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Gdańsk University of Technology

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Publications from the year 2024

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  • Looking through the past: better knowledge retention for generative replay in continual learning
    • Valeriya Khan
    • Sebastian Cygert
    • Kamil Deja
    • Tomasz Trzciński
    • Bartłomiej Twardowski
    2024 Full text IEEE Access

    In this work, we improve the generative replay in a continual learning setting to perform well on challenging scenarios. Because of the growing complexity of continual learning tasks, it is becoming more popular, to apply the generative replay technique in the feature space instead of image space. Nevertheless, such an approach does not come without limitations. In particular, we notice the degradation of the continually trained model’s performance could be attributed to the fact that the generated features are far from the original ones when mapped to the latent space. Therefore, we propose three modifications that mitigate these issues. More specifically, we incorporate the distillation in latent space between the current and previous models to reduce feature drift. Additionally, a latent matching for the reconstruction and original data is proposed to improve generated features alignment. Further, based on the observation that the reconstructions are better for preserving knowledge, we add the cycling of generations through the previously trained model to make them closer to the original data. Our method outperforms other generative replay methods in various scenarios. Code available at https://github.com/valeriya-khan/looking-through-the-past.

  • Losing influence: the Changing Role of the Merchant Community of Danzig in the Timber Value Chain (1919-1939)
    • Luciano Segreto
    2024 Full text Jahrbuch f. Wirtschaftsgeschichte/Economic History Yearbook

    The article highllghts the changing role of the merchant community of Danzig after the establishment of the Second Polish Republic and the Fee City of Danzig

  • Loss Minimization-Based Sensorless Control of High-Speed Induction Motor Considering Core Loss
    • Tadele Ayana
    • Piotr Kołodziejek
    • Marcin Morawiec
    • Lelisa Wogi
    2024 Full text IEEE Access

    This paper presents loss-minimizing sensorless control (LMC) strategies utilized to optimize the energy of high-speed induction motor (HSIM) drives. A machine’s ability to operate effectively depends on the estimation of its electrical losses. Although copper losses account for the majority of electrical losses in electrical machines, core loss also contributes a major part, particularly in high-speed induction motors. A review of design solutions of power electronic converters to feed HSIMs and the effect of their parameters on iron losses were analyzed. In the gathered literature, HSIM loss analysis was generally performed using software analytical techniques such as finite element methods. There were few real-time loss analysis and loss minimization sensorless control approaches for HSIM in the literature. Finally, the study of sensorless control of 500Hz frequency with synchronous speed of 15000 rpm HSIM with optimal flux and reference reactive torque based optimization for loss minimization through nonlinear control system design was presented as a solution to the evaluated gaps found in the literature and the simulation findings were experimentally verified.

  • Low temperature rotary Stirling engine: conceptual design and theoretical analysis
    • Jacek Kropiwnicki
    2024 APPLIED THERMAL ENGINEERING

    The use of low-temperature energy sources for electricity generation demands a dual focus: a substantial enhancement in the efficiency of energy conversion devices and a reduction in system production costs. Particularly in scenarios where low-temperature energy sources are scarce, this factor can be pivotal in facilitating widespread adoption of such technologies. The Stirling engine emerges as a promising solution capable of meeting these articulated expectations, owing to its straightforward design and utilization of non-toxic, non-flammable, and cost-effective working mediums. This paper introduces a novel concept of a rotary Stirling engine, exhibiting significant potential for operation with low-temperature energy sources. Additionally, an analytical model of the engine is presented, enabling simulations of its operation under varying supply temperatures and geometric configurations. To analyse the impact of internal leaks on the net efficiency and net power of the engine, a modified adiabatic model was introduced. It was observed that utilizing identical heat exchangers for heat supply at 250°C and 100°C could lead to a decline in net efficiency from 8% to 3% for the worst case. Furthermore, an analysis was performed to assess the impact of the heater's overall heat transfer coefficient and engine rotational speed on both net efficiency and net mechanical power for a heat supply temperature of 200°C.

