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Gdańsk University of Technology

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Publications from the year 2023

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  • LOS and NLOS identification in real indoor environment using deep learning approach
    • Alicja Olejniczak
    • Olga Błaszkiewicz
    • Krzysztof Cwalina
    • Piotr Rajchowski
    • Jarosław Sadowski
    2023 Full text Digital Communications and Networks

    Visibility conditions between antennas, i.e. Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) can be crucial in the context of indoor localization, for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning. In this paper a deep learning (DL) model to classify LOS/NLOS condition while analyzing two Channel Impulse Response (CIR) parameters: Total Power (TP) [dBm] and First Path Power (FP) [dBm] is proposed. The experiments were conducted using DWM1000 DecaWave radio module based on measurements collected in a real indoor environment and the proposed architecture provides LOS/NLOS identification with an accuracy of more than 100% and 95% in static and dynamic senarios, respectively. The proposed model improves the classification rate by 2-5% compared to other machine learning (ML) methods proposed in the literature.

  • (Lost) Pride and Prejudice. Journalistic Identity Negotiation Versus the Automation of Content
    • Jan Kreft
    • Monika Boguszewicz-Kreft
    • Mariana Fydrych
    2023 Journalism Practice

    The objective of our research was to broaden the knowledge regarding the relationship between the work of journalists and their professional identity, and, in particular, to identify the attitudes of this professional group towards algorithmic content creation under conditions of liminality. Previously, the implementation of the technology of algorithmic content creation by media organisations was associated primarily with financial factors (production savings). The pandemic situation, for security reasons forcing the use of new technologies to perform remote work, became an additional factor enhancing the sense of liminality. A qualitative study was conducted in the form of 25 in-depth interviews in leading Polish media at the initial stage of the pandemic. The results showed that the most important aspect concerning liminality was the loss of pride in performing a prestigious profession. Following waves of financial savings in editorial offices, and after the pandemic, journalists viewed the algorithmic creation of content as the next potential “plague” affecting their perceived degradation of the profession. The anticipated change in working conditions, already interpreted as a threat to journalists, signified a liminal experience dictated by a new factor and prompted them to choose defence strategies.

  • Low-cost 3D Printed Circularly Polarized Lens Antenna for 5.9 GHz V2X Applications
    • Weronika Kalista
    • Luiza Leszkowska
    • Mateusz Rzymowski
    • Krzysztof Nyka
    • Łukasz Kulas
    2023

    This paper presents design and realization of a circularly polarized antenna consisting of a linearly polarized patch antenna and a 3D printed lens, at the same time performing the functions of wave collimator and a polarizer. The antenna is dedicated for 802.11p systems, as a part of road infrastructure, with operation bandwidth 5.85 - 5.925 GHz. Its realised gain and axial ratio at center frequency 5.9 GHz are 14.3 dBi and 2.17 dB respectively. The lens provides approximately 6% bandwidth with axial ratio below 3 dB. The proposed antenna is easy to design and fabricate and can be realized with the use of low-cost materials.

  • Low-Cost and Highly-Accurate Behavioral Modeling of Antenna Structures by Means of Knowledge-Based Domain-Constrained Deep Learning Surrogates
    • Sławomir Kozieł
    • Nurullah Calik
    • Peyman Mahouti
    • Mehmet Belen
    2023 IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION

    The awareness and practical benefits of behavioral modeling methods have been steadily growing in the antenna engineering community over the last decade or so. Undoubtedly, the most important advantage thereof is a possibility of a dramatic reduction of computational expenses associated with computer-aided design procedures, especially those relying on full-wave electromagnetic (EM) simulations. In particular, the employment of fast replacement models (surrogates) allows for repetitive evaluations of the antenna structure at negligible cost, thereby accelerating processes such as parametric optimization, multi-criterial design, or uncertainty quantification. Notwithstanding, a construction of reliable data-driven surrogates is seriously hindered by the curse of dimensionality and the need for covering broad ranges of geometry/material parameters, which is imperative from the perspective of design utility. A recently proposed constrained modeling approach with knowledge-based stochastic determination of the model domain addresses this issue to a large extent and has been demonstrated to enable quasi-global modeling capability while maintaining a low setup cost. This work introduces a novel technique that capitalizes on the domain confinement paradigm and incorporates deep-learning-based regression modeling to facilitate handling of highly-nonlinear antenna characteristics. The presented framework is demonstrated using three microstrip antennas and favorably compared to several state-of-the-art techniques. The predictive power of our models reaches remarkable two percent of a relative RMS error (averaged over the considered antenna structures), which is a significant improvement over all benchmark methods.

  • Low-Cost Behavioral Modeling of Antennas by Dimensionality Reduction and Domain Confinement
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    • Leifur Leifsson
    2023

    Behavioral modeling has been rising in importance in modern antenna design. It is primarily employed to diminish the computational cost of procedures involving massive full-wave electromagnetic (EM) simulations. Cheaper alternative offer surrogate models, yet, setting up data-driven surrogates is impeded by, among others, the curse of dimensionality. This article introduces a novel approach to reduced-cost surrogate modeling of antenna structures, which focuses the modeling process on design space regions containing high-quality designs, identified by randomized pre-screening. A supplementary dimensionality reduction is applied via the spectral analysis of the random observable set. The reduction process identifies the most important directions from the standpoint of geometry parameter correlations, and spans the domain along a small subset thereof. As demonstrated, domain confinement as outlined above permits a dramatic improvement of surrogate accuracy in comparison to the state-of-the-art modeling approaches.

  • Low-Cost Open-Hardware System for Measurements of Antenna Far-Field Characteristics in Non-Anechoic Environments
    • Jan Olencki
    • Vorya Waladi
    • Adrian Bekasiewicz
    2023 Full text

    Experimental validation belongs to the most important steps in the development of antenna structures. Measurements are normally performed in expensive, dedicated facilities such as anechoic chambers, or open-test sites. A high cost of their construction might not be justified when the main goal of antenna verification boils down to demonstration of the measurement procedure, or rough validation of the simulation models used for the development of the structure. Although solutions for far-field measurement of antennas in non-anechoic environments have been demonstrated in the literature, they utilize expensive equipment. In this work, a low-cost (around 3300 USD), system for experimental validation of antenna prototypes in non-anechoic conditions has been discussed. Its main components include the in-house developed heads and an open-hardware-based vector network analyzer. Performance of the system has been demonstrated using two antenna structures for which radiation patterns have been obtained. Comparisons against measurements performed in the anechoic chamber and using other expensive equipment have also been provided.

  • Low-frequency noise in Au-decorated graphene–Si Schottky barrier diode at selected ambient gases
    • Janusz Smulko
    • Katarzyna Drozdowska
    • Adil Rehman
    • Tesfalem Welearegay
    • Lars Österlund
    • Sergey Rumyantsev
    • Grzegorz Cywiński
    • Bartłomiej Stonio
    • Aleksandra Krajewska
    • Maciej Filipiak
    • Pavlo Sai
    2023 Full text APPLIED PHYSICS LETTERS

    We report results of the current–voltage characteristics and low-frequency noise in Au nanoparticle (AuNP)-decorated graphene–Si Schottky barrier diodes. Measurements were conducted in ambient air with addition of either of two organic vapors, tetrahydrofuran [(CH2)4O; THF] and chloroform (CHCl3), as also during yellow light illumination (592nm), close to the measured particle plasmon polariton frequency of the Au nanoparticle layer. We observed a shift of the DC characteristics at forward voltages (forward resistance region) when tetrahydrofuran vapor was admitted (in a Au-decorated graphene–Si Schottky diode), and a tiny shift under yellow irradiation when chloroform was added (in not decorated graphene–Si Schottky diode). Significantly larger difference in the low-frequency noise was observed for the two gases during yellow light irradiation, compared with no illumination. The noise intensity was suppressed by AuNPs when compared with noise in graphene–Si Schottky diode without an AuNP layer. We conclude that flicker noise generated in the investigated Audecorated Schottky diodes can be utilized for gas detection.

