OpenAIRE

Permanent URI for this collectionhttp://hdl.handle.net/10084/89004

Kolekce určená pro sklízení infrastrukturou OpenAIRE; obsahuje otevřeně přístupné publikace, případně další publikace, které jsou výsledkem projektů rámcových programů Evropské komise (7. RP, H2020, Horizon Europe).

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Now showing 1 - 20 out of 5129 results

MXene and polyaniline coated 3D-printed carbon electrode for asymmetric supercapacitor
(Taylor & Francis, 2024) Mappoli, Shidhin; Ghosh, Kalyan; Pumera, Martin
3D printing has emerged as an attractive manufacturing technique in supercapacitor electrodes owing to the precise and customisable fabrication of complex electrode designs, enhancing the performance and efficiency of the device. Despite the advantages, 3D-printed electrodes are limited by their low conductivity and electrochemical properties, predominantly due to the lack of availability of suitable conductive materials. To address this limitation, we modified the 3D-printed nanocarbon (3D-PnC) electrode by activation and surface deposition of Ti3C2Tx MXene. A solid-state asymmetric supercapacitor was fabricated by using 3D-PnC/Ti3C2Tx as the negative electrode and polyaniline (PANI) electrodeposited 3D-printed nanocarbon electrode (3D-PnC@PANI) as the positive electrode. The fabricated symmetric supercapacitor exhibits enhancement in overall voltage window, areal capacitance and energy density. The successful operation of the supercapacitor was demonstrated by the illumination of the red light-emitting diodes. Furthermore, this research opens the possibility of utilising MXene-modified 3D-printed electrodes for various electrochemical applications and devices.
Sustainable refrigeration technology selection: An innovative DEA-TOPSIS hybrid model
(Elsevier, 2024) Arabi, Behrouz; Toloo, Mehdi; Yang, Zaoli; Zhang, Peihao; Xu, Bing
This study proposes a novel multiple criteria decision making (MCDM) framework aimed at selecting refrigeration technologies that are both carbon- and energy-efficient, aligning with the UK's net-zero policies and the UN's Sustainable Development Goals (SDGs). Addressing the challenge of a limited number of competing technologies and the need to incorporate diverse stakeholders' perspectives, we design a hybrid DEA-TOPSIS approach utilizing the Feasible Super-Efficiency Slacks-Based Algorithm (FSESBA). FSESBA proves invaluable, especially in scenarios involving super-efficiency or efficiency trend measurement, where addressing undesirable factors may lead to the well-known infeasibility problem. While we establish the theoretical soundness of the DEA-TOPSIS model, we validate the efficacy of our proposed approach through comparative analysis with conventional methods. Subsequently, we evaluate the choices of present and upcoming refrigeration technologies at a leading UK supermarket. Our findings reveal a shift from prevalent HFO-based technologies in 2020 to CO2-based technologies by 2050, attributed to their lower energy usage and GHG emissions. In addition, maintaining current refrigeration systems could contribute to achieving international and national targets to decrease F-Gas refrigerant usage, although net-zero targets will remain out of reach. In summary, our research findings underscore the potential of the introduced model to reinforce the adoption of novel refrigeration system technology, offering valuable support for the UK SDGs taskforces and net-zero policy formulation.
Performance analysis of a cognitive RIS-NOMA in wireless sensor network
(MDPI, 2024) Thien, Huynh Thanh; Le, Anh-Tu; Minh, Bui Vu; Rejfek, Luboš; Koo, Insoo
The reconfigurable intelligent surfaces (RIS) represent a transformative technology in wireless communication, offering a novel approach to managing and enhancing radio signal propagation. By dynamically adjusting their electromagnetic properties, RIS can significantly improve the performance and efficiency of 5G and beyond communication systems. In this paper, we study a cognitive RIS-aided non-orthogonal multiple access (NOMA) network that serves multiple users and improves spectrum efficiency. Our analysis assumes a secondary network operates under multi-primary user constraints and interference from the primary source. We derive approximation closed-form formulas for outage probability (OP), and system throughput. To obtain further insights, an asymptotic expression for OP is computed by taking into account two power configurations at the source. Additionally, numerical results show the effects of important factors on performance, confirming the accuracy of the theoretical derivation. According to the simulation results, performance by the system under consideration might be improved considerably by combining a RIS and NOMA, particularly when compared to an orthogonal multiple access scheme.
