OpenAIRE: Open Access Infrastructure for Research in Europe

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Archivo Abierto Institucional de la Universidad Carlos III de Madrid: OpenAIRE: Open Access Infrastructure for Research in Europe

OpenAIRE promueve el acceso abierto a los resultados de proyectos de investigación financiados por la Unión Europea (7PM, H2020, etc.), en virtud del cuál, los beneficiarios de dichos proyectos han de depositar los artículos producidos revisados por pares.

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Clustering and forecasting of day-ahead electricity supply curves using a market-based distance
(2024) Li, Zehang; Alonso Fernández, Andrés Modesto; Elías, Antonio; Morales, Juan M.; Universidad Carlos III de Madrid. Departamento de Estadística; European Commission; Ministerio de Ciencia e Innovación (España); European Commission
Gathering knowledge of supply curves in electricity markets is critical to both energy producers and regulators. Indeed, power producers strategically plan their generation of electricity considering various scenarios to maximize profit, leveraging the characteristics of these curves. For their part, regulators need to forecast the supply curves to monitor the market’s performance and identify market distortions. However, the prevailing approaches in the technical literature for analyzing, clustering, and predicting these curves are based on structural assumptions that electricity supply curves do not satisfy in practice, namely, boundedness and smoothness. Furthermore, any attempt to satisfactorily cluster the supply curves observed in a market must take into account the market’s specific features. Against this background, this article introduces a hierarchical clustering method based on a novel weighted-distance that is specially tailored to non bounded and non-smooth supply curves and embeds information on the price distribution of offers, thus overcoming the drawbacks of conventional clustering techniques. Once the clusters have been obtained, a supervised classification procedure is used to characterize them as a function of relevant market variables. Additionally, the proposed distance is used in a learning procedure by which explanatory information is exploited to forecast the supply curves in a day-ahead electricity market. This procedure combines the idea of nearest neighbors with a machine-learning method. The prediction performance of our proposal is extensively evaluated and compared against two nearest-neighbor benchmarks and existing competing methods. To this end, supply curves from the markets of Spain, Pennsylvania-New Jersey-Maryland (PJM), and West Australia are considered.
Baseline pupil size does not correlate with fluid intelligence: a laboratory study with children and adults
(2024-03) Lorente Labrado, Patricia; Ruuskanen, Veera; Mathôt, Sebastiaan; Crespo, Antonio; Radl, Jonas; European Commission
Recent studies have investigated resting-state, or baseline, pupil size as a general measure of cognitive abilities, based on the previous finding that larger pupils might be predictive of higher general intelligence or working memory capacity. However, evidence for such relationships has been mixed, and all previous studies thus far have focused on adult samples. The present study adds to this debate by examining the correlation between fluid intelligence and baseline pupil size in a sample of both children (10 years old) and adults (their parents). Importantly, our sample is representative in terms of socioeconomic background, which was not the case in previous studies, thus addressing concerns about sample selection and variability. We did not find evidence for a relationship of fluid intelligence with baseline pupil size or with pupil-size variability, neither for children nor adults. Therefore, our results do not replicate the relationship between cognitive abilities and baseline pupil size as reported in previous research.
HEFactory: A symbolic execution compiler for privacy-preserving Deep Learning with Homomorphic Encryption
(Elsevier, 2023-05-01) Cabrero Holgueras, José; Pastrana Portillo, Sergio; Comunidad de Madrid; European Commission; Ministerio de Economía y Competitividad (España)
Homomorphic Encryption (HE) allows computing operations on encrypted data, and it is a potential solution to enable Deep Learning (DL) in privacy-enforcing scenarios (e.g., sending private data to cloud services). However, HE remains a complex technology with multiple challenges that prevent successful application by non-experts. In this work, we present HEFactory, a program compiler that effectively assists in building HE applications in Python for both general-purpose and Deep Learning applications, focusing on non-expert data scientists. HEFactory relies on a layered architecture that deals with challenges such as automatic parameter selection and specific data representation of HE applications. Our benchmarks show that HEFactory substantially lowers the programming complexity (i.e., a reduction of 80% in the number of lines of code) with negligible performance overhead over programs written by experts using native HE frameworks.
