Design of novel adsorption processes for the removal of arsenic from polluted groundwater employing functionalized magnetic nanoparticles

For many developing countries, groundwater is the main source for water consumption in rural and urban areas. The occurrence of arsenic in groundwater is an environmental problem due to its high toxicity. Although the removal of arsenic by different technologies has been proven, adsorption is one of the best alternatives due to its simplicity and low cost. In particular, nanoadsorbents incorporating magnetic properties are promising separation agents because of their advantageous and efficient potential recovery in a magnetic field, characteristic that is very attractive and of utmost relevance in the development of low cost technologies to provide drinking water in developing countries. In this work, Fe3O4 and Fe3O4/SiO2 magnetic nanoparticles functionalized by amino derivatives coordinated with Fe3+ were synthesized and characterized and further evaluated as adsorbents to remove arsenate from groundwater. The adsorption equilibrium of As5+ was satisfactorily described at 298 K by the Langmuir model with the following parameters: a) Fe3O4: qm=20.4±0.3 mg g-1 and KL=0.373±0.003 L mg-1 and b) Fe3O4/SiO2: qm=121±4.1 mg g-1 and KL=0.383±0.066 L mg-1. At low arsenate concentrations, 50-1000 µg L-1, the adsorption equilibrium As5+-Fe3O4/SiO2 was described by linear isotherms with equilibrium parameters KH=278.8 L g-1 in monocomponent systems and KH=1.80 L g-1 in the presence of competing ions, being carbonate and especially phosphate the main species affecting the process with contributions to the loss of efficacy around 70%. Finally, the material reuse after regeneration with NaOH 10-3 mol L-1 d was assessed under several composition scenarios reaching adsorption yields similar to those obtained with fresh materials.Financial support from the Spanish Ministry of Economy and Competitiveness under the project CTQ2012-31639 (FEDER 2007-2013) is gratefully acknowledged

Resistance of ion exchange membranes in aqueous mixtures of monovalent and divalent ions and the effect on reverse electrodialysis

Salinity gradient energy has gained attention in recent years as a renewable energy source, especially employing reverse electrodialysis technology (RED), which is based on the role of ion exchange membranes. In this context, many efforts have been developed by researchers from all over the world to advance the knowledge of this green source of energy. However, the influence of divalent ions on the performance of the technology has not been deeply studied. Basically, divalent ions are responsible for an increased membrane resistance and, therefore, for a decrease in voltage. This work focuses on the estimation of the resistance of the RED membrane working with water flows containing divalent ions, both theoretically by combining the one-thread model with the Donnan exclusion theory for the gel phase, as well as the experimental evaluation with Fumatech membranes FAS-50, FKS-50, FAS-PET-75, and FKS-PET-75. Furthermore, simulated results have been compared to data recently reported with different membranes. Besides, the influence of membrane resistance on the overall performance of reverse electrodialysis technology is evaluated to understand the impact of divalent ions in energy generation. Results reflect a minor effect of sulfate on the gross power in comparison to the effect of calcium and magnesium ions. Thus, this work takes a step forward in the knowledge of reverse electrodialysis technology and the extraction of salinity gradient energy by advancing the influence of divalent ions on energy recovery.The authors of this work would like to acknowledge the financial support from the LIFE program (LIFE19 ENV/ES/000143). The UC team wants to thank J.A. Abarca and F.J. Rodríguez-Oria for their help in impedance measurements and SGP-RED experiences. This work was also facilitated by REDstack BV in the Netherlands. REDstack BV aims to develop and market the ED and the RED technology. J.V. would like to thank his colleagues from the REDstack company for the fruitful discussions

ORIENTATE: automated machine learning classifiers for oral health prediction and research