A comparison between the quality of two level and three levels bidirectional buck-boost converter using the neural network controller
(IEEE, 2024) Gaied, Hajer; Flah, Aymen; Kraiem, Habib; Prokop, Lukáš
A comparison between two-phase and three-phase interlaced DC converter with parallel MOSFET is presented. PWM is evaluated using a two-way DC-DC converter to charge and discharge a battery. The results show an excellent DC voltage gain without an extremely high cycle load. The interlaced DC-DC converters with MOSFETs in parallel in two and three phases offer distinct advantages and limitations. The two-phase converter has a simpler design and a potentially lower cost due to the reduced number of components. However, it can present challenges in terms of precise voltage regulation and current balancing, due to the limited number of switching phases. On the other hand, the three-phase converter offers more precise voltage regulation and improved current balance thanks to its higher number of phases. While this results in increased design complexity and potentially higher cost, it allows for a more uniform distribution of current load among MOSFETs. The choice between the two will depend on the specific requirements of the application, acceptable trade-offs in terms of complexity, cost and performance, as well as the need for accurate voltage regulation and optimal current balancing.
Advancing short-term solar irradiance forecasting accuracy through a hybrid deep learning approach with Bayesian optimization
(Elsevier, 2024) Molu, Reagan Jean Jacques; Tripathi, Bhaskar; Mbasso, Wulfran Fendzi; Naoussi, Serge Raoul Dzonde; Bajaj, Mohit; Wira, Patrice; Blažek, Vojtěch; Prokop, Lukáš; Mišák, Stanislav
The optimization of solar energy integration into the power grid relies heavily on accurate forecasting of solar irradiance. In this study, a new approach for short-term solar irradiance forecasting is introduced. This method combines Bayesian Optimized Attention-Dilated Long Short-Term Memory and Savitzky-Golay filtering. The methodology is implemented to analyze data obtained from a solar irradiance probe situated in Douala, Cameroon. Initially, the unprocessed data is augmented by integrating distinctive solar irradiation variables, and the Savitzky-Golay filter with Bayesian Optimization is used to enhance its quality. Subsequently, multiple deep learning models, including Long Short-Term Memory, Bidirectional Long Short-Term Memory, Artificial Neural Networks, Bidirectional Long Short-Term Memory with Additive Attention Mechanism, and Bidirectional Long Short-Term Memory with Additive Attention Mechanism and Dilated Convolutional layers, are trained and evaluated. Out of all the models considered, the proposed approach, which combines the attention mechanism and dilated convolutional layers, demonstrates exceptional performance with the best convergence and accuracy in forecasting. Bayesian Optimization is further utilized to fine -tune the polynomial and window size of the Savitzky-Golay filter and optimize the hyperparameters of the deep learning models. The results show a Symmetric Mean Absolute Percentage Error of 0.6564, a Normalized Root Mean Square Error of 0.2250, and a Root Mean Square Error of 22.9445, surpassing previous studies in the literature. Empirical findings highlight the effectiveness of the proposed methodology in enhancing the accuracy of short-term solar irradiance forecasting. This research contributes to the field by introducing novel data pre-processing techniques, a hybrid deep learning architecture, and the development of a benchmark dataset. These advancements benefit both researchers and solar plant managers, improving solar irradiance forecasting capabilities.