On Futuring Body Perception Transformation Technologies: Roles, Goals and Values
(Association For Computing Machinery (ACM), 2023-10-03) Turmo Vidal, Laia; Vega Cebrián, José Manuel; D'Adamo, Amar; Roel Lesur, Marte Ernesto; Dehshibi, Mohammad Mahdi; Díaz Durán, Joaquín Roberto; Tajadura Jiménez, Ana; European Commission
Body perception transformation technologies augment or alter our own body perception outside of our usual bodily experience. As emerging technologies, research on these technologies is limited to proofs-of-concept and lab studies. Consequently, their potential impact on the way we perceive and experience our bodies in everyday contexts is not yet well understood. Through a speculative design inquiry, our multidisciplinary team envisioned utopian and dystopian technology visions. We surfaced potential roles, goals and values that current and future body perception transformation technologies could incorporate, including non-utilitarian purposes. We contribute insights on such roles, goals and values to inspire current and future work. We also present three provocations to stimulate discussions. Finally, we contribute methodologically with insights into the value of speculative design as a fruitful approach for articulating and bridging diverse perspectives in multidisciplinary teams.
Sustainable conditions for waste tires recycling through gasification in a bubbling fluidized bed
(Elsevier, 2023-08-20) Batuecas Fernández, Esperanza; Serrano García, Daniel; Horvat, Alen; Abelha, Pedro; European Commission
Gasification in a bubbling fluidized bed reactor was introduced as sustainable technology to treat waste such as tires. However, uncertainty arises when defining the most sustainable gasification process conditions. In this paper, eight experimental conditions were analyzed. The experiments were carried out at 700 and 850 °C with different equivalence ratios (ER) while using air as a gasifying agent. At 850 °C the effect of steam addition was also studied. A Life Cycle Assessment (LCA) was assessed to the product gas and its energy content by the following environmental impacts: Climate Change (CC), Ozone Depletion (OD), and Particulate Matter (PM). LCA methodology revealed that optimal gasification conditions provide 74, 69 and 66% environmental impact reductions in CC, PM, and OD, respectively by comparing the best and the worst-case scenarios. The combination of high temperature, low ER, and steam addition offered the most environmentally sustainable process, achieving 8.3·10−2 kg CO2 eq/MJ in CC, 1.3·10−8 kg CFC-11eq/MJ in OD and 4.9·10−5 kg PM2.5eq/MJ in PM. Despite the great savings in environmental impacts, additional efforts are still needed to reduce the energy consumption of the preheating system to ensure the product gas is levelled to conventional natural gas.
Exascale programing models for extreme data processing
(IEEE, 2023-05-09) García Blas, Francisco Javier; Carretero Pérez, Jesús; European Commission
Extreme Data is an incarnation of Big Data concept distinguished by the massive amounts of data that must be queried, communicated and analysed in (near) real-time by using a very large number of memory/storage elements and Exascale computing systems. Immediate examples are the scientific data produced at a rate of hundreds of gigabits-per-second that must be stored, filtered and analysed, the millions of images per day that must be mined/analyzed in parallel, the one billion of social data posts queried in real-time on an in-memory components database. Traditional disks or commercial storage cannot handle nowadays the extreme scale of such application data. Following the need of improvement of current concepts and technologies, ASPIDE's activities focus on data-intensive applications running on systems composed of up to millions of computing elements (Exascale systems). Practical results will include the methodology and software prototypes that will be designed and used to implement Exascale applications. The ASPIDE project contributed with the definition of a new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on Exascale systems, which can pave the way for the exploitation of massive parallelism over a simplified model of the system architecture, promoting high performance and efficiency, and offering powerful operations and mechanisms for processing extreme data sources at high speed and/or real-time. This work is continued now in the ADMIRE EuroHPC project.
