Recent Advances in Mesoporous Materials and Their Biomedical Applications

Since the beginning of civilization, porous materials have been used for medical purposes. Some studies have reported that the first uses of applications of porous materials were carried out by the Ancient Egyptian or Western Africans, where porous charcoal or clay minerals were used as antidiarrheal medicine [1,2]. The use of charcoal continued to be used for medical purposes throughout history. Indeed, the Hindi civilization used charcoal for the purification of H2O [3]. More recently, the British Empire added charcoal to water barrels to increase the durability of drinking water [4]. Nowadays, charcoal is used as animal feed because it helps in the health and growth of the animals [5]. Regarding clay minerals, the use of kaolinite or montmorillonite is actually employed in some medicine such as Kaopectate® due to its good antidiarrheal applications [2,6]. 8 (...)This research was funded by the Spanish Ministry of Science and Innovation (PID2021-122736OB-C42), FEDER (European Union) funds (PID2021-122736OB-C42, P20-00375, UMA20-FEDERJA88). Partial funding for open access charge: Universidad de Málag

Cr-free Ni/MgO catalysts for hydrogenation of furfural

In the last century, the industrial development and the increase of the world population have caused the depletion of fossil reserves. This fact together with others factors have led to the search of alternative. Biomass is emerging as a widely available source to produce energy and, excluding fossil fuels, is the only source that can provide liquid fuels and chemicals. Lignocellulose is formed by cellulose, hemicellulose, lignin and other extractable components. In the case of hemicellulose, its hydrolysis leads to the formation of xylans and pentosans, which after dehydration can give rise to furfural. The high interest for furfural is attributed to its chemical structure, which provide high reactivity, making it potentially interesting for the synthesis of a vast variety of high value-added chemicals. Two of these important chemicals are furfuryl alcohol (FOL) and 2-methylfuran (MF), can be synthesized through hydrogenation of furfural, either in liquid or vapor phase. FOL is mainly used for the production of thermostatic resins, intermediates in the manufacture of lysines, vitamin C and dispersing agents. Meanwhile, MF is used in the synthesis of pesticides, in the pharmaceutical or perfume industries. Industrially, copper chromite catalyst is used, although the toxicity associated to the presence of chromium species has prompted the search of Cr-free catalysts. Therefore, much attention is being paid to the development of more sustainable and environmentally friendly catalysts, among them, catalytic systems based on Cu or Ni have demonstrated to be active and selective towards the formation of FOL and MF. The dispersion of metalspecies and their interaction with the support are key parameters that affect the catalytic activity and stabilityof catalysts. The use of metal oxides as supports can allow to obtain highly active and stable catalytic systems, and the electronic density of metal sites can be modified. The present work is aimed at the synthesis by Ni/MgO catalysts and the evaluation of their catalytic performance in the gas phase hydrogenation of furfural, at atmospheric pressure. x-Ni_MgO catalysts have been tested in the furfural hydrogenation, attaining the full furfural conversion with the 0.20-Ni_MgO catalyst, after 5 h of TOS, at 190 °C, by feeding a 5% furfural solution in cyclopentyl methyl ether, at a constant flow of hydrogen of 10 ml min-1. In all cases, catalysts are highly selective to furan. The analysis of the influence of the reaction temperature has revealed the existence of a volcano distribution, attaining the best catalytic performance at 190 °C. However, all catalysts suffer a progressive deactivation with TOS, by deposition of reactants and product.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech Ministerio de Economía y Competitividad (Proyecto CTQ2015-64226-C3-3-R) Fondos FEDE

Supported nickel nitride catalysts for the gas-phase hydrogenation of furfural

A series of catalysts with different nickel loading (2.5-30 wt%) has been prepared by UGR. The preparation of Ni3N phase was ascertained via Powder X-rays diffraction together with cubic nickel . The elemental chemical analysis and XPS data confirm the presence of the nitride phase. Their catalytic performance points out that catalysts with loading of 5-10 wt% Ni exhibit a higher stability, maintaining furfural conversion values higher than 75% after 5 h of time-on-stream at 170ºC, and the main products detected were furfuryl alcohol (hydrogenation) and furan (decarbonylation). This would indicate that two types of active sites are present on the catalyst surface. It is noteworthy the high catalytic activity of this family of catalyst, since they exhibit a better performance than Cu-ZnO catalysts, but using a lower reaction temperature and H2/furfural molar ratio, as well as a higher furfural concentration and WHSV values. The experimental conditions have been optimized in order to achieve the maximum yield in the target product, but preserving a high activity and stability. The fresh and spent catalysts have been characterized in order to elucidate structure-activity-stability relationships.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Nanocátalysts for oxygen removal from biomass derived biofuel