  • Low-frequency noise in ZrS3 van der Waals semiconductor nanoribbons
    • Adil Rehman
    • Grzegorz Cywiński
    • W. Knap
    • Janusz Smulko
    • Alexander Balandin
    • Sergey Rumyantsev
    2023 Full text APPLIED PHYSICS LETTERS

    We report the results of the investigation of low-frequency electronic noise in ZrS3 van der Waals semiconductor nanoribbons. The test structures were of the back-gated field-effect-transistor type with a normally off n-channel and an on-to-off ratio of up to four orders of magnitude. The current–voltage transfer characteristics revealed significant hysteresis owing to the presence of deep levels. The noise in ZrS3 nanoribbons had spectral density SI ~ 1/f^c (f is the frequency) with c ~ 1.3–1.4 within the whole range of the drain and gate bias voltages. We used light illumination to establish that the noise is due to generation–recombination, owing to the presence of deep levels, and determined the energies of the defects that act as the carrier trapping centers in ZrS3 nanoribbons.

  • Low-Voltage LDO Regulator Based on Native MOS Transistor with Improved PSR and Fast Response
    • Grzegorz Blakiewicz
    2023 Full text ENERGIES

    In this paper, a low-voltage low-dropout analog regulator (ALDO) based on a native n-channel MOS transistor is proposed. Application of the native transistor with the threshold voltage close to zero allows elimination of the charge pump in low-voltage regulators using the pass element in a common drain configuration. Such a native pass transistor configuration allows simplification of regulator design and improved performance, with supply voltages below 1 V, compared to commonly used regulators with p-channel MOS transistors. In the presented design of ALDO regulator in 180 nm CMOS X-FAB technology, an output voltage of 0.7 V was achieved with an output current of 10 mA and a supply voltage of 0.8 V. Simulation results show that despite the low supply voltage, output voltage spikes do not exceed 70 mV at the worst technology corner when output current transients from 100 uA to 10 mA. Under such conditions, stable operation and power supply rejection PSR = 35 dB were achieved with an output capacitance of 0–500 pF. The proposed regulator allows to push the limit of ALDO regulator applications to voltages below 1 V with only slight degradation of its performance.

  • Low-volume label-free SARS-CoV-2 detection with the microcavity-based optical fiber sensor
    • Monika Janik
    • Tomasz Gabler
    • Marcin Koba
    • Mirosława Panasiuk
    • Yanina Dashkevich
    • Tomasz Łęga
    • Agnieszka Dąbrowska
    • Antonina Naskalska
    • Sabina Żołędowska
    • Dawid Nidzworski
    • Krzysztof Pyrć
    • Beata Gromadzka
    • Mateusz Śmietana
    2023 Full text Scientific Reports

    Accurate and fast detection of viruses is crucial for controlling outbreaks of many diseases; therefore, to date, numerous sensing systems for their detection have been studied. On top of the performance of these sensing systems, the availability of biorecognition elements specific to especially the new etiological agents is an additional fundamental challenge. Therefore, besides high sensitivity and selectivity, such advantages as the size of the sensor and possibly low volume of analyzed samples are also important, especially at the stage of evaluating the receptor-target interactions in the case of new etiological agents when typically, only tiny amounts of the receptor are available for testing. This work introduces a real-time, highly miniaturized sensing solution based on microcavity in-line Mach–Zehnder interferometer (μIMZI) induced in optical fiber for SARS-CoV-2 virus-like particles detection. The assay is designed to detect conserved regions of the SARS-CoV-2 viral particles in a sample with a volume as small as hundreds of picoliters, reaching the detection limit at the single ng per mL level.

  • Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects
    • Hammed Mojeed
    • Rafał Szłapczyński
    2023

    Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects Hammed A. Mojeed & Rafal Szlapczynski Conference paper First Online: 14 September 2023 161 Accesses Part of the Lecture Notes in Computer Science book series (LNAI,volume 14125) Abstract Software development project requires proper planning to mitigate risk and uncertainty. Overtime planning within software project management has been receiving attention recently from search-based software engineering researchers. Multi-objective evolutionary algorithms are used to build automated tools that could effectively help Project Managers (PM) plan overtime on project schedules. Existing models however lack applicability by the PMs due to their disregard for expert knowledge in planning overtime. This study proposes a new interactive problem formulation for software overtime planning and presents a framework for building a machine learning-based interactive multi-objective optimization algorithm for overtime planning in software development projects. The framework is designed to train a priori a machine learning model to mimic the PM’s subjective judgment of overtime plans within the project schedule. The machine learning model is integrated with a memetic multi-objective optimization algorithm via an interactive module. Also, the memetic algorithm incorporates a preference-based w-dominance method for selecting non-dominated solutions. The proposed framework will be developed to assist software project managers to better plan overtime in order to prevent the expected risk of software development overrun

  • Machine learning-based prediction of preplaced aggregate concrete characteristics
    • Farzam Omidi Moaf
    • Farzin Kazemi
    • Hakim S. Abdelgader
    • Marzena Kurpińska
    2023 ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

    Preplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile strengths of PAC, this research developed 12 supervised Machine Learning (ML) algorithms in Python software to provide estimations for civil engineers. To prepare the training and testing datasets, a comprehensive investigation was performed to prepare experimental studies on the compressive and tensile strengths of PAC. Then, according to the features of the dataset, four scenarios were defined based on the input features. The capability of ML algorithms was investigated in each scenario. Results showed that the ETR, RDF, and BR algorithms achieved the prediction accuracy of 98.3%, 95.3% and 94.6%, respectively, for estimating the compressive strength of PAC with input features of Case B. Therefore, due to the performance of the ML models, their generality was investigated by preparing the experimental test of two specimens of PAC and by validating the results. Notably, that the proposed ML models (e.g. BR method) can accurately predict the compressive and tensile strengths of specimens (e.g. with accuracy of 98.4 99.7%, respectively) and can be used to facilitate and reduce the experimental tests as well as the experimental efforts.

  • Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction
    • Neda Asgarkhani
    • Farzin Kazemi
    • Robert Jankowski
    2023 COMPUTERS & STRUCTURES

    Nowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To provide an accurate yet reliable prediction model, 32 improved Machine Learning (ML) algorithms were considered using the Python software to investigate the best method for estimating Maximum Interstory Drift Ratio (IDRmax) and RIDR of 384 Steel Moment-Resisting Frames (SMRFs). In addition, the curve plot ability of methods was investigated to provide an estimation of Median of IDA curve (IDAMed) and Seismic Failure Probability curve (SFPCurve) considering Soil-Structure Interaction (SSI) effects. It is noteworthy that ML algorithms were improved with a pipeline-based hyper-parameters Fine-Tuning (FT) method followed by forward and backward feature selection methodologies to avoid overfitting and data leakage issues. The improved methods were evaluated to find the best prediction model regarding seismic demands. The results show that proposed methods have higher prediction accuracy and curve fitting ability (i.e. more than 95%) that can be used to estimate IDAMed and SFPCurve of a structure to accelerate the seismic risk assessment. A prediction tool is introduced to use the methods of this study for estimating abovementioned seismic demands.

  • Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction
    • Farzin Kazemi
    • Robert Jankowski
    2023 COMPUTERS & STRUCTURES

    Regarding the unpredictable and complex nature of seismic excitations, there is a need for vulnerability assessment of newly constructed or existing structures. Predicting the seismic limit-state capacity of steel Moment-Resisting Frames (MRFs) can help designers to have a preliminary estimation and improve their views about the seismic performance of the designed structure. This study improved data-driven decision techniques in Python software, known as supervised Machine Learning (ML) algorithms, to find median IDA curves (M-IDAs) for predicting the seismic limit-state capacities of steel MRFs considering Soil-Structure Interaction (SSI) effects. For this purpose, Incremental Dynamic Analyses (IDAs) were per-formed on the steel MRFs from two to nine-story elevations modeled in Opensees subjected to three ground motion subsets of Far Fault (FF), near-fault Pulse-Like (PL) and No-Pulse (NP) suggested by FEMA-P695. The result of the analysis confirmed that there is no specific model for predicting the M-IDA curve of steel structures; therefore, the best developed ML algorithms to reduce a complex modelling process with high computational cost using 128,000 data points were proposed. To provide convenient access to prediction results, Graphical User Interface (GUI) was developed to predict Sa (T1) of seismic limit-state performance levels with a large database based on prediction models.

  • Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
    • Farzin Kazemi
    • Neda Asgarkhani
    • Robert Jankowski
    2023 SOIL DYNAMICS AND EARTHQUAKE ENGINEERING

    Many studies have been performed to put quantifying uncertainties into the seismic risk assessment of reinforced concrete (RC) buildings. This paper provides a risk-assessment support tool for purpose of retrofitting and potential design strategies of RC buildings. Machine Learning (ML) algorithms were developed in Python software by innovative methods of hyperparameter optimization, such as halving search, grid search, random search, fine-tuning method, and the k-fold cross-validation, to derive the seismic fragility curve for accelerating seismic risk assessment. Proposed ML methods significantly reduced the computational efforts compared to conventional procedure of seismic fragility assessment. The prediction results can be combined with considered hazard curves for the purpose of seismic risk assessment of RC buildings. To prepare the training dataset, Incremental Dynamic Analyses (IDAs) were performed on 165 RC frames to achieve 1121184 data points. Performance indicators showed that the algorithms of Artificial Neural Networks (ANNs), Extra-Trees Regressor (ETR), Extremely Randomized Tree Regressor (ERTR), Bagging Regressor (BR), Extreme Gradient Boosting (XGBoost), and Histogram-based Gradient Boosting Regression (HGBR) had higher performance, which achieved acceptable accuracy and fitted to actual curves. In addition, Graphical User Interface (GUI) was introduced as a practical tool yet reliable for seismic risk assessment of RC buildings.

  • Machine learning-based seismic response and performance assessment of reinforced concrete buildings
    • Farzin Kazemi
    • Neda Asgarkhani
    • Robert Jankowski
    2023 Full text Archives of Civil and Mechanical Engineering

    Complexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data points of training dataset for developing data-driven techniques, Incremental Dynamic Analyses (IDAs) were performed considering 165 RC MRFs with two-, to twelve-Story elevations having the bay lengths of 5.0 m, 6.1 m, and 7.6 m assuming near-fault seismic excitations. Then, important structural features were considered in datasets to train and test the ML-based prediction models, which were improved with innovative techniques. The results show that improved algorithms have higher R2 values for estimating the Maximum Interstory Drift Ratio (IDRmax), and two improved algorithms of artificial neural networks and extreme gradient boosting can estimate the Median of IDA curves (M-IDAs) of RC MRFs, which can be used to estimate the seismic limit-state capacity and performance assessment of existing or newly constructed RC buildings. To validate the generality and accuracy of the proposed ML-based prediction model, a five-Story RC building with different input features was used, and the results are promising. Therefore, graphical user interface is introduced as user-friendly tool to help researchers in estimating the seismic limit-state capacity of RC buildings, while reducing the computational cost and analytical efforts.