Renewable energy resource management using an integrated robust decision making model under entropy and similarity measures of fuzzy hypersoft set
(Elsevier, 2024) Saeed, Muhammad Haris; Saeed, Muhammad; Rahman, Atiqe Ur; Ahsan, Muhammad; Mohammed, Mazin Abed; Marhoon, Haydar Abdulameer; Nedoma, Jan; Martinek, Radek
The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing longlasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management. This multi -criteria decision making (MCDM) challenge is tackled by applying a dynamic fuzzy hypersoft set -based Method for the evaluation of currently deployed Renewable Energy systems and providing a decision support system for the installation of new ones based on the factors mentioned above for Turkey. As the installation of new renewable energy projects and the evaluation of old ones is significantly influenced by human judgment, it leaves great room for uncertainty primarily because of the psychological factors of the expert. The novel concept of Fuzzy Hypersoft Sets (FHSs) and their Entropy (EN) and TOPSIS-based operations are first discussed with reference to the problem at hand. The presented structure is superior to the ones in the literature by allowing access to data parameters as sub -parametric values while utilizing the versatility of Fuzzy structures to deal with uncertainty. The technique has great potential to serve as a potential decision support system in any setting. For now, hypothetical expert ratings are used to illustrate the working of the dynamic structure along with a sensitivity analysis to investigate the primary criterion weights in sorting. The evaluation of currently deployed renewable energy systems using our methodology revealed an average improvement in system performance compared to traditional methods. Furthermore, the decision support system for the installation of new projects based on geographical, political, social, and natural factors exhibited a potential increase in overall system efficiency. These numeric outcomes highlight the effectiveness and practical applicability of our approach in optimizing resource management and fostering sustainable energy practices.
Computation of soliton structure and analysis of chaotic behaviour in quantum deformed Sinh-Gordon model
(PLOS, 2024) Jhangeer, Adil; Ibraheem, Farheen; Jamal, Tahira; Riaz, Muhammad Bilal; Kader, Atef Abdel
Soliton dynamics and nonlinear phenomena in quantum deformation has been investigated through conformal time differential generalized form of q deformed Sinh-Gordon equation. The underlying equation has recently undergone substantial amount of research. In Phase 1, we employed modified auxiliary and new direct extended algebraic methods. Trigonometric, hyperbolic, exponential and rational solutions are successfully extracted using these techniques, coupled with the best possible constraint requirements implemented on parameters to ensure the existence of solutions. The findings, then, are represented by 2D, 3D and contour plots to highlight the various solitons' propagation patterns such as kink-bright, bright, dark, bright-dark, kink, and kink-peakon solitons and solitary wave solutions. It is worth emphasizing that kink dark, dark peakon, dark and dark bright solitons have not been found earlier in literature. In phase 2, the underlying model is examined under various chaos detecting tools for example lyapunov exponents, multistability and time series analysis and bifurcation diagram. Chaotic behavior is investigated using various initial condition and novel results are obtained.
Single atom catalysts based on earth-abundant metals for energy-related applications
(American Chemical Society, 2024) Kment, Štěpán; Bakandritsos, Aristides; Tantis, Iosif; Kmentová, Hana; Zuo, Yunpeng; Henrotte, Olivier; Naldoni, Alberto; Otyepka, Michal; Varma, Rajender S.; Zbořil, Radek
Anthropogenic activities related to population growth, economic development, technological advances, and changes in lifestyle and climate patterns result in a continuous increase in energy consumption. At the same time, the rare metal elements frequently deployed as catalysts in energy related processes are not only costly in view of their low natural abundance, but their availability is often further limited due to geopolitical reasons. Thus, electrochemical energy storage and conversion with earth-abundant metals, mainly in the form of single-atom catalysts (SACs), are highly relevant and timely technologies. In this review the application of earth-abundant SACs in electrochemical energy storage and electrocatalytic conversion of chemicals to fuels or products with high energy content is discussed. The oxygen reduction reaction is also appraised, which is primarily harnessed in fuel cell technologies and metal-air batteries. The coordination, active sites, and mechanistic aspects of transition metal SACs are analyzed for two-electron and four-electron reaction pathways. Further, the electrochemical water splitting with SACs toward green hydrogen fuel is discussed in terms of not only hydrogen evolution reaction but also oxygen evolution reaction. Similarly, the production of ammonia as a clean fuel via electrocatalytic nitrogen reduction reaction is portrayed, highlighting the potential of earth-abundant single metal species.
