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.

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Now showing 1 - 20 out of 99 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.
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.
Comparing conventional physician-led education with VR education for pacemaker implantation: A randomized study
(MDPI, 2024) Drozdová, Adéla; Poloková, Karin; Jiravský, Otakar; Jiravská Godula, Bogna; Chovančík, Jan; Ranič, Ivan; Jiravský, Filip; Hečko, Jan; Škňouřil, Libor
Introduction: Education of patients prior to an invasive procedure is pivotal for good cooperation and knowledge retention. Virtual reality (VR) is a fast-developing technology that helps educate both medical professionals and patients. Objective: To prove non-inferiority of VR education compared to conventional education in patients prior to the implantation of a permanent pacemaker (PPM). Methods: 150 participants scheduled for an elective implantation of a PPM were enrolled in this prospective study and randomized into two groups: the VR group (n = 75) watched a 360 degrees video about the procedure using the VR headset Oculus Meta Quest 2, while the conventional group (n = 75) was educated by a physician. Both groups filled out a questionnaire to assess the quality of education pre- and in-hospital, their knowledge of the procedure, and their subjective satisfaction. Results: There was no significant difference in the quality of education. There was a non-significant trend towards higher educational scores in the VR group. The subgroup with worse scores was older than the groups with higher scores (82 vs. 76 years, p = 0.025). Anxiety was reduced in 92% of participants. Conclusion: VR proved to be non-inferior to conventional education. It helped to reduce anxiety and showed no adverse effects.
Nanoarchitectonics of laser induced MAX 3D-printed electrode for photo-electrocatalysis and energy storage application with long cyclic durability of 100 000 cycles
(Wiley, 2024) Nouseen, Shaista; Deshmukh, Sujit; Pumera, Martin
3D printing, a rapidly expanding domain of additive manufacturing, enables the fabrication of intricate 3D structures with adjustable fabrication parameters and scalability. Nonetheless, post-fabrication, 3D-printed materials often require an activation step to eliminate non-conductive polymers, a process traditionally achieved through chemical, thermal, or electrochemical methods. These conventional activation techniques, however, suffer from inefficiency and inconsistent results. In this study, a novel chemical-free activation method employing laser treatment is introduced. This innovative technique effectively activates 3D-printed electrodes, which are then evaluated for their photo and electrochemical performance against traditional solvent-activated counterparts. The method not only precisely ablates surplus non-conductive polymers but also exposes and activates the underlying electroactive materials. The 3D-printed electrodes, processed with this single-step laser approach, exhibit a notably low overpotential of approximate to 505 mV at a current density of -10 mA cm(-2) under an illumination wavelength of 365 nm. These electrodes also demonstrate exceptional durability, maintaining stability through >100 000 cycles in energy storage applications. By amalgamating 3D printing with laser processing, the creation of electrodes with complex structures and customizable properties is enabled. This synergy paves the way for streamlined production of such devices in the field of energy conversion and storage.
Complexity-based analysis of the variations in the brain response of porn-addicted and healthy individuals under different function tasks
(World Scientific Publishing Co Pte Ltd, 2024) Pakniyat, Najmeh; Ramadoss, Janarthanan; Karthikeyan, Anitha; Penhaker, Marek; Krejcar, Ondřej; Namazi, Hamidreza
The examination of brain responses in individuals with a pornography addiction compared to those without sheds light on the neurobiological aspects associated with this behavior. Neuroscientific studies utilizing techniques such as electroencephalography (EEG) have shown that porn-addicted individuals may exhibit alterations in neural pathways related to reward processing and impulse control. In this paper, we analyzed the variations in the brain response of porn-addicted versus healthy individuals under five function tasks including baseline, emotional state, memorize task, executive task, and recall task. For this purpose, we analyzed the complexity of EEG signals using fractal theory, approximate entropy (ApEn), and sample entropy. The results showed that the EEG signals of porn-addicted teenagers are more complex than the ones for healthy individuals, which reflects a higher level of brain activity for porn-addicted teenagers. This method of analysis can be extended to examine the brain activity of other types of addiction versus healthy brains.