  • Macro-nutrients recovery from liquid waste as a sustainable resource for production of recovered mineral fertilizer: Uncovering alternative options to sustain global food security cost-effectively
    • Bogna Śniatała
    • Tonni Agustiono Kurniawan
    • Dominika Sobotka
    • Jacek Mąkinia
    • Mohd Hafiz Dzarfan Othman
    2023 SCIENCE OF THE TOTAL ENVIRONMENT

    Global food security, which has emerged as one of the sustainability challenges, impacts every country. As food cannot be generated without involving nutrients, research has intensified recently to recover unused nutrients from waste streams. As a finite resource, phosphorus (P) is largely wasted. This work critically reviews the technical applicability of various water technologies to recover macro-nutrients such as P, N, and K from wastewater. Struvite precipitation, adsorption, ion exchange, and membrane filtration are applied for nutrient recovery. Technological strengths and drawbacks in their applications are evaluated and compared. Their operational conditions such as pH, dose required, initial nutrient concentration, and treatment performance are presented. Cost-effectiveness of the technologies for P or N recovery is also elaborated. It is evident from a literature survey of 310 published studies (1985–2022) that no single technique can effectively and universally recover target macro-nutrients from liquid waste. Struvite precipitation is commonly used to recover over 95 % of P from sludge digestate with its concentration ranging from 200 to 4000 mg/L. The recovered precipitate can be reused as a fertilizer due to its high content of P and N. Phosphate removal of higher than 80 % can be achieved by struvite precipitation when the molar ratio of Mg2+/PO4 3− ranges between 1.1 and 1.3. The applications of artificial intelligence (AI) to collect data on critical parameters control optimization, improve treatment effectiveness, and facilitate water utilities to upscale water treatment plants. Such infrastructure in the plants could enable the recovered materials to be reused to sustain food security. As nutrient recovery is crucial in wastewater treatment, water treatment plant operators need to consider (1) the costs of nutrient recovery techniques; (2) their applicability; (3) their benefits and implications. It is essential to note that the treatment cost of P and/or N-laden wastewater depends on the process applied and local conditions.

  • Magazynowanie ciepła
    • Michał Ryms
    2023

    Magazynowanie ciepła, obok magazynowania energii elektrycznej, w dzisiejszych czasach stanowi podstawę zrównoważonego gospodarowania zasobami surowcowymi. Przyczynia się do poprawy efektywności energetycznej procesów przemysłowych, wydajniejszego ogrzewania obiektów czy pomieszczeń, a dzięki wykorzystaniu różnego rodzaju urządzeń i materiałów sprzyja zmniejszeniu zużycia paliw, w efekcie czego następuje ograniczenie emisji do środowiska nadmiernej ilości gazów cieplarnianych, pyłów, tlenków azotu i innych produktów ubocznych produkcji ciepła. Tym samym magazynowanie ciepła staje się obecnie nie tylko koniecznością, lecz także obowiązkiem, jeśli chcemy optymalizować wykorzystanie paliw i dbać o środowisko, w którym żyjemy. Ciepło można magazynować na wiele sposobów: począwszy od gromadzenia w zasobnikach ciepłej wody użytkowej w domowych instalacjach wodno-grzewczych przez systemy zintegrowane z instalacją wspieraną przez odnawialne źródła energii (kolektory słoneczne, pompy ciepła), skończywszy na komercyjnych magazynach ciepła dużych mocy opartych na odzysku przemysłowej energii odpadowej.