Deep Neural Network-Based QoT Estimation for SMF and FMF Links
(IEEE, 2023-03-15) Amirabadi, Mohammad Ali; Kahaei, Mohammad Hossein; Nezamalhosseini, S. Alireza; Arpanaei, Farhad; Carena, Andrea; European Commission
Quality of transmission (QoT) estimation tools for fiber links are the enabler for the deployment of reconfigurable optical networks. To dynamically set up lightpaths based on traffic request, a centralized controller must base decisions on reliable performance predictions. QoT estimation methods can be categorised in three classes: exact analytical models which provide accurate results with heavy computations, approximate formulas that require less computations but deliver a reduced accuracy, and machine learning (ML)-based methods which potentially have high accuracy with low complexity. To operate an optical network in real-time, beside accurate QoT estimation, the speed in delivering results is a strict requirement. Based on this, only the last two categories are candidates for this application. In this paper, we present a deep neural network (DNN) structure for QoT estimation considering both regular single-mode fiber (SMF) and future few-mode fiber (FMF) proposed to increase the overall network capacity. We comprehensively explore ML-based regression methods for estimating generalized signal-to-noise ratio (GSNR) in partial-load SMF and FMF links. Synthetic datasets have been generated using the enhanced Gaussian noise (EGN) model. Results indicate that the proposed DNN-based regressor can provide better accuracy along with less computation complexity, compared with other state-of-the-art ML methods as well as closed-form-EGN and closed-form-GN models
Ultra-Wideband Multi-Octave Planar Interconnect for Multi-Band THz Communications
(Springer, 2023-08-01) Iwamatsu, Shuya; Ali, Muhsin; Fernandez Estevez, Jose Luis; Tebart, Jonas; Kumar, Ashish; Makhlouf, Sumer; Carpintero del Barrio, Guillermo; Stohr, Andreas; European Commission
An ultra-wideband (UWB) interconnect technology using indium phosphide (InP)-based transitions for coupling the output signals from terahertz (THz) photodiodes featuring coplanar waveguide (CPW) outputs to low-loss dielectric rod waveguides (DRWs) is presented. The motivation is to exploit the full bandwidth offered by THz photodiodes without limitations due to standard rectangular waveguide interfaces, e.g., for future high data rate THz communications. Full electromagnetic wave simulations are carried out to optimize the electrical performance of the proposed InP transitions in terms of operational bandwidth and coupling efficiency. The transitions are fabricated on 100-µm-thin InP and integrated with silicon (Si) DRWs. Experimental frequency domain characterizations demonstrate efficient THz signal coupling with a maximum coupling efficiency better than - 2 dB. The measured 3-dB and 6-dB operational bandwidths of 185 GHz and 280 GHz, respectively, prove the multi-octave ultra-wideband features of the developed interconnect technology. The 6-dB operational bandwidth covers all waveguide bands between WR-12 to WR-3, i.e., a frequency range between 60 and 340 GHz. In addition, the multi-octave performances of the fabricated interconnects were successfully exploited in proof-of-concept THz communication experiments. Using intermediate frequency orthogonal frequency division multiplexing (OFDM), THz communications are demonstrated for several frequency bands using the same interconnect. Considering soft-decision forward error correction, error-free transmission with data rates of 24 Gbps at 80 GHz and 8 Gbps at 310 GHz is achieved
On-Chip Terahertz antenna array based on amalgamation of metasurface-inspired and artificial magnetic conductor technologies for next generation of wireless electronic devices
(Published by Elsevier GmbH., 2023-07-01) Alibakhshikenari, Mohammad; Virdee, Bal Singh; Salekzamankhani, Shahram; Babaeian, Fatemeh; Ali, Syed Mansoor; Iqbal, Amjad; Al-Hasan, Muath; European Commission
The paper presents a feasibility study on an innovative terahertz (THz) on-chip antenna array designed to reliably meet the high-performance connectivity requirements for next generation of wireless devices to enable bandwidth intensive applications, superfast fast streaming, bulk data exchange between internet of things (IoT) devices/smartphones and the development of holographic video conferencing. The significantly smaller wavelength of the THz-band and metasurface-inspired and artificial magnetic conductor (AMC) technologies are exploited here to realize an on-chip antenna. Several experimental on-chip antenna arrays of various matrix sizes were investigated for application at millimeter-wave/Terahertz RF front-end transceivers. The technique proposed here is shown to enhance the antennas impedance bandwidth, gain and radiation efficiency. Purely for experimental purposes a 2 × 24 radiation element array was fabricated. It exhibits an average measured gain of 20.36 dBi and radiation efficiency of 37.5% across 0.3-0.314 THz. For proof of the concept purposes a THz receiver incorporating the proposed on-chip antenna was modelled. The results show that with the proposed antenna array a THz receiver can provide a gain of 25 dB when the antenna is directly matched to low-noise amplifier stage.