Downsizing nanoarchitectonics of multilayered MXenes electrocatalysts towards real time ion tracking via EQCM and electrocatalytic applications
(Elsevier, 2024) Padinjareveetil, Akshay Kumar K.; Pumera, Martin
Since their discovery, engineering two-dimensional (2D) MXene materials have attracted rapid interest in both energy storage and conversion applications. Among the several techniques being introduced for enhancing material properties, downsizing is one among the interesting approaches. Downsizing involves the deliberate scaling down of active 2D materials, such as MXenes, systematically. Although major studies are focused on the electrochemical applications of single layered MXene flakes, curiosity in evaluating the downsized multilayered MXenes for electrochemical applications motivates this project. Thus, in the current study, the multilayered bulk MXenes are sequentially stepped down to smaller multilayered fragments at definite time intervals, and subsequently the potential of this procured heterogeneous polydispersed solution are evaluated towards electrochemical applications without any further post -treatments. Real time monitoring of lithium -ion exchange in the downsized MXene materials at various time intervals was tracked using the electrochemical quartz crystal microbalance technique, where the downsized MXene system exhibited water -assisted lithium -ion transfer behavior. Increase in mass exchange was found to increase with increase in downsized MXene systems, thus making it very interesting towards ion storage applications. Further, downsized MXene electrocatalyst material delivered the lowest onset potential for hydrogen production among the set of catalysts studied. In short, this study outlines and pioneers some interesting observations regarding both energy storage and catalytic applications of multilayered downsized MXenes, thereby opening up new possibilities for facile, rapid and cost-effective material fabrication approaches.
Analysis on fetal phonocardiography segmentation problem by hybridized classifier
(Elsevier, 2024) Kong, Lingping; Barnová, Kateřina; Jaroš, René; Mirjalili, Seyedali; Snášel, Václav; Pan, Jeng-Shyang; Martinek, Radek
Fetal examinations are a significant and challenging field of healthcare. Cardiotocography is the most commonly used method for monitoring fetal heart rate and uterine contractions. As a promising alternative to cardiotocography, fetal phonocardiography is beginning to emerge. It is an entirely non-invasive, passive, and low-cost method. However, it is tough to estimate the ideal form of the fetal sound signal in most cases due to the presence of disturbances. The disturbances originate from movements or rotations of the fetal body, making fetal heart sound processing difficult. This study presents an automatic method for segmenting the fetal heart sounds in a phonocardiographic signal that is loaded with different types of disturbances and analyzes which of these disturbances most affect segmentation accuracy. To provide a comprehensive investigation, we propose a hybrid classifier based on Transformer and eXtreme Gradient Boosting, short for XGBoost, to improve segmentation performance by decision -making integration. 2000 segments of data from the Research Resource for Complex Physiologic Signals, PhysioNet repository, and created synthetic data (873 recordings) were used for the experiment. In the S1 label, our proposed method ranks first among all compared algorithms in precision, recall, F1, and accuracy score, tying with Transformer in recall score. It achieves an accuracy increase of 5% and 1.3% compared to XGBoost and Transformer, respectively. Similarly, in the S2 label, there is a precision score increase of 5.8% and 3.7% compared to XGBoost and Transformer, respectively. In general, our proposed method shows effective and promising performance..
A comprehensive analysis of cognitive CAPTCHAs through eye tracking
(IEEE, 2024) Dinh, Nghia; Ogiela, Lidia Dominika; Kiet, Tran-Trung; Tuan, Le-Viet; Hoang, Vinh Truong
CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) has long been employed to combat automated bots. It accomplishes this by utilizing distortion techniques and cognitive characteristics. When it comes to countering security attacks, cognitive CAPTCHA methods have proven to be more effective than other approaches. The advancement of eye-tracking technology has greatly improved human-computer interaction (HCI), enabling users to engage with computers without physical contact. This technology is widely used for studying attention, cognitive processes, and performance. In this specific research, we conducted eye-tracking experiments on participants to investigate how their visual behavior changes as the complexity of cognitive CAPTCHAs varies. By analyzing the distribution of eye gaze on each level of CAPTCHA, we can assess users’ visual behavior based on eye movement performance and process metrics. The data collected is then employed in Machine Learning (ML) algorithms to categorize and examine the relative importance of these factors in predicting performance. This study highlights the potential to enhance any cognitive CAPTCHA model by gaining insights into the underlying cognitive processes.
Single atom engineered materials for sensors
(Elsevier, 2024) Pumera, Martin; Thakkar, Parth
The pursuit of high-performance sensors necessitates the exploration of new materials to realize this potential. Single atom engineering (SAE) is used to plant individual atoms into the appropriate surrounding of (nano)materials in order to confer distinct materials properties. Hence, the nano architectonics principle utilized for single atom engineering allows us to build highly specific, selective, and sensitive sensors, taking these parameters to new and unprecedented levels. Such improvements derive from single atom–material interactions, coordination geometry, and environment, which in turn influence the electronic structure of the implanted atom and its surroundings. In this review, we briefly discuss the preparation and characterization of single atom engineered materials, and focus on their application for gas sensing, chemical sensing in the liquids, and biosensing. Single atom engineered materials offer tunable properties that in many cases enhance signal amplification and selectivity.
