Publikační činnost Děkanátu FEI / Publications of the Dean's Office of the Faculty of Electrical Engineering and Computer Science (400)

Permanent URI for this collectionhttp://hdl.handle.net/10084/151870

Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Děkanátu FEI (400) v časopisech registrovaných ve Web of Science od roku 2023 po současnost.
Do kolekce jsou zařazeny:
a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská-Technická univerzita Ostrava (VŠB-TUO),
b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

Browse

Now showing 1 - 20 out of 111 results

Advanced materials for micro/nanorobotics
(Royal Society of Chemistry, 2024) Kim, Jeonghyo; Mayorga-Burrezo, Paula; Song, Su-Jin; Mayorga-Martinez, Carmen C.; Medina-Sánchez, Mariana; Pané, Salvador; Pumera, Martin
Autonomous micro/nanorobots capable of performing programmed missions are at the forefront of next-generation micromachinery. These small robotic systems are predominantly constructed using functional components sourced from micro- and nanoscale materials; therefore, combining them with various advanced materials represents a pivotal direction toward achieving a higher level of intelligence and multifunctionality. This review provides a comprehensive overview of advanced materials for innovative micro/nanorobotics, focusing on the five families of materials that have witnessed the most rapid advancements over the last decade: two-dimensional materials, metal-organic frameworks, semiconductors, polymers, and biological cells. Their unique physicochemical, mechanical, optical, and biological properties have been integrated into micro/nanorobots to achieve greater maneuverability, programmability, intelligence, and multifunctionality in collective behaviors. The design and fabrication methods for hybrid robotic systems are discussed based on the material categories. In addition, their promising potential for powering motion and/or (multi-)functionality is described and the fundamental principles underlying them are explained. Finally, their extensive use in a variety of applications, including environmental remediation, (bio)sensing, therapeutics, etc., and remaining challenges and perspectives for future research are discussed.
MD-DCNN: Multi-Scale Dilation-Based Deep Convolution Neural Network for epilepsy detection using electroencephalogram signals
(Elsevier, 2024) Karnati, Mohan; Sahu, Geet; Yadav, Akanksha; Seal, Ayan; Jaworek-Korjakowska, Joanna; Penhaker, Marek; Krejcar, Ondřej
Approximately 65 million individuals experience epilepsy globally. Surgery or medication cannot cure more than 30% of epilepsy patients.However, through therapeutic intervention, anticipating a seizure can help us avoid it. According to previous studies, aberrant activity inside the brain begins a few minutes before the onset of a seizure, known as a pre-ictal state. Many researchers have attempted to anticipate the pre-ictal condition of a seizure; however, achieving high sensitivity and specificity remains challenging. Therefore, deep learning-based early diagnostic tools for epilepsy therapies using electroencephalogram (EEG) signals are urgently needed. Traditional methods perform well in binary epilepsy scenarios, such as normal vs. ictal, but poorly in ternary situations, such as ictal vs. normal vs. inter-ictal. This study proposes a multi-scale dilated convolution-based network (MD-DCNN) to predict seizures or epilepsy. Traditional DCNNs for epilepsy classification overfit due to insufficient training data (fewer subjects). Windowing 2-sec EEG recordings and extracting the frequency sub-band from each window prevents overfitting in deep networks, which lack training data. We convert each segmented window and its sub-bands into scalogram images and input them into MD-DCNN. The proposed MD-DCNN combines data from several scales without narrowing the acquisition domain. Integrating detailed information into high-level semantic features improves network interpretation and classification. The proposed MD-DCNN is evaluated for two-class, three-class, and cross-database strategy problems using three publicly accessible databases. Experiments show that the MD-DCNN statistically performs better than 13 other current approaches. This demonstrates its potential for developing equipment capable of measuring, monitoring, and recording EEG signals to diagnose epilepsy.