All-solid-state sodium-ion batteries operating at room temperature based on NASICON-type NaTi2(PO4)3 cathode and ceramic NASICON solid electrolyte: A complete in situ synchrotron X-ray study
(Elsevier, 2023-09-15) Pandit, Bidhan; Johansen, Morten; Andersen, Bettina P.; Martínez Cisneros, Cynthia Susana; Levenfeld Laredo, Belén; Ravnbaek, Dorthe B.; Varez, Alejandro; Agencia Estatal de Investigación (España); Comunidad de Madrid; European Commission
All-solid-state sodium-ion batteries that work at ambient temperature are a potential approach for large-scale energy storage systems. Nowadays, ceramic solid electrolytes are gaining attention because of their good ionic conductivity and excellent mechanical and chemical stabilities. Furthermore, a good interface between electrode and solid electrolyte is also required to achieve successful cell performances. In this work, sintered ceramic layer electrolyte Na3.16Zr1.84Y0.16Si2PO12, with high ionic conductivity (0.202 mS/cm at room temperature), are prepared by using uniaxial pressing followed by a sintering process. The conductive carbon coated NASICON material (NaTi2(PO4)3/C) exhibits, as cathode material, enhanced rate capability and stability for sodium ion batteries for high carbon (18.95 %) coated sample. At C/10, the optimized cathode (with higher carbon content) achieves a remarkable initial discharge capacity of 107.3 mAh/g (reversible capacity of 101.4 mAh/g), a sufficient rate capability up to a rate of 10C, and a long cycle life (capacity retention of 58% after 950 cycles). The one-stage reversible biphasic reaction mechanism and potential-dependent structure–property of NaTi2(PO4)3 can be explained by employing in situ X-ray synchrotron method. Sequential Rietveld refinements of the in situ data show the evolution of the Na-poor NaTi2(PO4)3 and Na-rich Na3Ti2(PO4)3 phase fractions (wt%), unit cell characteristics, and unit cell volume. The design of an all-solid-state sodium ion half-cell with a NaTi2(PO4)3/C cathode and a Na3.16Zr1.84Y0.16Si2PO12 solid-state electrolyte interface results in stable capacity of 83.6 mAh/g at C/10 and excellent reversible capacity at high C-rate. The results show that sintered NASICON-based electrolytes can significantly contribute for the fabrication of all-solid-state sodium-ion battery due to the superior conductivity and stability.
Artificial Intelligence in Government: Risks and Challenges of Algorithmic Governance in the Administrative State
(Indiana University Press, 2023-01-01) Vida Fernández, José; European Commission; Ministerio de Ciencia e Innovación (España)
This article analyzes the legal implications of using artificial intelligence in government and how it is challenging the foundations of the administrative state. It begins by demonstrating that a new model of government is emerging, based on information and intelligence (i-Gov). To understand the nature and scope of this new i-Gov model, this article will explain what artificial intelligence really is and analyze the applications that are currently being carried out in the US and the EU. Next, it will review the regulatory framework that is emerging that regulates government use of artificial intelligence in both the US and the EU. Finally, the article concludes by identifying and analyzing the main legal and policy problems involved in the use of artificial intelligence in government. It challenges values, principles, and institutions of the traditional administrative state and also requires us to think of new frameworks for constitutional and administrative law to guarantee citizens' rights and public interest.