Improved robust model predictive control for PMSM using backstepping control and incorporating integral action with experimental validation
(Elsevier, 2024) Djouadi, Hafidh; Ouari, Kamel; Belkhier, Youcef; Lehouche, Hocine; Bajaj, Mohit; Blažek, Vojtěch
The DC motor is being rapidly replaced in the industry by the permanent magnet synchronous motor (PMSM), which has a number of benefits over it. Nonlinear equations are used to describe the dynamics of the PMSM. It is susceptible to unidentified external disturbances (load), and its properties change over time. These constraints make it more difficult to exercise control. To overcome the non-linearities and the aforementioned shortcomings, non-linear controls are necessary. This manuscript refers to the development of a sturdy high-caliber position tracking controller that incorporates integral action for PMSM. A predictive control law for the speed loop is established, combined with the backstepping control law for the inner loop. The overall strategy can be divided into two distinct elements. The initial stage involves the derivation of a reference electromagnetic torque computed through the generalized non-linear predictive control method. Subsequently, the controller law is formulated utilizing the robust backstepping control technique. One of the cardinal merits of this method lies in its exemption from the requirement of measuring and observing the external disturbances and parametric uncertainties. The efficacy of this cutting-edge control approach is rigorously evaluated in simulation with MATLAB/Simulink environment and experimentally using OPAL-RT, under diverse operating conditions. The findings demonstrate steadfast resilience amidst external disruptions and adjustments to parameters, while ensuring swift convergence, a testament to its robustness and reliability.
Static characteristics and energy consumption of the pressure-compensated pump
(MDPI, 2024) Kolář, David; Bureček, Adam; Hružík, Lumír; Ledvoň, Marian; Polášek, Tomáš; Jablonská, Jana; Lenhard, Richard
The motivation of this research was to assess the possibility of speed control for the selected pressure-compensated pump. Measured static characteristics of an axial piston pump with pressure compensation are presented in the paper. Based on these characteristics, the pump efficiencies are determined. The characteristics and efficiencies are determined for the different pump outlet pressures, pump speeds, relative displacements and for the different pressures set at the pressure compensator. In addition, the different methods of pump control were compared. These are displacement control, speed control and both controls. The efficiency of each control method was compared based on the determined mechanical input power at the pump drive shaft. By comparing these control methods, it was found that the combination of both control methods can achieve up to 93% savings of mechanical power in the controlled state (stand-by state). Also, the adverse effects resulting from each control method that reduces pump efficiency were defined.
ZrN coating as a source for the synthesis of a new hybrid ceramic layer
(Elsevier, 2024) Gabor, Roman; Cvrček, Ladislav; Kudrnová, Marie; Hlinka, Josef; Večeř, Marek; Buřil, Matěj; Walter, Jan; Čekada, Miha; Drnovšek, Aljaž; Unucka, Petr; Mamulová Kutláková, Kateřina; Motyka, Oldřich; Seidlerová, Jana
The study focuses on an innovative process for the use of a ZrN coating on Ti6Al4V alloy for orthopaedic bone implants. The preparation process combines the technology of physical vapour deposition (PVD) and micro-arc oxidation (MAO) to achieve hydrophobic properties, improved corrosion resistance and enhanced coating adhesion to Ti6Al4V alloy. An alkaline electrolyte and different microarc discharge intensities were used to prepare MAO coatings. The evaluation of the structure and topography of the coatings was performed using SEM with XRPD, EDX, and XPS analysis. The prepared oxide coatings Zr, ZrSiO4, and ZrTiO4 increase the corrosion potential depending on the applied source frequency and thus increase the corrosion resistance of the hybrid system. At the same time, the formation of oxide phases leads to changes in surface topography associated with increasing friction coefficient and better wear resistance.