Programming self-assembling magnetic microrobots with multiple physical and chemical intelligence
(Elsevier, 2024) Mayorga-Martinez, Carmen C.; Zelenka, Jaroslav; Přibyl, Tomáš; Marzo, Adaris Lopez; Životský, Ondřej; Ruml, Tomáš; Pumera, Martin
Medical microrobots represent the cutting-edge of biomedical research, showcasing their potential as versatile tools. They exhibit promise in acting as carriers for cancer cell therapy, effectively delivering drugs, and as manipulators equipped for biosensing, offering mobility and adaptability. Despite these advancements, the intricate challenge of creating a microrobot that seamlessly integrates various physical and chemical functionalities persists. This includes the fusion of selective sensing, manipulation capabilities, carrier functionality, precise time-based actuators for motion control, and adaptive shaping. Addressing these complexities remains an ongoing endeavor. In this context, our work introduces a pioneering magnetic microrobot founded on CaCO3 microparticles (MPs) synthesized alongside polyethylenimine (CaCO3-PEI), forming the core body. This is combined with Fe3O4 nanoparticles (NPs) enveloped in glutaraldehyde (Fe3O4-Glu), constituting the propulsive engine. The synergy of these elements enables the microrobot to execute multimodal motions, orchestrating its movement with finesse. This dynamic capability follows a “deliver-and-return” pattern for precise targeting applications with real-world relevance. Furthermore, the Fe3O4-Glu/CaCO3-PEI microrobots demonstrated remarkable proficiency in the targeted identification, manipulation, and transportation of cancer cells through the strategic integration of specific antibodies onto their structure. Within the realm of selective cancer cell detection, these microrobots adeptly function as dynamic mobile immunosensors. The versatile utility of the Fe3O4-Glu/CaCO3-PEI microrobots extends to their role as carriers for drugs and imaging agents, facilitated by the mediation of extracellular pH modulation in cancer cells orchestrated by CaCO3. This innovative work introduces a novel “on-the-fly” concept, revolutionizing the landscape of robotics programmed with multifaceted chemical and physical intelligences.
Sarcopenia and adipose tissue evaluation by artificial intelligence predicts the overall survival after TAVI
(Springer Nature, 2024) Pekař, Matej; Jiravský, Otakar; Novák, Jan; Branny, Piotr; Balušík, Jakub; Daniš, Daniel; Hečko, Jan; Kantor, Marek; Prosecký, Robert; Blaha, Lubomír; Neuwirth, Radek
Sarcopenia is a serious systemic disease that reduces overall survival. TAVI is selectively performed in patients with severe aortic stenosis who are not indicated for open cardiac surgery due to severe polymorbidity. Artificial intelligence-assisted body composition assessment from available CT scans appears to be a simple tool to stratify these patients into low and high risk based on future estimates of all-cause mortality. Within our study, the segmentation of preprocedural CT scans at the level of the lumbar third vertebra in patients undergoing TAVI was performed using a neural network (AutoMATiCA). The obtained parameters (area and density of skeletal muscles and intramuscular, visceral, and subcutaneous adipose tissue) were analyzed using Cox univariate and multivariable models for continuous and categorical variables to assess the relation of selected variables with all-cause mortality. 866 patients were included (median(interquartile range)): age 79.7 (74.9–83.3) years; BMI 28.9 (25.9–32.6) kg/m2. Survival analysis was performed on all automatically obtained parameters of muscle and fat density and area. Skeletal muscle index (SMI in cm2/m2), visceral (VAT in HU) and subcutaneous adipose tissue (SAT in HU) density predicted the all-cause mortality in patients after TAVI expressed as hazard ratio (HR) with 95% confidence interval (CI): SMI HR 0.986, 95% CI (0.975–0.996); VAT 1.015 (1.002–1.028) and SAT 1.014 (1.004–1.023), all p < 0.05. Automatic body composition assessment can estimate higher all-cause mortality risk in patients after TAVI, which may be useful in preoperative clinical reasoning and stratification of patients.