Enhanced confinement induced by pellet injection in the stellarator TJ-II
(2023-07-01) Garcia Cortes, Maria Isabel; Mccarthy, K. J.; Estrada, T.; Tribaldos Macía, Víctor; Medina-Roque, D.; Van Milligen, B.; Ascasibar, E.; Carrasco, R.; Chmyga, A. A.; Garcia, R.; Hernandez Sanchez, J.; Hidalgo, C.; Kozachek, A. S.; Medina, F.; Ochando, M.A.; De Pablos, J.L.; Panadero, N.; Pastor, I.; European Commission; Ministerio de Economía y Competitividad (España)
Enhanced confinement is observed in neutral beam injector (NBI)-heated hydrogen discharges made in the stellarator TJ-II after the injection of a single cryogenic fuel pellet into the plasma core. In addition to the expected increase in electron density, ne, in the core after pellet injection (PI), the plasma diamagnetic energy content is seen to rise, with respect to similar discharges without PI, by up to 40%. Furthermore, the energy confinement time, sE diag, as determined using a diamagnetic loop, is enhanced when compared to predictions obtained using the International Stellarator Scaling law [H. Yamada et al., Nucl. Fusion 45, 1684 (2005)] and the triple product, ne _ Ti _ sE diag, exhibits a clear bifurcation point toward an improved confinement branch as compared to the branch product predicted by this scaling law. In general, once such a pellet-induced enhanced confinement (PiEC) phase has been established, it is characterized by steepened radial density gradients, by more negative plasma potential in the core, more negative radial electric fields, Er, across a broad plasma region, as well as by reductions in density and plasma potential fluctuations in the density gradient region. In addition, experimental observations show increased peaking of core radiation losses, this pointing to edge/core plasma decoupling. In parallel, neoclassical simulations of reference and PiEC plasmas predict increased particle and energy confinement times during a PiEC phase together with a more negative Er profile. Qualitative rather than quantitative agreement with experimental parameters is found, indicating that turbulence seems to play a significant role here. In summary, single cryogenic pellet injection facilitates the achievement of an enhanced operational regime that was previously not observed in NBIheated discharges of the TJ-II.
Online verification in Cyber-physical Systems: Practical bounds for meaningful temporal costs
(John Wiley and Sons, 2018-03-01) Bersani, Marcello M.; García Valls, María Soledad; European Commission; Ministerio de Economía y Competitividad (España); Ministerio de Educación (España)
Cyber-physical systems (CPS) are highly dynamic and large scale systems integrated with the physical environment that they monitor and actuate on. Given the changing nature of physical environments, CPS have to adapt on-line to new situations while preserving their correct operation. This means that the system model may have to change or, at least, will have to be modified during its operation life, preserving correctness. Correctness by construction relies on using formal tools, which suffer from a considerable computational overhead. As the current system model of a CPS may adapt to the environment, the new system model must be verified before its execution to ensure that the properties are preserved. However, CPS development has mainly concentrated on the design-time aspects, existing only few contributions that address their on-line adaptation. We design a framework for managing dynamic changes of a system with a core entity that is an autonomic manager; we investigate the pros and cons of using formal tools within this framework to guarantee that the system properties are met at all times and across changes.We formalize the semantics of the adaptation logic of an autonomic manager (OLIVE) that performs on-line verification for a specific application, a dynamic virtualized server system. The on-line verification manager services requests from mobile clients that might require a change in both the running software components and services executed by the server. We explore the use of formal tools based on CLTLoc to express functional and non-functional properties of the system. In this scenario, we provide empirical results showing the temporal costs of our approach.