  • Low-Barrier Hydrogen Bond Determines Target-Binding Affinity and Specificity of the Antitubercular Drug Bedaquiline
    • Joanna Słabońska
    • Subrahmanyam Sappati
    • Antoni Marciniak
    • Jacek Czub
    2024 ACS Medicinal Chemistry Letters

    The role of short strong hydrogen bonds (SSHB) in ligand-target binding remains largely unexplored, thereby hin- dering a potentially important avenue in the rational drug de- sign. Here, we investigate the interaction between bedaquiline (Bq), a potent anti-tuberculosis drug, and the mycobacterial ATP synthase, to unravel the role of a specific hydrogen bond to a conserved acidic residue in the target affinity and specificity. Our ab initio molecular dynamics simulations reveal that this bond belongs to the SSHB category and accounts for a substan- tial fraction of the target binding energy. We also demonstrate that the presence of an extra acidic residue (D32), found exclu- sively in mycobacteria, cooperatively enhances the HB strength ensuring the specificity for the mycobacterial target. Consis- tently, we show that the removal of D32 markedly weakens the affinity, leading to Bq resistance associated with mutations of D32 to non-acidic residues. By designing simple Bq analogs, we then explore the possibility to overcome the resistance and po- tentially broaden the Bq antimicrobial spectrum by making the SSHB independent on the presence of the extra acidic residue.

  • Low-Cost 3-D Printed Lens Antenna for Ka-Band Connectivity Applications
    • Kamil Trzebiatowski
    • Weronika Kalista
    • Mateusz Rzymowski
    • Łukasz Kulas
    • Krzysztof Nyka
    2024

    This paper discusses the use of low-cost 3-D printing technology to fabricate dielectric lenses for Ka-band wireless networks. A low-cost FDM alternative to previously presented 3-D printed lens in SLA technology with high performance resin is presented. The presented approach has been demonstrated for a 39 GHz MU-MIMO antenna array modified to realize multibeam or switched-beam antenna that can support demanding energy-efficient applications in millimeter waves. The impact of different 3-D printing settings on the lens performance is also investigated. The results demonstrate that with proper printing settings, low-cost 3-D printed lenses created using FDM process are a viable alternative for high-frequency applications.

  • Low-Cost and Precise Automated Re-Design of Antenna Structures Using Interleaved Geometry Scaling and Gradient-Based Optimization
    • Anna Pietrenko-Dąbrowska
    • Sławomir Kozieł
    2024 KNOWLEDGE-BASED SYSTEMS

    Design of contemporary antennas is an intricate endeavor involving multiple stages, among others, tuning of geometry parameters. In particular, re-designing antennas to different operating frequencies, makes parametric optimization imperative to ensure the best achievable system performance. If the center frequency at the current design is distant from the target one, local tuning methods generally fail, whereas global algorithms (e.g., nature-inspired procedures) incur prohibitive computational expenses, especially when antenna evaluation is performed using full-wave electromagnetic (EM) analysis. In this paper, a novel technique involving automated decision-making has been developed, whose main objective is low-cost and precise re-design of antenna structures over wide ranges of operating frequencies. The employed methodology involves knowledge-based simultaneous scaling of antenna dimensions and gradient-based performance improvements. The two stages are automatically interleaved, and embedded into an iterative optimization procedure. The problem-specific knowledge allows for carrying out the scaling phase, in which fast relocation of the center frequency of the antenna is performed, based on a single EM analysis of the structure. The gradient-based tuning phase enhances the design quality with regard to the assumed objectives. The process defaults to local optimization after the antenna center frequency becomes sufficiently close to the target. The main novelty of the proposed algorithm consists in development of an automated knowledge-based framework of quasi-global search capabilities linking brute-force scaling and design refinement. Our technique has been demonstrated with the use of three microstrip antennas, optimized for best matching and maximum in-band gain. The main findings are that for all structures, satisfactory designs have been identified despite poor starting points, with operating frequencies being away from the assumed targets. At the same time, the computational cost is comparable to conventional local search. The proposed approach is versatile, simple to implement and easy to handle, in particular, its control parameters do not require tailoring to a specific antenna structure at hand.