Numerical solution to the time-fractional Burgers-Huxley equation involving the Mittag-Leffler function
(MDPI, 2024) Hayat, Afzaal Mubashir; Riaz, Muhammad Bilal; Abbas, Muhammad; Alosaimi, Moataz; Jhangeer, Adil; Nazir, Tahir
Fractional differential equations play a significant role in various scientific and engineering disciplines, offering a more sophisticated framework for modeling complex behaviors and phenomena that involve multiple independent variables and non-integer-order derivatives. In the current research, an effective cubic B-spline collocation method is used to obtain the numerical solution of the nonlinear inhomogeneous time-fractional Burgers-Huxley equation. It is implemented with the help of a theta-weighted scheme to solve the proposed problem. The spatial derivative is interpolated using cubic B-spline functions, whereas the temporal derivative is discretized by the Atangana-Baleanu operator and finite difference scheme. The proposed approach is stable across each temporal direction as well as second-order convergent. The study investigates the convergence order, error norms, and graphical visualization of the solution for various values of the non-integer parameter. The efficacy of the technique is assessed by implementing it on three test examples and we find that it is more efficient than some existing methods in the literature. To our knowledge, no prior application of this approach has been made for the numerical solution of the given problem, making it a first in this regard.
Association of selected adipokines with vitamin D deficiency in children with inflammatory bowel disease
(BMC, 2024) Geryk, Miloš; Kučerová, Veronika; Velgáňová-Véghová, Mária; Foltenová, Hana; Bouchalová, Kateřina; Karásek, David; Radvansky Jr., Martin; Karásková, Eva
Background: Adipose tissue is significantly involved in inflammatory bowel disease (IBD). Vitamin D can affect both adipogenesis and inflammation. The aim of this study was to compare the production of selected adipokines, potentially involved in the pathogenesis of IBD - adiponectin, resistin, retinol binding protein 4 (RBP-4), adipocyte fatty acid binding protein and nesfatin-1 in children with IBD according to the presence of 25-hydroxyvitamin D (25(OH)D) deficiency. Methods: The study was conducted as a case-control study in pediatric patients with IBD and healthy children of the same sex and age. In addition to adipokines and 25(OH)D, anthropometric parameters, markers of inflammation and disease activity were assessed in all participants. Results: Children with IBD had significantly higher resistin levels regardless of 25(OH)D levels. IBD patients with 25(OH)D deficiency only had significantly lower RBP-4 compared to healthy controls and also compared to IBD patients without 25(OH)D deficiency. No other significant differences in adipokines were found in children with IBD with or without 25(OH)D deficiency. 25(OH)D levels in IBD patients corelated with RBP-4 only, and did not correlate with other adipokines. Conclusions: Whether the lower RBP-4 levels in the 25(OH)D-deficient group of IBD patients directly reflect vitamin D deficiency remains uncertain. The production of other adipokines does not appear to be directly related to vitamin D deficiency.
Investigating pseudo parabolic dynamics through phase portraits, sensitivity, chaos and soliton behavior
(Springer Nature, 2024) Jhangeer, Adil; Ibraheem, Farheen; Jamal, Tahira; Rahimzai, Ariana Abdul; Khan, Ilyas
This research examines pseudoparabolic nonlinear Oskolkov-Benjamin-Bona-Mahony-Burgers (OBBMB) equation, widely applicable in fields like optical fiber, soil consolidation, thermodynamics, nonlinear networks, wave propagation, and fluid flow in rock discontinuities. Wave transformation and the generalized Kudryashov method is utilized to derive ordinary differential equations (ODE) and obtain analytical solutions, including bright, anti-kink, dark, and kink solitons. The system of ODE, has been then examined by means of bifurcation analysis at the equilibrium points taking parameter variation into account. Furthermore, in order to get insight into the influence of some external force perturbation theory has been employed. For this purpose, a variety of chaos detecting techniques, for instance poincar & eacute; diagram, time series profile, 3D phase portraits, multistability investigation, lyapounov exponents and bifurcation diagram are implemented to identify the quasi periodic and chaotic motions of the perturbed dynamical model. These techniques enabled to analyze how perturbed dynamical system behaves chaotically and departs from regular patterns. Moreover, it is observed that the underlying model is quite sensitivity, as it changing dramatically even with slight changes to the initial condition. The findings are intriguing, novel and theoretically useful in mathematical and physical models. These provide a valuable mechanism to scientists and researchers to investigate how these perturbations influence the system's behavior and the extent to which it deviates from the unperturbed case.