Fast Marching Techniques for Teaming UAV's Applications in Complex Terrain
(MDPI, 2023-02-01) Garrido Bullón, Luis Santiago; Muñoz Mendi, Javier; López Palomino, Blanca; Quevedo Vallejo, Fernando; Monje Micharet, Concepción Alicia; Moreno Lorente, Luis Enrique; European Commission
In this paper, we present a study on coverage missions carried out by UAV formations in 3D environments. These missions are designed to be applied in tracking and search and rescue missions, especially in the case of accidents. In this manner, the presented method focuses on the path planning stage, the objective of which is to compute a convenient trajectory to completely cover a certain area in a determined environment. The methodology followed uses a Gaussian mixture to approximate a probability of containment distribution along with the Fast Marching Square (FM2) as path planner. The Gaussians permit to define a zigzag trajectory that optimizes the path. Next, a first 2D geometric path perpendicular to the Voronoi diagram of the Gaussian distribution is calculated, obtained by skeletonization. To this path, the height above the ground is added plus the desired flight height to make it 3D. Finally, the FM2 method for formations is applied to make the path smooth and safe enough to be followed by UAVs. The simulation experiments show that the proposed method achieves good results for the zigzag path in terms of smoothness, safety and distance to cover the desired area through the formation of UAVs.
Towards Unsupervised Knowledge Extraction
(Ceur-Ws.Org Team, 2021-03-22) Tsatsou, Dorothea; Karangeorgos, Konstantinos; Dimou, Anastasios; Carbó Rubiera, Javier Ignacio; Molina López, José Manuel; Daras, Petros; European Commission
Integration of symbolic and sub-symbolic approaches is rapidly emerging as an Artificial Intelligence (AI) paradigm. This paper presents a proof-of-concept approach towards an unsupervised learning method, based on Restricted Boltzmann Machines (RBMs), for extracting semantic associations among prominent entities within data. Validation of the approach is performed in two datasets that connect language and vision, namely Visual Genome and GQA. A methodology to formally structure the extracted knowledge for subsequent use through reasoning engines is also offered.
Critical quantum metrology in fully-connected models: From Heisenberg to Kibble-Zurek scaling
(IOP Publishing, 2022-07-01) Garbe, Louis; Abah, Obinna; Felicetti, Simone; Puebla Antunes, Ricardo; European Commission
Phase transitions represent a compelling tool for classical and quantum sensing applications. It has been demonstrated that quantum sensors can in principle saturate the Heisenberg scaling, the ultimate precision bound allowed by quantum mechanics, in the limit of large probe number and long measurement time. Due to the critical slowing down, the protocol duration time is of utmost relevance in critical quantum metrology. However, how the long-time limit is reached remains in general an open question. So far, only two dichotomic approaches have been considered, based on either static or dynamical properties of critical quantum systems. Here, we provide a comprehensive analysis of the scaling of the quantum Fisher information for different families of protocols that create a continuous connection between static and dynamical approaches. In particular, we consider fully-connected models, a broad class of quantum critical systems of high experimental relevance. Our analysis unveils the existence of universal precision-scaling regimes. These regimes remain valid even for finite-time protocols and finite-size systems. We also frame these results in a general theoretical perspective, by deriving a precision bound for arbitrary time-dependent quadratic Hamiltonians.
Review: High Speed Temperature Measurements Under Dynamic Loading
(Springer, 2024-01-24) Goviazin, Gleb Gil; Nieto Fuentes, Juan Carlos; Rittel, Daniel; European Commission
Background This review discusses high-speed thermal measurements and their significance in understanding solid materials' behavior, focusing on rapid loading conditions. Objective While high-speed thermal measurements are challenging in some cases, these measurements provide unique insights into material response at high rates, by shedding light on failure modes, thermomechanical coupling, and thermal dissipation phenomena that are otherwise overlooked. Methods The review presents various direct measurement techniques (contact and non-contact) relevant to high-speed load- ing, with emphasis on the frequently used ones in mechanics of materials applications: thermocouples, infrared detectors, and high-speed infrared cameras. Results Pros and cons of each technique, alongside with typical applications are discussed. Understanding the interplay between thermal effects and mechanical responses opens new avenues for enhancing material performance and energy efficiency. Conclusions This review is expected to serve as a valuable resource for researchers and practitioners seeking to leverage high-speed thermal measurements to drive innovation and advance materials science in various applications.