Magnetic microrobot swarms with polymeric hands catching bacteria and microplastics in water
(American Chemical Society, 2024) Ussia, Martina; Urso, Mario; Oral, Cagatay M.; Peng, Xia; Pumera, Martin
The forefront of micro- and nanorobot research involves the development of smart swimming micromachines emulating the complexity of natural systems, such as the swarming and collective behaviors typically observed in animals and microorganisms, for efficient task execution. This study introduces magnetically controlled microrobots that possess polymeric sequestrant “hands” decorating a magnetic core. Under the influence of external magnetic fields, the functionalized magnetic beads dynamically self-assemble from individual microparticles into well-defined rotating planes of diverse dimensions, allowing modulation of their propulsion speed, and exhibiting a collective motion. These mobile microrobotic swarms can actively capture free-swimming bacteria and dispersed microplastics “on-the-fly”, thereby cleaning aquatic environments. Unlike conventional methods, these microrobots can be collected from the complex media and can release the captured contaminants in a second vessel in a controllable manner, that is, using ultrasound, offering a sustainable solution for repeated use in decontamination processes. Additionally, the residual water is subjected to UV irradiation to eliminate any remaining bacteria, providing a comprehensive cleaning solution. In summary, this study shows a swarming microrobot design for water decontamination processes.
Magnetic soft centirobot to mitigate biological threats
(Wiley, 2024) Vaghasiya, Jayraj V.; Mayorga-Martinez, Carmen C.; Zelenka, Jaroslav; Sharma, Shelja; Ruml, Tomáš; Pumera, Martin
Soft robots have drawn a lot of interest in the field of human–robot interfaces because they can mimic the propulsion of soft bodies and archive complex tasks that cannot be made by rigid robots such as performing the complex motion, avoiding collisions by absorbing impacts, and shape adaptation by elastic deformation. Herein, drawing inspiration from creatures in the Cambrian period, such as Hallucigenia, we develop a centimeter-sized soft robot with multiple magnetic legs (referred to as a soft centirobot). This robot is equipped with graphitic carbon nitride (g-C3N4) nanosheets to kill biological threats by photogenerated reactive oxygen species under black light illumination. The motion of g-C3N4 soft centirobot is controlled by magnetic actuation even in complex wastewater samples (with a relative speed of 0.12 body lengths per second). The magnetic multilegs work as a propeller to walk across and cover large regions, and water disinfection is more efficient than what could be achieved by nano/micrometer scale sheets of g-C3N4. Finally, factors affecting the accelerated propulsion of g-C3N4 soft centirobot such as design principle, structure geometry, body mass, driving mechanism, and magnetic sensitivity, have been investigated. We envision that such a photoactive 2D material-based integrated centimeter-sized robot shall find application in many areas where pathogen removal is required.
Intelligent magnetic microrobots with fluorescent internal memory for monitoring intragastric acidity
(Wiley, 2024) Senthilnathan, N.; Oral, Cagatay M.; Novobilský, Adam; Pumera, Martin
This study investigates the dynamic ﬂuctuations of pH caused by gastric acidsecretion, a process of both biological and clinical signiﬁcance, withmicrorobots. Abnormal patterns of acidity often indicate gastrointestinaldiseases, underlying the importance of precise intragastric pH monitoring.Traditional methods using ﬂuorescent probes face challenges due to theirfaint solid-state ﬂuorescence, limited target speciﬁcity, and accuracy. Toovercome these obstacles, pH-responsive ﬂuorescent organic microparticlesdecorated with magnetite (Fe 3 O4 ) nanoparticles are engineered. Thesemicrorobots exhibit a unique ﬂuorescence switching capability at a critical pH,enabling the monitoring of gastric acidity. The magnetic part of thesemicrorobots ensures magnetic maneuverability to enable targeted navigation.The microrobots’ ﬂuorescence switching mechanism is elucidated throughcomprehensive spectroscopy, microscopy, and X-ray diﬀraction analyses,revealing molecular-level structural transformations upon interaction withgastric acid and antacids. These transformations, speciﬁcally protonation anddeprotonation of the microrobots’ ﬂuorescent components, prompt a distinctﬂuorescence response correlating with pH shifts. In vitro and ex vivoexperiments, simulating stomach conditions, conﬁrm the microrobots’eﬃcacy in pH-responsive imaging. The results showcase the promisingdiagnostic potential of microrobots for gastrointestinal tract diseases,marking a signiﬁcant advancement in imaging-based medical diagnostics attargeted locations.

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