the field. sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample). depth_middle [numeric] - middle depth of a sampling increment, calculating as the average of depth_min and depth_max (in centimeters). Corrected for compaction. depth_min [numeric] - minimum depth of a sampling increment, corrected for compaction (in centimeters). Corrected for compaction. depth_max [numeric] - maximum depth of a sampling increment, corrected for compaction (in centimeters). Corrected for compaction. sample_volume [numeric] - volume of the sediment sample, corrected for compaction (in cubic centimeters). Corrected for compaction. sample_dw [numeric] - dry mass of the sample (in grams of dry weight). percentage_organic_matter [numeric] - mass of organic matter relative to sample dry mass, obtained by loss-on-ignition (in percentage of dry weight). percentage_total_carbon [numeric] - mass of total carbon relative to sample dry mass, obtained by CHN analysis (as a percentage of dry weight). percentage_inorganic_carbon [numeric] - mass of inorganic carbon relative to sample dry mass, obtained by CHN analysis in samples after being subjected to LOI (as a percentage of dry weight). percentage_organic_carbon [numeric] - mass of organic carbon relative to sample dry mass, obtained by the difference of the percentage total organic carbon and percentage inorganic carbon contents (as a percentage of dry weight). dry_bulk_density [numeric] - dry mass per unit volume of the sample. This is calculated as the sample_dw divided by the sample_dw (in grams of dry weight per cubic centimeter). data_rates.csv. Contains calculation of the etiamted sedimentation rate. core_id [character] - unique core identification code used in the field. tank_code [character] - tank identification where the core were sampled. replicate [character] - replicate number of the core in each pond. compaction_correction_factor [numeric] - fraction of the sample depth interval reduced due to compaction. It is calculated by dividing the core length by the core depth. om_layer [numeric] - depth of the organic matter (OM)-rich layer (corrected for compaction), in centimeters. sample_length_lab [numeric] - length of the sampled core measured in the laboratory (in centimeters). age [numeric] - estimated minimum age of the pond, in years. sar [numeric] - sediment accumulation rate for the OM-rich layer, in millimieters per year. depth49 [numeric] - depth of the sediment with an age of 49 years, which is the time since the last main reconfiguration of the ponds, in centimeters. data_fluxes_individual.csv season [factor] - sampling season. site [factor] - sampling site. day and time [daytime] - sampling date and time, as DDMMYYYY and HH:MM. water temperature [numeric] - temperature of water in the ponds, in Celsius degree. dissolved oxygen [numeric] - dissolved oxygen in the water of the ponds, in mg L-1. wind velocity [numeric] - wind speed recorded at 5 meters above water surface of the ponds, in meters s-1. pH [numeric] - pH of water in the ponds, no units. salinity [numeric] - salinity of water in the ponds, in practical salinity units (PSU). individual CH4 rate [numeric] - methane flux rate, in umol CH4 m-2 h-1. individual CO2 rate [numeric] - carbon dioxide flux rate, in umol CO2 m-2 h-1. total CO2-eq rate [numeric] - CO2 equivalent flux rate, obtained by multiplying CH4 flux rate by 28 and summed to the CO2 flux rate, in umol CO2-eq m-2 h-1. minutes from 00:00 [numeric] - minutes from start of the first sampling day. delta time minutes [numeric] - delta time, obtained as the difference in minutes