  • Low-Cost Method for Internal Surface Roughness Reduction of Additively Manufactured All-Metal Waveguide Components
    • Jakub Sorocki
    • Ilona Piekarz
    • Michał Baranowski
    • Adam Lamęcki
    • Alberto Cattenone
    • Stefania Marconi
    • Gianluca Alaimo
    • Nicolo Delmonte
    • Lorenzo Silvestri
    • Bozzi Maurizio
    2024 Full text IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES

    In this study, a novel low-cost polishing method for internal surface roughness reduction of additively manufactured components, developed for waveguide (WG) circuits operating in the millimeter frequency range is proposed. WG components fabricated using powder bed fusion (PBF) generally feature roughness of ten to fifty microns, which influences the increase of roughness-related conductor power losses having a major effect on the electrical performance of additively manufactured allmetal WGs. To improve and decrease the surface roughness of circuits fabricated using PBF, glass microbeads as an abrasive medium are proposed to be used in combination with a rotary tumbler. This technique allows the abrasive medium to efficiently penetrate internal long channels and cavities, having cross section dimensions in the range of sub- to a few millimeters. An experimental study was carried out on an example of WG sections and bandpass filters fabricated using PBF through selective laser melting (SLM), operating within the 8.2 to 40 GHz range. Polishing impact on both mechanical and electrical properties was studied showing surface roughness reduction by 18% and sixth order filter’s insertion loss reduction at 23 GHz by 40% after 24 h of tumbling with 300–400 µm large glass microbeads.

  • Low-cost multiband four-port phased array antenna for sub-6 GHz 5G applications with enhanced gain methodology in Radio-over-fiber systems using modulation instability
    • Hassan Zakeri
    • Rasul Azizpour
    • Parsa Khoddami
    • Gholamreza Moradi
    • Mohammad Alibakhshikenari
    • Chan Hwang See
    • Tayeb Dendini
    • Francisco Falcone
    • Sławomir Kozieł
    • Ernesto Limiti
    2024 Full text IEEE Access

    Phased array antenna (PAA) technology is essential for applications requiring high gain and wide bandwidth, such as sensors, medical, and 5G. Achieving such a design, however, is a challenging and intricate process that calls for precise calculations and a combination of findings to alter the phase and amplitude of each unit. Furthermore, coupling effects between these PAA structure elements can only be completed with the use of full-wave electromagnetic simulation tools. Due to recent advances, radio-over-fiber (RoF) technology has been positioned as a possible alternative for high-capacity wireless communications. This paper presents a low-cost, multiband Sub-6 GHz 5G PAA with enhanced gain achieved through integration with a new specialized RoF system design to improve PAA performance by using the phenomenon of modulation instability (MI). Optimizing the antenna’s Defected Ground Structure (DGS) leads to even more improvement. To enable operation across three distinct frequency bands (Sub6 GHz n78 band (3-3.8 GHz), n79 band (3.8-5 GHz), and n46 band (5-5.5 GHz)), the proposed antenna design features four elliptical patches strategically positioned at the four sides of the ground plane, providing comprehensive 360° coverage in the azimuth plane. Additionally, integrating elliptical slots and upper gaps contributes to improvement. The proposed PAA’s experimentally validated gain values are 5.2 dB, 7.4 dB, and 7.8 dB in the n78, n79, and n46 bands, respectively. For improving the performance of the proposed PAA in RoF systems, anomalous fibers (n2 ̸= 0 and β2 < 0) are employed to consider the modulation instability (MI) phenomenon, which can lead to the generation of the MI gain on the carrier sideband. The true time delay (TTD) technique controls the beam pattern by adjusting the time delay between adjacent radiation elements. Furthermore, the TTD technique utilizes frequency combs for the proposed 4-element array antenna to apply MI gain to all antenna elements.