The use of bio-energy as a renewable alternative to fossil fuels is nowadays attracting more and more attention. Bio-fuel from biomass seems to be a potential energy substitute for fossil fuels since it is a renewable resource that could contribute to sustainable development and global environmental preservation and it appears to have significant economic potential. Liquid fuels can be obtained from fast pyrolysis of lignocellulosic biomass, where fast pyrolysis is a promising route because the process takes place at moderate temperatures, in absence of air and with a short hot vapor residence time. However, these liquid fuels have poor quality due to their low volatility, high viscosity, low heating value, a high oxygen content and poor chemical stability. This high oxygen is due to the presence of oxygen-containing compounds such as alcohols, aldehydes, ketones, furans and phenols. In this sense, catalytic hydrodeoxygenation (HDO) is one the most efficient processes to remove oxygen from these liquid fuels. In this context, the catalyst design is of upmost importance to achieve a high degree of deoxygenation, and bifunctional catalysts are required to achieve high degrees of activity. Noble metal and non-noble metal based catalysts will be evaluated in HDO of model molecules in order to get further insight about the important role of the active phase. Transition metal phosphides have shown excellent catalytic performances due to their good hydrogen transfer properties that diminishes the amount of metal exposed, avoiding, as much as possible, the deactivation, and modifies the electronic density of the catalyst leading to solids that favors the HDO. In addition these phosphides show bifunctional catalytic properties (metallic sites for hydrogenation and acid sites for cracking, methyl transfer reaction, dehydration and isomerization).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms

[EN] Internet of Things (IoT) is becoming a new socioeconomic revolution in which data and immediacy are the main ingredients. IoT generates large datasets on a daily basis but it is currently considered as "dark data", i.e., data generated but never analyzed. The efficient analysis of this data is mandatory to create intelligent applications for the next generation of IoT applications that benefits society. Artificial Intelligence (AI) techniques are very well suited to identifying hidden patterns and correlations in this data deluge. In particular, clustering algorithms are of the utmost importance for performing exploratory data analysis to identify a set (a.k.a., cluster) of similar objects. Clustering algorithms are computationally heavy workloads and require to be executed on high-performance computing clusters, especially to deal with large datasets. This execution on HPC infrastructures is an energy hungry procedure with additional issues, such as high-latency communications or privacy. Edge computing is a paradigm to enable light-weight computations at the edge of the network that has been proposed recently to solve these issues. In this paper, we provide an in-depth analysis of emergent edge computing architectures that include low-power Graphics Processing Units (GPUs) to speed-up these workloads. Our analysis includes performance and power consumption figures of the latest Nvidia's AGX Xavier to compare the energy-performance ratio of these low-cost platforms with a high-performance cloud-based counterpart version. Three different clustering algorithms (i.e., k-means, Fuzzy Minimals (FM), and Fuzzy C-Means (FCM)) are designed to be optimally executed on edge and cloud platforms, showing a speed-up factor of up to 11x for the GPU code compared to sequential counterpart versions in the edge platforms and energy savings of up to 150% between the edge computing and HPC platforms.This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5 and by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18.Cecilia-Canales, JM.; Cano, J.; Morales-García, J.; Llanes, A.; Imbernón, B. (2020). Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms. Sensors. 20(21):1-19. https://doi.org/10.3390/s20216335S1192021Gebauer, H., Fleisch, E., Lamprecht, C., & Wortmann, F. (2020). Growth paths for overcoming the digitalization paradox. Business Horizons, 63(3), 313-323. doi:10.1016/j.bushor.2020.01.005Guillén, M. A., Llanes, A., Imbernón, B., Martínez-España, R., Bueno-Crespo, A., Cano, J.-C., & Cecilia, J. M. (2020). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing, 77(1), 818-840. doi:10.1007/s11227-020-03288-wWang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144-156. doi:10.1016/j.jmsy.2018.01.003Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), 179-188. doi:10.1007/s12525-015-0196-8Pramanik, M. I., Lau, R. Y. K., Demirkan, H., & Azad, M. A. K. (2017). Smart health: Big data enabled health paradigm within smart cities. Expert Systems with Applications, 87, 370-383. doi:10.1016/j.eswa.2017.06.027Weber, M., & Podnar Žarko, I. (2019). A Regulatory View on Smart City Services. Sensors, 19(2), 415. doi:10.3390/s19020415Ghosh, A., Chakraborty, D., & Law, A. (2018). Artificial intelligence in Internet of things. CAAI Transactions on Intelligence Technology, 3(4), 208-218. doi:10.1049/trit.2018.1008Monti, L., Vincenzi, M., Mirri, S., Pau, G., & Salomoni, P. (2020). RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning. Sensors, 20(19), 5583. doi:10.3390/s20195583Kumar, P., Sinha, K., Nere, N. K., Shin, Y., Ho, R., Mlinar, L. B., & Sheikh, A. Y. (2020). A machine learning framework for computationally expensive transient models. Scientific Reports, 10(1). doi:10.1038/s41598-020-67546-wMittal, S., & Vetter, J. S. (2015). A Survey of CPU-GPU Heterogeneous Computing Techniques. ACM Computing Surveys, 47(4), 1-35. doi:10.1145/2788396Singh, D., & Reddy, C. K. (2014). A survey on platforms for big data analytics. Journal of Big Data, 2(1). doi:10.1186/s40537-014-0008-6Khayyat, M., Elgendy, I. A., Muthanna, A., Alshahrani, A. S., Alharbi, S., & Koucheryavy, A. (2020). Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks. IEEE Access, 8, 137052-137062. doi:10.1109/access.2020.3011705Satyanarayanan, M. (2017). The Emergence of Edge Computing. Computer, 50(1), 30-39. doi:10.1109/mc.2017.9Capra, M., Peloso, R., Masera, G., Roch, M. R., & Martina, M. (2019). Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World. Future Internet, 11(4), 100. doi:10.3390/fi11040100Lu, H., Gu, C., Luo, F., Ding, W., & Liu, X. (2020). Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning. Future Generation Computer Systems, 102, 847-861. doi:10.1016/j.future.2019.07.019Mimmack, G. M., Mason, S. J., & Galpin, J. S. (2001). Choice of Distance Matrices in Cluster Analysis: Defining Regions. Journal of Climate, 14(12), 2790-2797. doi:10.1175/1520-0442(2001)0142.0.co;2Gimenez, C. (2006). Logistics integration processes in the food industry. International Journal of Physical Distribution & Logistics Management, 36(3), 231-249. doi:10.1108/09600030610661813Chang, P.-C., Liu, C.-H., & Fan, C.-Y. (2009). Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry. Knowledge-Based Systems, 22(5), 344-355. doi:10.1016/j.knosys.2009.02.005Zheng, B., Yoon, S. W., & Lam, S. S. (2014). Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications, 41(4), 1476-1482. doi:10.1016/j.eswa.2013.08.044Woodley, A., Tang, L.-X., Geva, S., Nayak, R., & Chappell, T. (2019). Parallel K-Tree: A multicore, multinode solution to extreme clustering. Future Generation Computer Systems, 99, 333-345. doi:10.1016/j.future.2018.09.038Kwedlo, W., & Czochanski, P. J. (2019). A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality. IEEE Access, 7, 42280-42297. doi:10.1109/access.2019.2907885Liu, B., He, S., He, D., Zhang, Y., & Guizani, M. (2019). A Spark-Based Parallel Fuzzy $c$ -Means Segmentation Algorithm for Agricultural Image Big Data. IEEE Access, 7, 42169-42180. doi:10.1109/access.2019.2907573Baydoun, M., Ghaziri, H., & Al-Husseini, M. (2018). CPU and GPU parallelized kernel K-means. The Journal of Supercomputing, 74(8), 3975-3998. doi:10.1007/s11227-018-2405-7Li, Y., Zhao, K., Chu, X., & Liu, J. (2013). Speeding up k-Means algorithm by GPUs. Journal of Computer and System Sciences, 79(2), 216-229. doi:10.1016/j.jcss.2012.05.004Cuomo, S., De Angelis, V., Farina, G., Marcellino, L., & Toraldo, G. (2019). A GPU-accelerated parallel K-means algorithm. Computers & Electrical Engineering, 75, 262-274. doi:10.1016/j.compeleceng.2017.12.002Al-Ayyoub, M., Abu-Dalo, A. M., Jararweh, Y., Jarrah, M., & Sa’d, M. A. (2015). A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation. The Journal of Supercomputing, 71(8), 3149-3162. doi:10.1007/s11227-015-1431-yAit Ali, N., Cherradi, B., El Abbassi, A., Bouattane, O., & Youssfi, M. (2018). GPU fuzzy c-means algorithm implementations: performance analysis on medical image segmentation. Multimedia Tools and Applications, 77(16), 21221-21243. doi:10.1007/s11042-017-5589-6Timón, I., Soto, J., Pérez-Sánchez, H., & Cecilia, J. M. (2016). Parallel implementation of fuzzy minimals clustering algorithm. Expert Systems with Applications, 48, 35-41. doi:10.1016/j.eswa.2015.11.011Cebrian, J. M., Imbernón, B., Soto, J., García, J. M., & Cecilia, J. M. (2020). High-throughput fuzzy clustering on heterogeneous architectures. Future Generation Computer Systems, 106, 401-411. doi:10.1016/j.future.2020.01.022Cecilia, J. M., Timon, I., Soto, J., Santa, J., Pereniguez, F., & Munoz, A. (2018). High-Throughput Infrastructure for Advanced ITS Services: A Case Study on Air Pollution Monitoring. IEEE Transactions on Intelligent Transportation Systems, 19(7), 2246-2257. doi:10.1109/tits.2018.2816741Sriramakrishnan, P., Kalaiselvi, T., & Rajeswaran, R. (2019). Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine. Biocybernetics and Biomedical Engineering, 39(2), 470-487. doi:10.1016/j.bbe.2019.02.002Fang, Y., Chen, Q., & Xiong, N. (2019). A multi-factor monitoring fault tolerance model based on a GPU cluster for big data processing. Information Sciences, 496, 300-316. doi:10.1016/j.ins.2018.04.053Rodriguez, M. Z., Comin, C. H., Casanova, D., Bruno, O. M., Amancio, D. R., Costa, L. da F., & Rodrigues, F. A. (2019). Clustering algorithms: A comparative approach. PLOS ONE, 14(1), e0210236. doi:10.1371/journal.pone.0210236Pandove, D., Goel, S., & Rani, R. (2018). Systematic Review of Clustering High-Dimensional and Large Datasets. ACM Transactions on Knowledge Discovery from Data, 12(2), 1-68. doi:10.1145/3132088Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203. doi:10.1016/0098-3004(84)90020-7Soto, J., Flores-Sintas, A., & Palarea-Albaladejo, J. (2008). Improving probabilities in a fuzzy clustering partition. Fuzzy Sets and Systems, 159(4), 406-421. doi:10.1016/j.fss.2007.08.016Kolen, J. F., & Hutcheson, T. (2002). Reducing the time complexity of the fuzzy c-means algorithm. IEEE Transactions on Fuzzy Systems, 10(2), 263-267. doi:10.1109/91.99512