between consecutive incubations, in minutes. Last value is calculated to complete a full day. delta time hours [numeric] - delta time, calculated by dividing delta minutes by 60, in hours. rate CH4 for time integration [numeric] - rate of integration of CH4, calculated as the average of consecutive individual CH4 rates, in umol CH4 m-2 h-1. rate CO2 for time integration [numeric] - rate of integration of CO2, calculated as the average of consecutive individual CO2 rates, in umol CO2 m-2 h-1. CH4 integration rate over delta time [numeric] - CH4 flux rate integrated over the incubation time, obtained by multiplying the rate CH4 for time integration by delta time (hours), in umol CH4 m-2. CO2 integration rate over delta time [numeric] - CO2 flux rate integrated over the incubation time, obtained by multiplying the rate CO2 for time integration by delta time (hours), in umol CO2 m-2. data_fluxes_daily_seasonal.csv season [factor] - sampling season. site [factor] - sampling site. daily CH4 flux rate [numeric] - CH4 flux rate integrated over the day, obtained by summing CH4 integration rate over delta time, in mmol CH4 m-2 day-1. daily CO2 flux rate [numeric] - CO2 flux rate integrated over the day, obtained by summing CO2 integration rate over delta time, in mmol CO2 m-2 day-1. daily CH4 flux rate [numeric] - CH4 flux rate integrated over the day converted to CO2-eq by multipling by 28, in mmol CO2-eq m-2 day-1. daily total flux rate [numeric] - total flux rate integrated over the day, obained by summing daily CH4 as CO2-eq and daily CO2 flux rate, in mmol CO2-eq m-2 day-1. season duration [numeric] - number of days of each season, in days. seasonal CH4 flux rate [numeric] - CH4 flux rate integrated over the season, in mmol CH4 m-2 season-1. seasonal CO2 flux rate [numeric] - CO2 flux rate integrated over the season, in mmol CO2 m-2 season-1. seasonal CH4 flux rate [numeric] - CH4 flux rate integrated over the season converted to CO2-eq by multipling by 28, in mmol CO2-eq m-2 season-1. seasonal total flux rate [numeric] - total flux rate integrated over season, obtained by summing seasonal CH4 flux rate as CO2-eq with seasonal CO2 flux rate, in mmol CO2-eq m-2 season-1. seasonal total flux rate [numeric] - total flux rate integrated over the season, calculated by multiplying by 44.01, in g CO2-eq m-2 season-1. data_rates_net.csv origin [factor] - origin of the value (site 1, 2 and 3). annual total flux rate [numeric] - total flux rate integrated over the year, calculated by summing seasonal total flux rates for each site, in g CO2-eq m-2 year-1. annual burial rate [numeric] - burial rate in the sediment, in g CO2-eq m-2 year-1. annual net rate [numeric] - burial rate in the sediment, in g CO2-eq m-2 year-1. data_rates_annual.csv annual CH4 flux rate [numeric] - average CH4 flux rate integrated over the year, calculated as the sum of seasonal CH4 flux rates, in mmol CH4 m-2 year-1. annual CO2 flux rate [numeric] - average CO2 flux rate integrated over the year, calculated as the sum of seasonal CO2 flux rates, in mmol CH4 m-2 year-1. annual total flux rate [numeric] - total flux rate integrated over year, calculated by multiplying annual CH4 flux rate by 28 and summing to the annual CO2 flux rate, mmol CO2-eq m-2 year-1. annual total flux rate [numeric] - total flux rate integrated over year, calculated by summing seasonal total flux rates, in g CO2-eq m-2 year-1. annual total flux rate [numeric] - total flux rate integrated over year, obtained by multiplying the total flux rate by the area of the saltern, in Mg CO2-eq year-1., Peer reviewed