Retinal image dataset of infants and retinopathy of prematurity
(Springer Nature, 2024) Timkovič, Juraj; Nowaková, Jana; Kubíček, Jan; Hasal, Martin; Varyšová, Alice; Kolarčík, Lukáš; Maršolková, Kristýna; Augustynek, Martin; Snášel, Václav
Retinopathy of prematurity (ROP) represents a vasoproliferative disease, especially in newborns and infants, which can potentially affect and damage the vision. Despite recent advances in neonatal care and medical guidelines, ROP still remains one of the leading causes of worldwide childhood blindness. The paper presents a unique dataset of 6,004 retinal images of 188 newborns, most of whom are premature infants. The dataset is accompanied by the anonymized patients' information from the ROP screening acquired at the University Hospital Ostrava, Czech Republic. Three digital retinal imaging camera systems are used in the study: Clarity RetCam 3, Natus RetCam Envision, and Phoenix ICON. The study is enriched by the software tool ReLeSeT which is aimed at automatic retinal lesion segmentation and extraction from retinal images. Consequently, this tool enables computing geometric and intensity features of retinal lesions. Also, we publish a set of pre-processing tools for feature boosting of retinal lesions and retinal blood vessels for building classification and segmentation models in ROP analysis.
Dual resource constrained flexible job shop scheduling with sequence-dependent setup time
(Wiley, 2024) Barak, Sasan; Javanmard, Shima; Moghdani, Reza
This study addresses the imperative need for efficient solutions in the context of the dual resource constrained flexible job shop scheduling problem with sequence-dependent setup times (DRCFJS-SDSTs). We introduce a pioneering tri-objective mixed-integer linear mathematical model tailored to this complex challenge. Our model is designed to optimize the assignment of operations to candidate multi-skilled machines and operators, with the primary goals of minimizing operators' idleness cost and sequence-dependent setup time-related expenses. Additionally, it aims to mitigate total tardiness and earliness penalties while regulating maximum machine workload. Given the NP-hard nature of the proposed DRCFJS-SDST, we employ the epsilon constraint method to derive exact optimal solutions for small-scale problems. For larger instances, we develop a modified variant of the multi-objective invasive weed optimization (MOIWO) algorithm, enhanced by a fuzzy sorting algorithm for competitive exclusion. In the absence of established benchmarks in the literature, we validate our solutions against those generated by multi-objective particle swarm optimization (MOPSO) and non-dominated sorted genetic algorithm (NSGA-II). Through comparative analysis, we demonstrate the superior performance of MOIWO. Specifically, when compared with NSGA-II, MOIWO achieves success rates of 90.83% and shows similar performance in 4.17% of cases. Moreover, compared with MOPSO, MOIWO achieves success rates of 84.17% and exhibits similar performance in 9.17% of cases. These findings contribute significantly to the advancement of scheduling optimization methodologies.
Computational study of elastic waves generated by ultrafast demagnetization in fcc Ni
(American Physical Society, 2024) Korniienko, I.; Nieves, P.; Fraile, A.; Iglesias, R.; Legut, Dominik
Picosecond ultrasonics is a fast growing and advanced research field with broad application to the imaging and characterization of nanostructured materials as well as at a fundamental level. The aim of this paper is to provide an advanced 3D model based on atomistic spin -lattice simulations of the laser -induced elastic response in ferromagnetically ordered fcc Ni. The advantage of such an approach is the possibility to take into account the laser radiation interaction with the spins and thus characterize the magnetic contribution to the total stress. We analyze the atomic displacements caused both by the ultrafast thermal expansion of the crystal lattice and by the demagnetization process due to the heating of a certain area of the sample by an ultrashort laser pulse. Subsequently, an attempt is made to propose mathematical expressions for describing the corresponding total stress. The lattice and magnetic contributions have been evaluated, whereupon the former is found to be much greater than the latter.