  • Low-Loss 3D-Printed Waveguide Filters Based on Deformed Dual-Mode Cavity Resonators
    • Michał Baranowski
    • Łukasz Balewski
    • Adam Lamęcki
    • Michał Mrozowski
    2024 Full text IEEE Access

    This paper introduces a new type of waveguide filter with smooth profile, based on specially designed dual-mode (DM) cavity resonators. The DM cavity design is achieved by applying a shape deformation scheme. The coupling between the two orthogonal cavity modes is implemented by breaking the symmetry of the structure, thus eliminating the need for additional coupling elements. The modes operating in the cavity are carefully analyzed and a scheme for managing the spurious modes is discussed. Two filter prototypes employing the designed DM cavities are developed and described in detail. The first design is a fourth-order bandpass filter (BPF) with a 90◦ rotated output and a transmission zero (TZ), whereas the second design is an eighth-order filter with four TZs. Both designs are developed, taking into account the limitations of 3D printing technology to enable their single-piece fabrication without internal supports. The structures benefit from additive manufacturing (AM) by having a smooth surface profile and reduced weight, which is often highly desirable for high-power and low-loss applications. Filter prototypes were manufactured using selective laser melting (SLM) from aluminum alloy and tested to validate the designs. Measurement results are consistent with the simulation and prove the validity of the proposed solutions. Both measured BPF prototypes demonstrate low insertion loss, i.e., 0.11 dB and 0.25 dB for the fourth-order and eighthorder designs, respectively. The estimated Q-factors reach 3500 and 4500, which is a very good result for 3D-printed parts.

  • LSA Is not Dead: Improving Results of Domain-Specific Information Retrieval System Using Stack Overflow Questions Tags
    • Szymon Olewniczak
    • Julian Szymański
    • Piotr Malak
    • Robert Komar
    • Agnieszka Letowska
    2024 Full text

    The paper presents the approach to using tags from Stack Overflow questions as a data source in the process of building domain-specific unsupervised term embeddings. Using a huge dataset of Stack Overflow posts, our solution employs the LSA algorithm to learn latent representations of information technology terms. The paper also presents the Teamy.ai system, currently developed by Scalac company, which serves as a platform that helps match IT project inquiries with potential candidates. The heart of the system is the information retrieval module that searches for the best-matching candidates according to the project requirements. In the paper, we used our pre-trained embeddings to enhance the search queries using the query expansion algorithm from the neural information retrieval domain. The proposed solution improves the precision of the retrieval compared to the basic variant without query expansion.

  • Łukowy wiadukt Pomorskiej Kolei Metropolitalnej w Gdańsku. Założenia projektowe i stan techniczny po 10 latach eksploatacji
    • Krzysztof Żółtowski
    • Przemysław Kalitowski
    • Mikołaj Binczyk
    • Tomasz Romaszkiewicz
    2024 Mosty

    Artykuł przedstawia historię, projektowanie i ocenę techniczną po 10 latach eksploatacji wiaduktu WK11 w Gdańsku, będącego częścią Pomorskiej Kolei Metropolitalnej. Opisuje proces budowy i wyzwania związane z rekonstrukcją historycznego mostu z 1914 roku, który został zniszczony podczas II wojny światowej. Autorzy szczegółowo analizują koncepcje projektowe, w tym rozważania nad schematem statycznym i zastosowanym materiałem. W artykule zawarto wyniki zaawansowanych analiz numerycznych, które doprowadziły do ostatecznego kształtu wiaduktu. Po 10 latach użytkowania wiadukt WK11 nadal jest w dobrym stanie technicznym, a jedyne zauważalne ślady zużycia to drobne rysy skurczowe. Konstrukcja działa zgodnie z założeniami projektowymi.