Synthesis of type A zeolites from natural kaolinite for their application in CO2 capture processes

Climate change is the greatest environmental threat of the 21st century, with major economic, social and environmental consequences. The level of carbon dioxide (CO2) emissions has increased by 31%, therefore, both governments and the scientific community are taking steps to mitigate emissions into the atmosphere. The most economically sustainable method is the use of low cost adsorbents that perform a selective adsorption of CO2 with respect to other inert gases such as N2. Clay minerals are highly available materials on the planet, are a low cost raw material and have great versatility for various processes in the field of adsorption and catalysis. The present work describes the synthesis of type A zeolite from a hydrothermal process in basic medium using metacaolinite as a starting material. Several parameters such as temperature and time were modified to evaluate the relationship between the formation conditions of the zeolite and its CO2 adsorption capacity. Synthesized catalysts were characterized by X-ray diffraction (XRD), N2 adsorption-desorption at -196 ºC, nuclear magnetic resonance of solids (NMR) and infrared spectroscopy (IR). In addition, the absorption capacity of CO2 with type A zeolites has been evaluated, and all the results were compared with the commercial zeolites. With respect to the results obtained, it can be said that the bands obtained by IR for the synthesized Zeolites are similar to those of the commercial Zeolite. On the other hand, the NMR results show that the synthesized and commercial zeolite present the same chemical environment. Finally, the textural parameters corroborate that in all cases the surface area is low from 12 m2g-1 for kaolinite to 7 m2g-1 for commercial zeolite AUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Hydrogenation of furfural over supported Pd catalysts