© The Author(s) 2023. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/. This document is the Published version of a Published Work that appeared in final form in BMC Oral Health. To access the final edited and published work see https://doi.org/10.1186/s12903-023-03112-wBackground The application of data-driven methods is expected to play an increasingly important role in healthcare. However, a lack of personnel with the necessary skills to develop these models and interpret its output is preventing a wider adoption of these methods. To address this gap, we introduce and describe ORIENTATE, a software for automated application of machine learning classification algorithms by clinical practitioners lacking specific technical skills. ORIENTATE allows the selection of features and the target variable, then automatically generates a number of classification models and cross-validates them, finding the best model and evaluating it. It also implements a custom feature selection algorithm for systematic searches of the best combination of predictors for a given target variable. Finally, it outputs a comprehensive report with graphs that facilitates the explanation of the classification model results, using global interpretation methods, and an interface for the prediction of new input samples. Feature relevance and interaction plots provided by ORIENTATE allow to use it for statistical inference, which can replace and/or complement classical statistical studies. Results Its application to a dataset with healthy and special health care needs (SHCN) children, treated under deep sedation, was discussed as case study. On the example dataset, despite its small size, the feature selection algorithm found a set of features able to predict the need for a second sedation with a f1 score of 0.83 and a ROC (AUC) of 0.92. Eight predictive factors for both populations were found and ordered by the relevance assigned to them by the model. A discussion of how to derive inferences from the relevance and interaction plots and a comparison with a classical study is also provided. Conclusions ORIENTATE automatically finds suitable features and generates accurate classifiers which can be used in preventive tasks. In addition, researchers without specific skills on data methods can use it for the application of machine learning classification and as a complement to classical studies for inferential analysis of features. In the case study, a high prediction accuracy for a second sedation in SHCN children was achieved. The analysis of the relevance of the features showed that the number of teeth with pulpar treatments at the first sedation is a predictive factor for a second sedation

Global diagnosis of nitrate pollution in groundwater and review of removal technologies

Clean water and sanitation for the world population is one of the most important challenges established by the Sustainable Development Goals of the United Nations since worldwide, one in three people do not have access to safe drinking water. Groundwater, one of the main sources of fresh water, has been considerably damaged by human activities. Nevertheless, while numerous plants are globally aimed at removing pollutants from surface waters, a much scarcer number of facilities have focused on groundwater remediation. Nowadays, there is increasing concern about the presence of nitrates (NO3-) in groundwaters as a consequence of the intensive use of fertilizers and other anthropogenic sources, such as sewage or industrial wastewater discharge. In this context, the selection and development of highly effective and low-cost solutions for the sustainable management of groundwater resources need to be addressed. Thus, this work collects data from the literature regarding the presence of nitrates in groundwater, and, simultaneously, it reviews the main alternatives available to remove NO3- from groundwater sources. A total of 292 sites have been analyzed categorized by continents, carefully discussing the possible origins of nitrate pollution. In addition, a discussion is carried out of the different technologies currently employed to treat groundwater, highlighting the progress made and the main challenges to be overcome. Finally, the review gathers the data available in the literature for nitrate treatment plants at full-scale.This work has been conducted with financial support from the Spanish Ministry of Economy and Competitiveness (project RTI2018-093310-B100). This research is also being supported by the Project “HYLANTIC”-EAPA_204/2016, which is co-financed by the European Regional Development Fund in the framework of the Interreg Atlantic program. E. Abascal is supported by the Spanish Ministry of Science, Innovation and Universities through the grant PEJ2018-003323-A

Unravelling the mechanisms that drive the performance of photocatalytic hydrogen production

The increasing interest and applications of photocatalysis, namely hydrogen production, artificial photosynthesis, and water remediation and disinfection, still face several drawbacks that prevent this technology from being fully implemented at the industrial level. The need to improve the performance of photocatalytic processes and extend their potential working under visible light has boosted the synthesis of new and more efficient semiconductor materials. Thus far, semiconductor–semiconductor heterojunction is the most remarkable alternative. Not only are the characteristics of the new materials relevant to the process performance, but also a deep understanding of the charge transfer mechanisms and the relationship with the process variables and nature of the semiconductors. However, there are several different charge transfer mechanisms responsible for the activity of the composites regardless the synthesis materials. In fact, different mechanisms can be carried out for the same junction. Focusing primarily on the photocatalytic generation of hydrogen, the objective of this review is to unravel the charge transfer mechanisms after the in-depth analyses of already reported literature and establish the guidelines for future research.This research was funded by the Spanish Ministry of Science, Innovation, and Universities (grant numbers RTI2018-099407-B-I00 and RTI2018-093310-B-I00 MCIU/AEI/FEDER, UE)

Performance of continuous-flow micro-reactors with curved geometries. Experimental and numerical analysis