  • Machine learning approach to packaging compatibility testing in the new product development process
    • Norbert Piotrowski
    2024 Full text JOURNAL OF INTELLIGENT MANUFACTURING

    The paper compares the effectiveness of selected machine learning methods as modelling tools supporting the selection of a packaging type in new product development process. The main goal of the developed model is to reduce the risk of failure in compatibility tests which are preformed to ensure safety, durability, and efficacy of the finished product for the entire period of its shelf life and consumer use. This kind of testing is mandatory inter alia for all aerosol packaging as any mechanical alterations of the packaging can cause the pressurized product to unseal and stop working properly. Moreover, aerosol products are classified as dangerous goods and any leaking of the product or propellent can be a serious hazard to the storage place, environment, and final consumer. Thus, basic compatibility observations of metal aerosol packaging (i.e. general corrosion, pitting corrosion, coating blistering or detinning) and different compatibility factors (e.g. formula ingredients, water contamination, pH, package material and coatings) were discussed. Artificial intelligence methods applied in the design process can reduce the lengthy testing time as well as developing costs and help benefit from the knowledge and experience of technologists stored in historical data in databases.

  • Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
    • Giao Van Nguyen
    • Prabhakar Sharma
    • Ümit Ağbulut
    • Huu Son Le
    • Thanh Hai Truong
    • Marek Dzida
    • Minh Ho Tran
    • Huu Cuong Le
    • Viet Dung Tran
    2024 Biofuels Bioproducts & Biorefining-Biofpr

    Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine-learning-based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far-reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data-driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.

  • Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
    • Rana Waqar Aslam
    • Hong Shu
    • Iram Naz
    • Abdul Quddoos
    • Andaleeb Yaseen
    • Khansa Gulshad
    • Saad Saud Alarifi
    2024 Full text Remote Sensing

    Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model’s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems.

  • Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
    • Torkan Shafighfard
    • Farzin Kazemi
    • Neda Asgarkhani
    • Doo-Yeol Yoo
    2024 ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

    High-performance alkali-activated concrete (HP-AAC) is acknowledged as a cementless and environmentally friendly material. It has recently received a substantial amount of interest not only due to the potential it has for being used instead of ordinary concrete but also owing to the concerns associated with climate change, sustainability, reduction of CO2 emissions, and energy consumption. The characteristics and amounts of the ingredients used to produce HP-AAC influence its compressive strength. This study performs a comparative analysis based on machine learning (ML) algorithms to present an ensemble model capable of predicting the compressive strength of HP-AAC. This is in response to the development of sophisticated prediction approaches that seek to lower the cost of experimental tools and labor. An extensive framework including 538 experimental datasets with 18 input parameters are extracted. In addition, stacked ML (SM) models are developed to provide their best base estimator combination with the highest capability. The results show that stacked model (SM-5) with score of 14, and prediction accuracy of 98% following by the largest experiment-to-predicted ratio, provide the best estimations of compressive strength of HP-AAC, which has the lowest error values compare to other 18 ML models. Thereafter, a graphical user interface (GUI) is provided and validated by extra experimental tests for estimating the compressive strength, cost, and carbon emission of HP-AAC. Overall, the significance of the current study highlight the outstanding performance of developed stacked ML and GUI for predicting the compressive strength of HP-ACC, which contribute for the on-going research in this area.

  • Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers
    • Farzin Kazemi
    • Neda Asgarkhani
    • Torkan Shafighfard
    • Robert Jankowski
    • Doo-Yeol Yoo
    2024 ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

    In recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for assessing their suitability incur significant time and cost. The emergence of Industry 4.0 has presented opportunities to address these drawbacks by leveraging machine learning (ML) methods. ML techniques have recently been used to forecast the properties and assess the importance of process parameters for efficient structural design and their broad applications. Given their wide range of applications, this work aims to perform a comprehensive analysis of ML algorithms used for predicting the mechanical properties of FRPs. The performance evaluation of various models was discussed, and a detailed analysis of their pros and cons was provided. Finally, the limitations that currently exist in these techniques were pinpointed, and suggestions were given to improve their prediction precision suitable for evaluating the mechanical properties of FRP components.