Real time tracking of nanoconfined water-assisted ion transfer in functionalized graphene derivatives supercapacitor electrodes
(Wiley, 2024) Padinjareveetil, Akshay Kumar K.; Pykal, Martin; Bakandritsos, Aristides; Zbořil, Radek; Otyepka, Michal; Pumera, Martin
Water molecules confined in nanoscale spaces of 2D graphene layers have fascinated researchers worldwide for the past several years, especially in the context of energy storage applications. The water molecules exchanged along with ions during the electrochemical process can aid in wetting and stabilizing the layered materials resulting in an anomalous enhancement in the performance of supercapacitor electrodes. Engineering of 2D carbon electrode materials with various functionalities (oxygen (& horbar;O), fluorine (& horbar;F), nitrile (& horbar;C equivalent to N), carboxylic (& horbar;COOH), carbonyl (& horbar;C & boxH;O), nitrogen (& horbar;N)) can alter the ion/water organization in graphene derivatives, and eventually their inherent ion storage ability. Thus, in the current study, a comparative set of functionalized graphene derivatives-fluorine-doped cyanographene (G-F-CN), cyanographene (G-CN), graphene acid (G-COOH), oxidized graphene acid (G-COOH (O)) and nitrogen superdoped graphene (G-N) is systematically evaluated toward charge storage in various aqueous-based electrolyte systems. Differences in functionalization on graphene derivatives influence the electrochemical properties, and the real-time mass exchange during the electrochemical process is monitored by electrochemical quartz crystal microbalance (EQCM). Electrogravimetric assessment revealed that oxidized 2D acid derivatives (G-COOH (O)) are shown to exhibit high ion storage performance along with maximum water transfer during the electrochemical process. The complex understanding of the processes gained during supercapacitor electrode charging in aqueous electrolytes paves the way toward the rational utilization of graphene derivatives in forefront energy storage applications.
Translational nanorobotics breaking through biological membranes
(Royal Society of Chemistry, 2025) Ressnerová, Alžběta; Heger, Zbyněk; Pumera, Martin
In the dynamic realm of translational nanorobotics, the endeavor to develop nanorobots carrying therapeutics in rational in vivo applications necessitates a profound understanding of the biological landscape of the human body and its complexity. Within this landscape, biological membranes stand as critical barriers to the successful delivery of therapeutic cargo to the target site. Their crossing is not only a challenge for nanorobotics but also a pivotal criterion for the clinical success of therapeutic-carrying nanorobots. Nevertheless, despite their urgency, strategies for membrane crossing in translational nanorobotics remain relatively underrepresented in the scientific literature, signaling an opportunity for further research and innovation. This review focuses on nanorobots with various propulsion mechanisms from chemical and physical to hybrid mechanisms, and it identifies and describes four essential biological membranes that represent the barriers needed to be crossed in the therapeutic journey of nanorobots in in vivo applications. First is the entry point into the blood stream, which is the skin or mucosa or intravenous injection; next is the exit from the bloodstream across the endothelium to the target site; further is the entry to the cell through the plasma membrane and, finally, the escape from the lysosome, which otherwise destroys the cargo. The review also discusses design challenges inherent in translating nanorobot technologies to real-world applications and provides a critical overview of documented membrane crossings. The aim is to underscore the need for further interdisciplinary collaborations between chemists, materials scientists and chemical biologists in this vital domain of translational nanorobotics that has the potential to revolutionize the field of precision medicine.
Reconfigurable magnetic liquid metal microrobots: A regenerable solution for the capture and removal of micro/nanoplastics
(Wiley, 2024) Wu, Xianghua; Peng, Xia; Ren, Long; Guan, Jianguo; Pumera, Martin
The pervasive presence of micro/nanoplastics in the environment is a significant threat to ecosystems and human health, demanding effective remediation strategies. Traditional methods for extracting these pollutants from water are often inadequate, typically leaving environmentally harmful residues. In response, this work introduces an innovative approach using reconfigurable and regenerable liquid metal microrobots (LiquidBots) that are magnetically driven to actively sequester micro/nanoplastics from aquatic environments. These LiquidBots utilize a coating of gallium oxide for enhanced adhesion and electrostatic interaction to capture over 80% of nanoplastics present in the solution. Additionally, the LiquidBots can be easily regenerated through sonication, which dislodges captured nanoplastics, allowing the microrobots to be reused. This novel technology offers a highly efficient, adaptable, and sustainable solution to combat the micro/nanoplastic pollution crisis.