One of the major challenges in the design of micro-devices, when very fast reactions are carried out, is to overcome the limited performance due to the poor mixing efficiency of the reactants. Here, we report a holistic analysis of reactants mixing and reaction rate in liquid phase flow micro-reactors with curved geometries. In this sense, a mathematical model that accounts for momentum and mass conservation equations, together with species transport and chemical reaction rate under isothermal conditions, has been developed using computational fluid dynamics techniques (CFD). To validate the predictive model, four micro-reactor geometries with different radius and curved length (straight reactor, two types of serpentines and an Archimedean spiral) have been evaluated. Simulated results proved that mixing is promoted through the formation of Dean vortices as a consequence of the reduction of the radius of curvature and at the same time of the extension of the curve. Thus, the overall performance of the micro-reactor is improved because mass transport limitations are minimized and the process kinetics are greatly enhanced. Accordingly, the spiral micro-reactor reported the best performance by reducing by half the time required to obtain 95 % conversion when compared with the straight reactor. Simulated findings have been confirmed with the experimental analysis of the reaction between aqueous ammonium and hypochlorite ions. Very good agreement between simulated and experimental results has been achieved with an error lower than 10 %. Therefore, the robust model herein reported is a novel and valuable tool to assist in the optimum design of micro-reactors for fluid-phase isothermal applications.Financial assistance from the project RTI2018-093310-B-I00 (MCI/AEI/FEDER,UE) is gratefully acknowledged

Comparative performance of salinity gradient power-reverse electrodialysis under different operating conditions

Promotion of renewable energies to substitute carbon-based energy has boosted the development of new membrane technologies based on Salinity Gradient Power (SGP) by Reverse Electrodialysis (RED). This paper is focused on providing a useful, feasible and robust tool for the design of this technology, able to predict the behaviour under different operational conditions, critical for RED performance. Therefore, open circuit voltage (OCV), internal resistance (Ri) and gross power (P) are evaluated. Furthermore, the model predictability has been validated with experimental results obtained working with three cases of study corresponding to seawater/WWTP effluent, brines/brackish water and an intermediate concentration gradient scenario. Feed flow rate (Reynolds numbers from 2.7 to 13.6), and temperature (from 286 K to 297 K) have been also tested in a lab-scale set-up with 0.4 m2 of membrane area; the maximum power achieved at 297 ± 1 K was 0.66 W, 1.6 W and 0.3 W for the three cases respectively. The results highlight the strong influence of temperature and the dominance of the low compartment resistance on the process performance; thus, working with the highest possible SG does not always provide the best outcome, but a trade-off between SG and resistance of the dilute solution should be searched.Financial support from Community of Cantabria - Regional Plan for the project: Gradisal “RM16-XX-046-SODERCAN/FEDER” is gratefully acknowledged. Moreover, authors acknowledge Spanish Ministry of Economy and Competitiveness for the projects CTM2015-66078-R, CTM2014-57833-R and CTM2017-87850-R and Dr. Jordi Carrillo for his advice and technical support

Integration of chemical engineering skills in the curriculum of a master course in industrial engineering

Promoting new teaching methodologies is essential to improve the participation, motivation, interest, and results of students in all educational stages. In this sense, flipped classroom and problem-based learning have emerged in the last years as fascinating options to be implemented in high education levels thanks to the students’ maturity and previously acquired background. Working with motivating case studies based on real processes with their restrictions appears as an opportunity to bring future professionals closer to the industrial problems; this will capacitate engineers to solve and understand complex procedures getting tangible results. In this context, the main goal of this work is to combine flipped classroom and problem-based learning methodologies to gain the interest of students of a Master course in Industrial Engineering in the subject of Chemical Processes using real data of local companies. A survey, designed by the academics involved, will help collecting the opinion of students as well as the acquired skills in the frame of the specific subject. Results demonstrated the satisfaction of the students with the course, highlighting mainly the acquisition or improvement of self-learning skills (survey 4.0/5.0), capacity for organization and planning (survey 4.0/5.0), analytical ability (survey 4.2/5.0), and teamwork (survey 4.3/5.0). In addition, the grades accomplished during the year of implementation show that although the success rate is quite similar to preceding years, the marks achieved are considerably higher

Optimized copper-based microfeathers for glucose detection

Diabetes is expected to rise substantially by 2045, prompting extensive research into accessible glucose electrochemical sensors, especially those based on non-enzymatic materials. In this context, advancing the knowledge of stable metal-based compounds as alternatives to non-enzymatic sensors becomes a scientific challenge. Nonetheless, these materials have encountered difficulties in maintaining stable responses under physiological conditions. This work aims to advance knowledge related to the synthesis and characterization of copper-based electrodes for glucose detection. The microelectrode presented here exhibits a wide linear range and a sensitivity of 1009 µA∙cm−2∙mM−1, overperfoming the results reported in literature so far. This electrode material has also demonstrated outstanding results in terms of reproducibility, repeatability, and stability, thereby meeting ISO 15197:2015 standards. Our study guides future research on next-generation sensors that combine copper with other materials to enhance activity in neutral media.This research was funded by the Spanish Ministry of Science, Innovation, and Universities under the project PDC2022-133122-I00