  • Machine-Learning-Based Global Optimization of Microwave Passives with Variable-Fidelity EM Models and Response Features
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    2024 Full text Scientific Reports

    Maximizing microwave passive component performance demands precise parameter tuning, particularly as modern circuits grow increasingly intricate. Yet, achieving this often requires a comprehensive approach due to their complex geometries and miniaturized structures. However, the computational burden of optimizing these components via full-wave electromagnetic (EM) simulations is substantial. EM analysis remains crucial for circuit reliability, but the expense of conducting rudimentary EM-driven global optimization by means of popular bio-inspired algorithms is impractical. Similarly, nonlinear system characteristics pose challenges for surrogate-assisted methods. This paper introduces an innovative technique leveraging variable-fidelity EM simulations and response feature technology within a kriging-based machine-learning framework for cost-effective global parameter tuning of microwave passives. The efficiency of this approach stems from performing most operations at the low-fidelity simulation level and regularizing the objective function landscape through the response feature method. The primary prediction tool is a co-kriging surrogate, while a particle swarm optimizer, guided by predicted objective function improvements, handles the search process. Rigorous validation demonstrates the proposed framework's competitive efficacy in design quality and computational cost, typically requiring only sixty high-fidelity EM analyses, juxtaposed with various state-of-the-art benchmark methods. These benchmarks encompass nature-inspired algorithms, gradient search, and machine learning techniques directly interacting with the circuit's frequency characteristics.

  • Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    • Marek Wójcikowski
    • Bogdan Pankiewicz
    2024 Full text Engineering Science and Technology-An International Journal-JESTECH

    Air pollution remains a considerable contemporary challenge affecting life quality, the environment, and economic well-being. It encompasses an array of pollutants—gases, particulate matter, biological molecules—emanating from sources such as vehicle emissions, industrial activities, agriculture, and natural occurrences. Nitrogen dioxide (NO2), a harmful gas, is particularly abundant in densely populated urban areas. Given its detrimental impact on health and the environment, precise monitoring of NO2 levels is crucial for devising effective strategies to mitigate risks. However, precise measurement of NO2 presents challenges as it traditionally relies on expensive and heavy (therefore, stationary) equipment. This has led to the pursuit of more affordable alternatives, though their dependability is frequently questionable. This study introduces an innovative technique for precise calibration of low-cost NO2 sensors. Our methodology involves statistical preprocessing of sensor measurements to align their distributions with reference data. The core of the calibration model is an artificial neural network (ANN), trained to synchronize sensor and reference time series measurements. It incorporates environmental variables such as temperature, humidity, and atmospheric pressure, along with readings from redundant NO2 sensors for cross-referencing, and short time series of primary sensor NO2 measurements. This enables efficient learning of typical sensor changes over time in relation to these factors. Additionally, an interpolative kriging model serves as an auxiliary surrogate to enhance the correction process's reliability. Validation using an autonomous monitoring platform from Gdansk University of Technology, Poland, and public reference station data gathered over five months shows remarkable calibration accuracy, with a correlation coefficient close to 0.95 and RMSE of 2.4 µg/m3. These results position the corrected sensor as an attractive and cost-effective alternative to conventional NO2 measurement methods.

  • Macrocyclic derivatives of imidazole as chromoionophores for bismuth(III)/lead(II) pair
    • Błażej Galiński
    • Ewa Wagner-Wysiecka
    2024 Full text SENSORS AND ACTUATORS B-CHEMICAL

    18-membered diazomacrocycles with imidazole or 4-methylimidazole residue as a part of macrocycle were used as chromoionophores in bismuth(III) and lead(II) dual selective optodes for the first time. Cellulose triacetate membranes doped with macrocyclic chromoionophores are bismuth(III) and lead(II) selective with color change from orange/red to different shades of blue and violet, respectively. Results obtained for model and real samples of bismuth(III) and lead(II) showed that easily accessible and regenerable sensor materials can be used for spectrophotometric and colorimetric detection and determination of bismuth(III) and lead(II). The obtained LOD values for bismuth(III) are 1.63×10-7 M and 3.03×10-7 M with spectrophotometric and colorimetric detection, respectively, when using optode with imidazole residue. For sensing material with 4-methylimidazole in macroring the lowest detection limits were obtained for lead(II): 2.14×10-7 M and 3.99×10-7 M with spectrophotometric and digital color analysis detection mode, respectively.