Optimized target delineation procedure for the radiosurgery treatment of ventricular tachycardia: observer-independent accuracy
(Via Medica, 2024) Hečko, Jan; Knybel, Lukáš; Rybář, Marian; Penhaker, Marek; Jiravský, Otakar; Neuwirth, Radek; Šramko, Marek; Hašková, Jana; Kautzner, Josef; Cvek, Jakub
Background: Here we aimed to evaluate the respiratory and cardiac-induced motion of a ICD lead used as surrogate in the heart during stereotactic body radiotherapy (SBRT) of ventricular tachycardia (VT). Data provides insight regarding motion and motion variations during treatment. Materials and methods: We analyzed the log files of surrogate motion during SBRT of ventricular tachycardia performed in 20 patients. Evaluated parameters included the ICD lead motion amplitudes; intrafraction amplitude variability; correlation error between the ICD lead and external markers; and margin expansion in the superior-inferior (SI), latero-lateral (LL), and anterior-posterior (AP) directions to cover 90% or 95% of all amplitudes. Results: In the SI, LL, and AP directions, respectively, the mean motion amplitudes were 5.0 +/- 2.6, 3.4. +/- 1.9, and 3.1 +/- 1.6 mm. The mean intrafraction amplitude variability was 2.6 +/- 0.9, 1.9 +/- 1.3, and 1.6 +/- 0.8 mm in the SI, LL, and AP directions, respectively. The margins required to cover 95% of ICD lead motion amplitudes were 9.5, 6.7, and 5.5 mm in the SI, LL, and AP directions, respectively. The mean correlation error was 2.2 +/- 0.9 mm. Conclusions: Data from online tracking indicated motion irregularities and correlation errors, necessitating an increased CTV-PTV margin of 3 mm. In 35% of cases, the motion variability exceeded 3 mm in one or more directions. We recommend verifying the correlation between CTV and surrogate individually for every patient, especially for targets with posterobasal localization where we observed the highest difference between the lead and CTV motion.
Microrobots enhancing synthetic chemistry reactions in non-aqueous media
(Wiley, 2024) Jančík-Procházková, Anna; Jančík, Ján; Palacios-Corella, Mario; Pumera, Martin
Catalysis is a foundational pillar of modern synthetic chemistry, essential for countless industrial processes. Traditional catalysts are often static, either immobilized or dispersed in fluid media. The innovative concept of catalytic microrobots allows the introduction of self-propelled and navigable catalyst particles that are engineered for dynamic and customizable catalysis. Catalytic microrobots are microscale devices with the inherent ability to move and swarm, designed to execute complex tasks in diverse environments, including biomedicine, and environmental remediation. Typically confined to aqueous media, their use in synthetic chemical reactions remains largely unexplored. Here, microrobots are presented as adaptable self-propelled, self-mixing micro-catalysts for the Baeyer-Villiger oxidation, a key industrial process. Zeolite microstructures are tailored, outfitted with magnetic nanoparticles to create zeolite-based microrobots (ZeoBOTs) that are maneuverable in magnetic fields. Uniquely, these ZeoBOTs are not limited to water but can operate in organic solvents, facilitating the Baeyer-Villiger oxidation in non-aqueous conditions. Comparative analysis with static ZeoBOTs reveals that the dynamic, "on-the-fly" movement of the microrobots significantly enhances reaction yields. The findings herald a new era for synthetic chemistry, demonstrating the potential of microrobots as versatile catalysts beyond aqueous systems, and setting the stage for their broader application in synthetic processes.
