Resultados de investigación

UV–Vis absorption spectroelectrochemistry has been selected as a suitable multiresponse technique to determine the fluoroquinolone ofloxacin in urine samples without any previous pretreatment. Due to the modification of the working electrode surface during the oxidation of this antibiotic, the electrode has to be replaced or polished between measurements. A new spectroelectrochemistry cell has been developed, which allows in a simple way to perform reproducible experiments with different screen-printed electrodes. The new cell has been used to perform analysis in a complex matrix such as urine. Due to the trilinear character of the spectroelectrochemical data, PARAFAC has been used to determine ofloxacin with very good figures of merit, demonstrating the capability of trilinear methods to avoid the influence of some interfering compounds., Ministerio de Ciencia e Innovación and Agencia Estatal de Investigación (Grant numbers: MCIN/AEI/10.13039/501100011033, PID2020-113154RB-C21), Ministerio de Ciencia, Innovación y Universidades (Grant number: RED2022-134120-T), Junta de Castilla y León and European Regional Development Fund (Grant number: BU036P23) are gratefully acknowledged for funding this work. F.O. acknowledges Junta de Castilla y León and European Social Found for his predoctoral contract.

The dataset contains all raw data of the work "SEM-EDS and hyperspectral images of vine leafs treated with antifungal products", This dataset is part of the project TED2021-131551B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR

The dataset contains data used in the work "Fractionation and refining of pectic oligosaccharides derived from onion skins through continuous feed diafiltration", published in Journal of Membrane Science 708, 2024, 123054, This work was supported by the Agencia Estatal de Investigación [grant numbers PID2020-116716RJ-I00, PDC2022-133443-I00, PID2022-136385OB-I00] and the Junta de Castilla y León (JCyL) and the European Regional Development Fund (ERDF) [grant number BU027P23]. Benito Román post-doctoral contract was funded by the Agencia Estatal de Investigación through project PID2020–116716RJ-I00.

The dataset contains data used in the work "Biorefinery approach for the development of an integrated extraction and purification process for the recovery of pectin derived compounds and flavonoids from onion peels", This work was supported by the Agencia Estatal de Investigación (AEI) through projects PID2020-116716RJ-I00/ AEI / 10.13039/501100011033, PID2019-104950RB-I00 / AEI / 10.13039/501100011033 and TED2021-129311B-I00.

This dataset provides multivariate time series data from sensors monitoring a cutting fluid (taladrina) test tank, collected as part of the "SmarTaladrine" project. The primary monitored variables are pH, Temperature, Concentration, and Conductivity. The data spans from December 17, 2024, to April 1, 2025. The dataset includes a raw, preprocessed version of these four variables, which contains missing values as originally observed. Additionally, for each of the four primary variables, separate files are provided showcasing the results of imputing these missing values using five different methods: a pre-trained MOMENT model, a fine-tuned MOMENT model, an LSTM-based Variational Autoencoder (LSTM-VAE), K-Nearest Neighbors (KNN), and a hybrid KNN-Clustering method (HybridKCL). This dataset is intended for developing and evaluating models for cutting fluid analysis, anomaly detection, predictive maintenance, and for benchmarking time series imputation techniques within industrial machining contexts., This work was supported by the project "Solución innovadora, embebida en máquina o auxiliar, para la eco gestión inteligente de taladrinas en operaciones de mecanizado (SmarTaladrine)", funded by the Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia, del Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023, within the framework of the Plan de Recuperación, Transformación y Resiliencia (Spain).

The data set is a set of txt files with the fictitious instaces used in the paper "Efficient assignment of flight levels: A variable neighborhood search approach", The authors are grateful for the following entities, projects, and grants: The Spanish Research Agency and European Union (Project PID2022-139543OB-C44) and Regional Government of Castilla y León and FEDER founds (BU021G24).

The data set is made up of the four data collection instruments that were used for the evaluation of the music programme designed for Early Childhood Education students. These are: the CER questionnaire for the evaluation of musical expression, attentional processes and social skills together with an Inventory of Signs, and for the evaluation of the programme, the CEP and CEF questionnaires.

The dataset contains the data generated by an individual SIR model in an IoT network simulated by a graph for 20 time steps. This dataset is designed for training graph-based AI models for malware propagation detection in IoT networks., This publication is part of the AI4SECIoT project ("Artificial Intelligence for Securing IoT Devices"), funded by the National Cybersecurity Institute (INCIBE), derived from a collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of Burgos. This initiative is carried out within the framework of the Recovery, Transformation and Resilience Plan funds, financed by the European Union (Next Generation), the project of the Government of Spain that outlines the roadmap for the modernization of the Spanish economy, the recovery of economic growth and job creation, for solid, inclusive and resilient economic reconstruction after the COVID19 crisis, and to respond to the challenges of the next decade.

The dataset contains all raw data of the work "Intrinsically Colored Magenta Aramids with Improved Mechanical Performance", This work was supported by the Regional Government of Castilla y León (Junta de Castilla y León), the Ministry of Science and Innovation MICIN and the European Union NextGenerationEU/PRTR, and the Spanish Agencia Estatal de Investigación (State Research Agency). Author M. Trigo-López received grant PID2019-108583RJ-I00 funded by MCIN/AEI/ 10.13039/501100011033. Marta-Guembe García received funding from Ministerio de Universidades-European Union in the frame of NextGenerationEU RD 289/2021 (2021/00002/001/001) by the Universidad de Burgos. Author S. Vallejos received Grant PID2023-147301OB-I00 funded by MICIU/AEI /10.13039/501100011033 and FEDER, EU, and grant BG22/00086 funded by Spanish Ministerio de Universidades.

Raman spectroscopy is a powerful analysis technique that shows its full potential when a high amplification of the Raman signal is achieved. In this sense, Surface-Enhanced Raman scattering (SERS) has been the most widely used phenomenon for analysis. SERS provides the amplification of the Raman intensity due to the interaction of molecules with a plasmonic nanostructured surface. The enhancement of the Raman signal can be also obtained during the electrochemical oxidation of a metal electrode; this phenomenon was denoted as Electrochemical-Surface Oxidation Enhanced Raman Scattering (EC-SOERS) and yields a good Raman signal enhancement with high reproducibility. Until now, only chloride and bromide have been employed in EC-SOERS, using a silver electrode to generate silver chloride and silver bromide nanocrystals. In this work, a new EC-SOERS substrate based on the electrogeneration of silver hexacyanoferrate (II) nanocrystals is presented which provides a very sensitive Raman response. The electrogeneration of this new material can be easily followed using spectroelectrochemistry since the characteristic Raman bands of the nanocrystals lie outside of the fingerprint region used for the analysis where the detection of most of the target molecules is performed. Indigo Carmine has been selected as target molecule, obtaining a very good response at nanomolar level under Raman resonance and nonresonance conditions., Open Access funding provided thanks to the CRUE-CSIC agreement with Elsevier B.V. Authors acknowledge the financial support from Ministerio de Ciencia e Innovacion and Agencia Estatal de Investigacion (MCIN/AEI/10.13039/501100011033, PID2020–113154RB-C21), and Ministerio de Ciencia, Innovacion y Universidades (RED2022–134120-T). S.H. thanks JCyL and European Social Fund for her postdoctoral fellowship and M.P.-E. thanks JCyL and European Social Fund for his predoctoral fellowship. W.Ch. acknowledges Junta de Castilla y Leon for his postdoctoral fellowship (Grant BU297P18) and funding received from European Union’s Horizon 2020 research and innovation program under the Marie Sk?odowska-Curie grant agreement No 101031622.