On a dislocation-based constitutive model and dynamic thermomechanical considerations
(Elsevier, 2018-09-01) Nieto Fuentes, Juan Carlos; Rittel, Daniel; Osovski, S.; European Commission
Dislocation-based constitutive models are widely used to predict the plastic behavior of metallic materials, in both quasi-static and dynamic conditions. In addition, if the ratio of (adiabatic) thermomechanical (plastic work to heat) conversion is known, the stress-strain-temperature relationship can be estimated. The main purpose of this study was to verify the applicability of a widely-used expression (where the strain energy of a plastically deformed material is proportional to the density of dislocations) to calculate the stored energy in the material, which can be used in parallel with the micromechanical model to estimate the temperature rise during dynamic plastic deformation. An experimental campaign, where Kolsky (split Hopkinson) pressure bar tests were combined with in situ infrared temperature measurements, was conducted on OFHC copper compression specimens. The analytical thermomechanical conversion was compared with the experimental one, revealing a significant discrepancy between the two. An empirical ad hoc factor was introduced in the analytical expression in order to describe adequately the thermomechanical response of the material under dynamic (impact) loading conditions.
Active Cellulose Acetate/Chitosan Composite Films Prepared Using Solution Blow Spinning: Structure and Electrokinetic Properties
(2023-08-02) Kramar, Ana; Luxbacher, Thomas; Moshfeghi Far, Nasrin; González Benito, Francisco Javier; European Commission; Ministerio de Ciencia e Innovación (España)
Cellulose acetate (CA), a very promising derivative of cellulose, has come into the focus of research due to its highly desired good film-forming ability for food packaging applications. Frequently, this derivative is used in combination with other compounds (polymers, nanoparticles) in order to obtain active materials. Here, we report the preparation of thin films made of cellulose acetate loaded with chitosan (CS) using the solution blow spinning (SBS) method. Films are prepared by SBS processing of the polymers mixture solution, considering the following variables: (i) the concentration of cellulose acetate and chitosan in the solution and (ii) the solvent system consisting of acetic or formic acid. The prepared materials are characterized in terms of physical properties, roughness (optical profilometer), porosity, wettability (contact angle measurements), chemical structure (Fourier transform infrared spectrometry), and electrokinetic properties (zeta potential). SBS enables the preparation of CA/CS films with high water vapor permeability, high porosity, and also higher water contact angle compared with pure CA films. The electrokinetic properties of composites are influenced by the inclusion of chitosan, which causes a shift of the isoelectric point (IEP) towards higher pH values, but the magnitude of the shift is not in correlation with chitosan concentration. Adsorption kinetic studies using bovine serum albumin (BSA) as a model protein reveal that chitosan modified cellulose acetate films manifest low affinity towards proteins that suggests prevention of biofilm formation on its surface.
Probing the dynamics of a superradiant quantum phase transition with a single trapped ion
(American Physical Society, 2017-02-17) Puebla Antunes, Ricardo; Hwang, Myung-Joong; Casanova, Jorge; Plenio, Martin B.; European Commission
We demonstrate that the quantum phase transition (QPT) of the Rabi model and critical dynamics near the QPT can be probed in the setup of a single trapped ion. We first demonstrate that there exists equilibrium and nonequilibrium scaling functions of the Rabi model by finding a proper rescaling of the system parameters and observables, and show that those scaling functions are representative of the universality class to which the Rabi model belongs. We then propose a scheme that can faithfully realize the Rabi model in the limit of a large ratio of the effective atomic transition frequency to the oscillator frequency using a single trapped ion and, therefore, the QPT. It is demonstrated that the predicted universal functions can indeed be observed based on our scheme. Finally, the effects of realistic noise sources on probing the universal functions in experiments are examined.

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