  • Magazynowanie energii elektrycznej
    • Monika Wilamowska-Zawłocka
    2023

    Światowa gospodarka opiera się na energii, dlatego wymaga się, aby energia była łatwo dostępna, stosunkowo tania, a jej dostawy niezawodne. Zmiany klimatyczne powodują jednak, że wzrastają wymogi dotyczące redukcji emisji CO2, a co za tym idzie – zwiększenia udziału „zielonej energii” pochodzącej ze źródeł odnawialnych. Ramy europejskiej polityki klimatyczno-energetycznej do roku 2030 zakładają m.in. redukcję emisji ditlenku węgla o co najmniej 55% względem poziomu z 1990 r. oraz zwiększenie udziału energii ze źródeł odnawialnych do 32%. Dostawy energii ze źródeł odnawialnych nie są stabilne, ponieważ zależą od warunków pogodowych. Aby mieć dostęp do energii zawsze, kiedy jest ona potrzebna, należy zabezpieczyć niezbędny bufor energii w postaci magazynu. Wydajne magazynowanie energii elektrycznej jest zatem kluczowe, aby móc sukcesywnie zwiększać udział energii ze źródeł odnawialnych. Oprócz stacjonarnych magazynów energii ważną rolę w zmniejszaniu emisji CO2 odgrywają samochody elektryczne. Bezemisyjny transport oznacza niższą emisję nie tylko CO2, lecz także innych zanieczyszczeń, tj. bezno(a)piren, tlenki azotu czy pyły zawieszone. Obserwowany w ostatnich latach oraz przewidywany dalszy wzrost udziału samochodów o napędzie hybrydowym i elektrycznym zapewne przyczyni się więc do poprawy jakości powietrza w miastach. Energię elektryczną można magazynować przez konwertowanie jej na energię chemiczną i uwalnianie w pożądanym czasie. Technologie elektrochemicznego magazynowania energii są i będą odgrywać dużą rolę w osiągnięciu założonych celów polityki klimatyczno-energetycznej. W rozdziale omówiono najnowsze trendy dotyczące urządzeń służących do konwersji i magazynowania energii, z uwzględnieniem możliwości wykorzystywania ich w dużej skali jako magazynów stacjonarnych, sprzężonych z odnawialnymi źródłami energii. Poruszone zostaną kwestie dostępności pierwiastków do produkcji urządzeń do magazynowania energii oraz związane z tym problemy geopolityczne. Ponadto przedstawione będą wyzwania i potencjalne kierunki dalszego rozwoju, w tym możliwości uzyskiwania bardziej ekologicznych magazynów energii z wykorzystaniem materiałów pochodzących z recyklingu.

  • Magnetic anisotropy and structural flexibility in the f ield-induced single ion magnets [Co{(OPPh2) (EPPh2)N}2], E = S, Se, explored by experimental and computational methods
    • Eleftherios Ferentinos
    • Demeter Tzeli
    • Silvia Sottini
    • Edgar J.J. Groenen
    • Mykhaylo Ozerov
    • Giordano Poneti
    • Kinga Kaniewska-Laskowska
    • J. Krzystek
    • Panayotis Kyritsis
    2023 Full text DALTON TRANSACTIONS

    During the last few years, a large number of mononuclear Co(II) complexes of various coordination geometries have been explored as potential single ion magnets (SIMs). In the work presented herein, the Co(II) S = 3/2 tetrahedral [Co{(OPPh2)(EPPh2)N}2], E = S, Se, complexes (abbreviated as CoO2E2), bearing chalcogenated mixed donor-atom imidodiphosphinato ligands, were studied by both experimental and computational techniques. Specifically, direct current (DC) magnetometry provided estimations of their zerof ield splitting (zfs) axial (D) and rhombic (E) parameter values, which were more accurately determined by a combination of far-infrared magnetic spectroscopy and high-frequency and-field EPR spectroscopy studies. The latter combination of techniques was also implemented for the S = 3/2 tetrahedral [Co Received 14th October 2022, Accepted 14th January 2023 DOI: 10.1039/d2dt03335f rsc.li/dalton Introduction {(EPiPr2)2N}2], E = S, Se, complexes, confirming the previously determined magnitude of their zfs parameters. For both pairs of complexes (E = S, Se), it is concluded that the identity of the E donor atom does not significantly affect their zfs parameters. High-resolution multifrequency EPR studies of CoO2E2 provided evidence of multiple conformations, which are more clearly observed for CoO2Se2, in agreement with the structural disorder previously established for this complex by X-ray crystallography. The CoO2E2 complexes were shown to be field-induced SIMs, i.e., they exhibit slow relaxation of magnetization in the presence of an external DC magnetic field. Advanced quantum-chemical calculations on CoO2E2 provided additional insight into their electronic and structural properties.