Lignocellulosic biomass is the most abundant and economical non-fossil carbon source. Furthermore, it is not competitive with the food chain, coming from lignocellulosic wastes including agricultural and food processing, local urban solid and forestry wastes. However, these are made up of complex carbohydrates (mainly, cellulose and hemicellulose), which require to be broken down in their respective monomers. The hemicellulose is mainly composed of pentosans, which, after an initial hydrolysis step, are dehydrated to furfural. Furfural is an important platform molecule, since it has a wide range of applications, being considered the main chemical, aside from bioethanol, obtained from the sugar platform for the synthesis of chemicals, for plastics, agrochemical and pharmaceutical industries. In the present work, the hydrogenation of furfural in gas phase has been studied by using Pd as active phase, and different metal oxides as support, in order to elucidate the influence of the support on the catalytic performance. Furfural can be converted into chemicals with important applications in many different industrial fields. Thus, reduction of furfural can proceed through different pathways depending on the experimental conditions, where the nature of the catalysts plays a key role. In the case of Pd-based catalysts, the main products come from the decarbonylation of furfural.The catalytic results reveals that the nature of the support exerts an important influence on furfural conversion and yield. The highest conversion (92% after 5 h of TOS at 463 K) was attained with a Pd-SiO2 catalyst, with a furan yield of 70 mol%. This catalyst is the most selective to furan and a moderate deactivation is only observed after 5 h reaction. The catalytic performance demonstrates that decarbonylation reaction was the main pathway, although the formation of furfuryl alcohol and 2-methylfuran also suggests that the hydrogenation of the carbonyl group of furfural takes place.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Kaolinite-based zeolites synthesis and their application in CO2 capture processes

In light of the urgent need of reducing the atmospheric CO2 emissions, the use of low-cost adsorbents, that exhibit a high affinity and CO2 adsorption capacity, is a promising method from the economic and environmental point of view to separate CO2 from the flue gas emitted from large sources of emissions like power-fueled plants. Clay minerals are low-cost raw materials with high availability all over planet and great versatility in the fields of adsorption and catalysis processes. The present study pretends to elucidate the link between the reaction conditions during the synthesis of the zeolite from kaolinite and its CO2 adsorption capacity. For that purpose, the type A zeolite was synthesized via hydrothermal process in alkaline solution using metakaolinite as a starting material. The metakaolinite was obtained by calcination of kaolinite at 600 °C and some parameters such as temperature and synthesis time were modified to optimize the synthesis aiming for a high CO2 adsorption capacity adsorbent. Synthesized materials were characterized by X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), N2 adsorption–desorption at −196 °C and CO2 adsorption at 0 °C (up to 10 bars) isotherms and Nuclear Magnetic Resonance of solids (NMR). In addition, the adsorption capacity of CO2 was evaluated by means of CO2 adsorption–desorption isotherms at 25 °C up to atmospheric pressure. The obtained results indicated that synthesized zeolite 4A can be successfully prepared from natural kaolinite (via metakaolinization) at 100 °C for 48 h under alkaline conditions, showing chemical and physical properties similar to that of the commercial 4A zeolite.This research was funded by the Ministry of Science, Innovation and Universities (Spain), Grant Nos. RTI2018-099668-B-C22 and project UMA18-FEDERJA-126 and P20_00375 of Junta de Andalucía (Spain) and FEDER funds (European Union). We also thank to Conselho Nacional de Desenvolvimento Científico e Tecnol ́ogico (CNPq, Ministry of Science and Technology, Brazil) and CAPES/PrInt (Project 88887.311867/2018-00) (Ministry of Education, Brazil) for financial support. Funding for open access charge: Universidad de Málaga / CBU

Gas phase selective hydrogenation of furfural to furfuryl alcohol and 2-methylfuran over Cu-CeO2 coprecipitated catalysts

Furfural is an important chemical derived from lignocellulosic biomass, in particular from C5 sugars like xylose, and it is considered as a platform molecule of great potential for the synthesis of a broad spectrum of chemicals. In this sense, furfuryl alcohol and 2-methylfuran are two important chemicals which can be produced through furfural hydrogenation, either in liquid or vapor phase, although the latter is preferred because it can be carried out at atmospheric pressure. Industrially, a copper chromite catalyst is used, although this catalyst can become very toxic due to the presence of chromium. Therefore, much attention is being paid to the development of chromium-free catalysts, more sustainable and environmentally friendly, as those based on Cu or Ni which are active and selective towards the formation of furfuryl alcohol and 2-methylfuran. Furfuryl alcohol is mainly used for the production of thermostatic resins, intermediate in the manufacture of lysine, vitamin C and dispersing agents. Meanwhile, 2-methyl furan is used in the synthesis of pesticides, or in the pharmaceutical and fragrance industries. The aim of this work is the synthesis of a series of copper based catalysts, which have been synthesized by coprecipitation of copper and cerium(IV) and subsequent thermal programmed reduction. This method allows increasing the dispersion of Cu particles, while the use of a support like CeO2 can modify the electronic density of the active phase, which can influence the catalytic activity and resistance to deactivation.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Economy and Competitiveness Ministry (Project CTQ2012-38204-C03-02), Junta de Andalucía (Project: RNM-1565) and FEDER funds of the European Unio