DMT-OMPA: Innovative applications of an efficient adversarial Marine Predators Algorithm based on dynamic matrix transformation in engineering design optimization
(Elsevier, 2024) Zhang, Zhen; Chu, Shu-Chuan; Nguyen, Trong-The; Wang, Xiaopeng; Pan, Jeng-Shyang
This paper introduces an innovative variant of the Marine Predators Algorithm (MPA), termed the Dynamic Matrix Transformation-based Oppositional Marine Predators Algorithm (DMTOMPA), aimed at enhancing the efficiency of engineering optimization strategies. Traditional MPAs have several shortcomings, including insufficient solution diversity and coverage in the initialization phase, a tendency to become trapped in local optima, and inadequate search capabilities in the later stages of iteration, all of which negatively impact the algorithm's efficiency and effectiveness. To address these issues, the DMT-OMPA incorporates oppositional learning mechanisms and dynamic matrix transformation strategies, significantly enhancing global search capabilities and accelerating convergence speed, particularly in handling complex multidimensional optimization problems.Experimental results on the CEC2013 and CEC2017 test suites demonstrate that DMT-OMPA outperforms other recent MPA variants, various classical algorithm variants, and newly proposed algorithms, verifying its advantages in precision and reliability. Furthermore, the application of this algorithm to various real-world engineering problems substantiates its broad applicability and high efficiency. The study's findings not only deepen our understanding of swarm intelligence optimization algorithms but also provide a new efficient tool for solving complex engineering problems. The results indicate a promising potential for wider application in diverse fields, suggesting that the DMT-OMPA algorithm could become an effective tool for tackling complex optimization problems in the future.
Single atom engineering for nanorobotics
(American Chemical Society, 2024) Ju, Xiaohui; Pumera, Martin
The fields of single atom engineering represent cutting-edge areas in nanotechnology and materials science, pushing the boundaries of how small we can go in engineering functional devices and materials. Nanorobots, or nanobots, are robotic systems scaled down to the nanometer level and designed to perform tasks at similarly small scales. Single atom engineering, on the other hand, involves manipulating individual atoms to create precise materials and devices with controlled properties and functionalities. By integrating single atom engineering into nanorobotics, we unlock the potential to enable the precise incorporation of multiple functionalities onto these minuscule machines with nanometer-level precision. In this perspective, we describe the nascent field of single atom engineering in nanorobotics.
Aqueous multivalent metal-ion batteries: Toward 3D-printed architectures
(Wiley, 2024) De, Puja; Pumera, Martin
Energy storage has become increasingly crucial, necessitating alternatives to lithium-ion batteries due to critical supply constraints. Aqueous multivalent metal-ion batteries (AMVIBs) offer significant potential for large-scale energy storage, leveraging the high abundance and environmentally benign nature of elements like zinc, magnesium, calcium, and aluminum in the Earth's crust. However, the slow ion diffusion kinetics and stability issues of cathode materials pose significant technical challenges, raising concerns about the future viability of AMVIB technologies. Recent research has focused on nanoengineering cathodes to address these issues, but practical implementation is limited by low mass-loading. Therefore, developing effective engineering strategies for cathode materials is essential. This review introduces the 3D printing-enabled structural design of cathodes as a transformative strategy for advancing AMVIBs. It begins by summarizing recent developments and common challenges in cathode materials for AMVIBs and then illustrates various 3D-printed cathode structural designs aimed at overcoming the limitations of conventional cathode materials, highlighting pioneering work in this field. Finally, the review discusses the necessary technological advancements in 3D printing processes to further develop advanced 3D-printed AMVIBs. The reader will receive new fresh perspective on multivalent metal-ion batteries and the potential of additive technologies in this field.