Calculation of distance-dependent junction relaxations, extraction of vibrational energies, theoretical description of CO tip vibrations, vibrational displacement patterns., No

Understanding different aspects of time is at the core of many areas in theoretical physics. Minimal models of continuous stochastic and quantum clocks have been proposed to explore fundamental limitations on the performance of timekeeping devices. Owing to the level of complexity in the clock structure and its energy consumption, such devices show trade-offs whose characterization remains an open challenge. Indeed, even conceptual designs for thermodynamically efficient quantum clocks are not yet well understood. In condensed matter theory, time-crystals were found as an exciting new phase of matter, featuring oscillations in (pseudo)-equilibrium with first experimental observations appearing recently. This naturally prompts the question: can time crystals be used as quantum clocks and what is their performance from a thermodynamic perspective? We answer this question and find that quantum time crystals are indeed genuine quantum clocks with a performance enhanced by the spontaneous breaking of time-translation symmetry., Peer reviewed

Description of the XPS and SPM equipment; description of the EC-UHV setup, the cell cleaning and emersion experiment; estimation of the OER onset; details of the CV sample conditioning; comparison of the Au 4f signal between emersion experiments; estimation of the amount of impurities across experiments; STM characterization after OCP and sample rinsing; OCP evolution of Au oxide; clean Au(111) STM/nc-AFM/LEED reference; detail of clean Au on Au oxide; thick Auδ+ oxide STM/nc-AFM/LEED reference; estimation of the Auδ+ reduction rate in UHV; comparison of Au3+ reduction in UHV and water; effect of air exposure on Auδ+; and LEED detail of Auδ+ oxide after thermal reduction., Peer reviewed

[Methods] - Numerical integration of non-causal integro-differential equations. - Monte Carlo simulations of escape trajectories with Langevin dynamics. - Data analysis to extract pertinent information., The dataset captures rare trajectories in the relaxation dynamics of the mean-field disordered 3-spin spherical model below the glass transition. It includes numerical integration of the mean-field correlation–response equations Monte Carlo simulations of rare escape trajectories and a static large-deviation calculation using a three-replica potential., Peer reviewed

The Supplemental Material provides additional details on the first-principles calculations, BornOppenheimer energy surface mapping, ARPES surface state simulations, electron localization analysis, Fermi surface nesting, and topological quantum chemistry analysis. It further includes crystallographic data, extended phonon and electronic band structure results, and a systematic analysis of the NbOX2 family of compounds., Peer reviewed

6 files, A Solvent-Topology Perspective on Hydrophobic Aggregation - DATA, Peer reviewed

1 file, Adaptive laboratory evolution (ALE) is a powerful tool for understanding and controlling the evolutionary trajectories of microorganisms. The scope of applications extends widely, including areas such as: biotechnology, synthetic biology, microbial ecology, and fundamental evolutionary research. In this work, we systematically explore the implementation and advantages of mini-bioreactors, defined as reactors with working volumes below 0.5 L, in ALE experiments. Mini-bioreactors offer substantial improvements over traditional large-scale reactors, including: reduced costs, enhanced parallelization capabilities, customizable configurations, and ease of automation. Through the utilization of illustrative case studies, which facilitate a comparative and critical evaluation of: batch, chemostat, turbidostat, and morbidostat operational modes, this review underscores the distinct capabilities of mini-bioreactors in enabling precise, dynamic control of evolutionary pressures. The novelty of this review lies in its comprehensive synthesis of recent advancements in mini-bioreactor technologies and operational strategies, particularly emphasizing innovations, such as: integrated automation, advanced sensors, and novel control algorithms adapted or specially designed for ALE. The ultimate objective is to provide both novices and experienced researchers with an updated, in-depth resource that addresses current technological limitations and future directions of mini-bioreactors in ALE. Previous literature reviews have discussed ALE and mini-bioreactors separately but have not explored in detail the use of mini-bioreactors in the context of ALE. Earlier works either partially addressed the topic or focused narrowly on specific aspects, missing the breadth of mini-bioreactor applications in ALE. In addition, recent advancements in the design and use of mini-bioreactors for ALE experiments emphasize the need for a comprehensive review. This article provides an up-to-date overview of mini-bioreactors in ALE, discussing their fundamentals, characteristics, modes of operation, and future perspectives, with the aim of filling a significant knowledge gap in the field, This work received funding from PID2022-136817OB-I00, AEI/10.13039/501100011033/FEDER, UE and Xunta de Galicia IN606A-2020/028 and IN607B 2021/11, Peer reviewed

We describe the Usadel formalism and numerical implementation used to simulate the diffusive superconductor tube structures. We also develop a number of analytical approximations useful to clarify aspects of the numerical solutions., No