Mitigation of humidity interference by graphene derivatives for efficient temperature sensors without encapsulation
(Wiley, 2024) Šedajová, Veronika; Štulík, Jiří; Jakubec, Petr; Otyepka, Michal
Temperature monitoring and regulation are essential in various environments, including modern industry and living and storage spaces. The growing demand for temperature sensors calls for affordable, efficient, interference-resistant, and eco-friendly solutions. The challenge of humidity interference in constructing temperature sensors often leads to compromising on the dynamic sensor properties in particular due to the need for encapsulation. To this end, this study introduces a temperature sensor leveraging a carefully designed graphene derivative to mitigate the humidity interference. The material, synthesize through scalable fluorographene chemistry with benzylamine, is optimized in order to enhance its properties, which led to achieving peak efficiency with a minimal humidity impact. The sensor demonstrated full functionality across a temperature range from 10 to 90 degrees C, with a temperature coefficient of resistivity 8.63 x 10-3 K-1, which is more than twice as high as that of conventional platinum thermometers. Remarkably, the sensor exhibited only a 2% change in resistance when exposed to relative humidity in the range of 20 to 70%. Notably, the sensor continues to give a consistent performance even after six months, which proved its stability. The presented device holds promise for evolving into a fully printed, cost-effective and reliable next-generation temperature sensors.
Exploration of bismuth-based materials for photocatalytic decomposition of N2O
(Royal Society of Chemistry, 2024) Atri, Shalu; Uma, Sitharaman; Nagarajan, Rajamani; Gregor, Maroš; Roch, Tomáš; Filip Edelmannová, Miroslava; Reli, Martin; Kočí, Kamila; Motola, Martin; Monfort, Olivier
This work is focused on the investigation of three different Bi-based materials, i.e., CaBi2O2(CO3)(2) (CBOC), Ca4Bi6O13 (CBO), and Bi2Ce2O7 (BCO), as photocatalysts in N2O reduction. This study has emphasized the effectiveness of the bismuth ion, irrespective of its presence in different structures with self-regulating electronic and morphological properties, when employed as a photocatalyst. Monophasic CBOC, CBO, and BCO samples have been synthesized by wet-chemical methods, and they exhibit distinct morphological features such as plate-like, dumbbell-shaped, and irregularly shaped crystallites. From the UV-visible diffuse reflectance spectroscopy (DRS) data, CBO exhibits a lower optical band gap of 2.52 eV compared to CBOC (3.95 eV), which CBO is synthesized from. BCO shows the lowest optical band gap of 2.16 eV. CBO exhibits the highest photocurrent generation and the lowest value in work function measurements, following the trend as CBO > CBOC > BCO. The efficiency of the Bi-based materials in photocatalytic decomposition of N2O also follows a similar trend as observed in the photocurrent measurements, wherein the CBO sample exhibits a maximum of 10.4% decomposition of N2O under UV-A in 24 h. Oxygen vacancies in CBO and BCO have been reasoned to play a crucial role in the photocatalytic decomposition of N2O.
Analytical study of fractional DNA dynamics in the Peyrard-Bishop oscillator-chain model
(Elsevier, 2024) Riaz, Muhammad Bilal; Fayyaz, Marriam; Rahman, Riaz Ur; Martinovič, Jan; Tunç, Osman
In this research, we present a new auxiliary equation approach, which uses two distinct fractional derivatives: /3- and M-truncated fractional derivatives to explore the space-time fractional Peyrard-Bishop DNA dynamic model equation. This examines the nonlinear interplay between neighboring displacements and hydrogen bonds through mathematical modeling of DNA vibration dynamics. The solutions are tasked with examining the nonlinear interaction among neighboring displacements of the DNA strand. The generated solutions exhibit various wave patterns under varying fractional values and parametric conditions: w-shape, bright, combined periodic wave solutions, dark-bright, bell shaped, m-shaped, w-shaped with two bright solutions, and m-shape with two dark solutions. Graphical representations provide a complete analysis of these physical features. The results demonstrate the successful implementation of the proposed approach, which will be advantageous for locating analytical remedies to more nonlinear challenges.

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