Flood algorithm (FLA): an efficient inspired meta-heuristic for engineering optimization
(Springer, 2024) Ghasemi, Mojtaba; Golalipour, Keyvan; Zare, Mohsen; Mirjalili, Seyedali; Trojovský, Pavel; Abualigah, Laith; Hemmati, Rasul
Introducing a novel meta-heuristic optimization algorithm, the Flood Algorithm (FLA) draws inspiration from the intricate movement and flow patterns of water masses during flooding events in river basins. FLA mathematically models key phenomena such as the movement of water toward slopes, flow rates over time, soil permeability effects, and periodic increases and decreases in water levels from precipitation and losses. Leveraging these models, the algorithm guides the movement and evolution of a population of potential solutions toward enhanced optimality. The algorithm endeavors to establish an appropriate correlation between the fundamental aspects of natural flood events and the optimization process. Its formulation and working mechanism are described in detail. It operates in two main phases-a regular movement phase, where the population moves naturally toward current best solutions, and a flooding phase, which introduces random disturbances to increase diversity. New solutions are periodically introduced while weaker ones are removed, mirroring the natural cycles of water levels. FLA's effectiveness is demonstrated through its application on well-known benchmark optimization problems and engineering design problems. Extensive comparisons have been carried out on CEC2005 functions using 16 algorithms in both basic and enhanced modes, as well as on CEC2014 functions with dimensions 30, 50, and 100 using a total of 20 other algorithms. These rigorous studies unequivocally confirm the robustness and strength of the proposed algorithm. Furthermore, the algorithm's performance on 12 constrained engineering problems demonstrates its ability to tackle real-world challenges. The FLA's source code is publicly available at https://www.optim-app.com/projects/fla.
Corrosion-resistant shape-programmable Zn-I2 battery
(Wiley, 2024) Sonigara, Keval K.; Vaghasiya, Jayraj V.; Pumera, Martin
Zinc-iodine (Zn-I-2) batteries are promising, low-cost and safe aqueous rechargeable energy storage devices. An iodide shuttle-induced corrosion and poor zinc (Zn) stripping/plating often result in a limited battery lifetime, urges the development of multifunctional Zn anodes. To overcome these problems, here multifunctional Zn-anode is demonstrated with shape-programmability and uniform Zn morphology along low-indexed (002) crystal plane, achieved by electrodepositing Zn on nitinol alloy (nickel-titanium, NiTi). It is found that the surface oxide layer on NiTi supports the uniform Zn deposition with densely packed and planar film formation that leads high corrosion resistance, while adopts the shape-memory function. NiTi-based device achieves extremely steady performance, benefiting from uniform and planar Zn morphology during cycling, whereas the Zn-based device short-circuits due to dendritic development under severe iodide corrosion. It is also demonstrated a flat-shape-programmed flexible pouch cell Zn-I-2 battery (SP-ZIB), which performs well in bent mode, recovers its original flat shape at elevated temperature, and shows consistent performance for validated cycles. The shape-memory function of NiTi makes this Zn-I-2 battery advanced by flexibility and shape-programmable features. This study represents fresh insight for using smart materials as multifunctional features for the next-generation Zn-I-2 batteries.
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.
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.
Catch fish optimization algorithm: a new human behavior algorithm for solving clustering problems
(Springer, 2024) Jia, Heming; Wen, Qixian; Wang, Yuhao; Mirjalili, Seyedali
This paper is inspired by traditional rural fishing methods and proposes a new metaheuristic optimization algorithm based on human behavior: Catch Fish Optimization Algorithm (CFOA). This algorithm simulates the process of rural fishermen fishing in ponds, which is mainly divided into two phases: the exploration phase and the exploitation phase. In the exploration phase, there are two stages to search: first, the individual capture stage based on personal experience and intuition, and second, the group capture stage based on human proficiency in using tools and collaboration. Transition from independent search to group capture during the exploration phase. Exploitation phase: All fishermen will surround the shoal of fish and work together to salvage the remaining fish, a collective capture strategy. CFOA model is based on these two phases. This paper tested the optimization performance of CFOA using IEEE CEC 2014 and IEEE CEC 2020 test functions, and compared it with 11 other optimization algorithms. We employed the IEEE CEC2017 function to evaluate the overall performance of CFOA. The experimental results indicate that CFOA exhibits excellent and stable optimization capabilities overall. Additionally, we applied CFOA to data clustering problems, and the final results demonstrate that CFOA's overall error rate in processing clustering problems is less than 20%, resulting in a better clustering effect. The comprehensive experimental results show that CFOA exhibits excellent optimization effects when facing different optimization problems. CFOA code is open at https://github.com/Meky-1210/CFOA.git.
Load monitoring and appliance recognition using an inexpensive, low-frequency, data-to-image, neural network, and network mobility approach for domestic IoT systems
(IEEE, 2024) Fazio, Peppino; Mehić, Miralem; Vozňák, Miroslav
With the low integration costs and quick development cycle of all-IP-based 5G+ technologies, it is not surprising that the proliferation of IP devices for residential or industrial purposes is ubiquitous. Energy scheduling/management and automated device recognition are popular research areas in the engineering community, and much time and work have been invested in producing the systems required for smart city networks. However, most proposed approaches involve expensive and invasive equipment that produces huge volumes of data (high-frequency complexity) for analysis by supervised learning algorithms. In contrast to other studies in the literature, we propose an approach based on encoding consumption data into vehicular mobility and imaging systems to apply a simple convolutional neural network to recognize certain scenarios (devices powered on) in real time and based on the nonintrusive load monitoring paradigm. Our idea is based on a very cheap device and can be adapted at a very low cost for any real scenario. We have also created our own data set, taken from a real domestic environment, contrary to most existing works based on synthetic data. The results of the study's simulation demonstrate the effectiveness of this innovative and low-cost approach and its scalability in function of the number of considered appliances.
Analysis of the variations in brain activity in response to various computer games
(World Scientific Publishing Co Pte Ltd, 2024) Vivekanandhan, Gayathri; Karthikeyan, Anitha; Pakniyat, Najmeh; Penhaker, Marek; Krejcar, Ondřej; Namazi, Hamidreza
The influence of video games on the human brain has been a topic of extensive research and discussion. Video games, characterized by their dynamic and immersive qualities, have demonstrated the capacity to impact diverse cognitive processes. In this study, we conducted a detailed analysis of brain response variations to different genres of computer games, specifically focusing on boring, calm, horror, and funny games. To achieve this, we computed the sample entropy and approximate entropy of electroencephalograms (EEG) signals recorded from participants while they engaged with each type of game. Our findings revealed that EEG signals exhibited the highest complexity during the funny game and the lowest complexity during the calm game. This suggests that the brain is most active when playing the funny game and least active during the calm game. These results provide valuable insights into how different types of video game content can influence brain activity. The methodology employed in this study can be extended to explore brain activity under various conditions, potentially offering a broader understanding of how different stimuli impact cognitive processes. This approach can be useful in examining the effects of various interactive media on brain function and could inform the design of video games and other digital experiences to optimize cognitive engagement and mental well-being.
Allotrope-dependent activity-stability relationships of molybdenum sulfide hydrogen evolution electrocatalysts
(Springer Nature, 2024) Escalera-López, Daniel; Iffelsberger, Christian; Zlatar, Matej; Novčić, Katarina; Maselj, Nik; Van Pham, Chuyen; Jovanovič, Primož; Hodnik, Nejc; Thiele, Simon; Pumera, Martin; Cherevko, Serhiy
Molybdenum disulfide (MoS2) is widely regarded as a competitive hydrogen evolution reaction (HER) catalyst to replace platinum in proton exchange membrane water electrolysers (PEMWEs). Despite the extensive knowledge of its HER activity, stability insights under HER operation are scarce. This is paramount to ensure long-term operation of Pt-free PEMWEs, and gain full understanding on the electrocatalytically-induced processes responsible for HER active site generation. The latter are highly dependent on the MoS2 allotropic phase, and still under debate. We rigorously assess these by simultaneously monitoring Mo and S dissolution products using a dedicated scanning flow cell coupled with downstream analytics (ICP-MS), besides an electrochemical mass spectrometry setup for volatile species analysis. We observe that MoS2 stability is allotrope-dependent: lamellar-like MoS2 is highly unstable under open circuit conditions, whereas cluster-like amorphous MoS3-x instability is induced by a severe S loss during the HER and undercoordinated Mo site generation. Guidelines to operate non-noble PEMWEs are therefore provided based on the stability number metrics, and an HER mechanism which accounts for Mo and S dissolution pathways is proposed.
Performance prediction of power beacon-aided wireless sensor-powered non-orthogonal multiple-access Internet-of-Things networks under imperfect channel state information
(MDPI, 2024) Nguyen, Ngoc-Long; Le, Anh-Tu; Nguyen, Phuong-Loan T.; Minh, Bui Vu; Rejfek, Luboš; Kim, Yong-Hwa
In this paper, we investigate a novel power beacon (PB)-aided wireless sensor-powered non-orthogonal multiple-access (NOMA) Internet-of-Things (IoT) network under imperfect channel state information (CSI). Furthermore, the exact expression outage probability (OP) of two IoT users is derived to analyze the performance of the considered network. To give further insight, the expression asymptotic OP and diversity order are also expressed when the transmit power at the PB goes to infinity. Furthermore, a deep neural network (DNN) framework is proposed to concurrently forecast IoT users' OP in relation to real-time setups for IoT users. Additionally, when compared to the traditional analysis, our created DNN shows the shortest run-time prediction, and the outcomes predicted by the DNN model almost match those of the simulation. In addition, numerical results validate our analysis, simulation, and prediction through a Monte Carlo Simulation. Furthermore, the results show the impact of the main parameter on our proposed system. Finally, these findings show that NOMA performs better than the conventional orthogonal multiple-access (OMA) techniques.
Multiscale hierarchical nanoarchitectonics with stereographically 3D-printed electrodes for water splitting and energy storage
(Elsevier, 2024) Subhadarshini, Suvani; Ghosh, Kalyan; Pumera, Martin
The pursuit of sustainable solutions to address the global energy crisis has led to a keen interest in the advancement of cost-effective and multifunctional electrochemical systems. These systems aim to achieve both zero -carbon emissions and the dual capability to convert and store energy ef ficiently. The electrochemical splitting of water is one way to create carbon -neutral, clean hydrogen gas. Electrocatalysis and hydrogen evolution in general depends not only on the catalyst but also on its nano- and microstructure, which in fluences local chemical conditions and hydrogen gas bubble detachment. Therefore, rapid screening of not only potential catalysts but also various structured surfaces is needed for effective electrode fabrication. The fused deposition modeling (FDM) method of 3D printing is frequently used for electrode fabrication using conducting filaments; however, its micro-structuration resolution is limited. Stereolithography can produce complex and fine structures; however, the resins are not conductive and therefore the structures are not suitable for electrode fabrication. In this work, we have fabricated a substrate with highresolution needle array architecture using stereolithographic (SLA) 3D printing and coated it with Co 3 Te 4 - CoTe 2 (COT) nano fiber for water splitting and energy storage. The SLA 3D -printed cobalt telluride electrodes showed appreciable performance as a photoelectrocatalyst for the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER), acting as a bifunctional catalyst. We also demonstrated fabrication of a cobalt telluride based SLA 3D -printed supercapacitor device with multiscale hierarchy. The SLA 3D -printed supercapacitor device exhibited good electrochemical behavior along with high cycling stability. In general, we show here a universal method for SLA conductive electrode fabrication with hierarchical structuring of functional elements and suitable for various applications.

Browse

Recent Submissions