Publications Repository - Gdańsk University of Technology

Page settings

polski
Publications Repository
Gdańsk University of Technology

Show publications from the year

Publications from the year 2024

Show all
  • Cytocompatibility, antibacterial, and corrosion properties of chitosan/polymethacrylates and chitosan/poly(4‐vinylpyridine) smart coatings, electrophoretically deposited on nanosilver‐decorated titania nanotubes
    • Łukasz Pawłowski
    • Michał Bartmański
    • Anna Ronowska
    • Adrianna Banach-Kopeć
    • Szymon Mania
    • Bartłomiej Cieślik
    • Aleksandra Mielewczyk-Gryń
    • Jakub Karczewski
    • Andrzej Zieliński
    2024 JOURNAL OF BIOMEDICAL MATERIALS RESEARCH PART B-APPLIED BIOMATERIALS

    The development of novel implants subjected to surface modification to achieve high osteointegration properties at simultaneous antimicrobial activity is a highly current problem. This study involved different surface treatments of titanium surface, mainly by electrochemical oxidation to produce a nanotubular oxide layer (TNTs), a subsequent electrochemical reduction of silver nitrate and decoration of a nanotubular surface with silver nanoparticles (AgNPs), and finally electrophoretic deposition (EPD) of a composite of chitosan (CS) and either polymethacrylate-based copolymer Eudragit E 100 (EE100) or poly(4-vinylpyridine) (P4VP) coating. The effects of each stage of this multi-step modification were examined in terms of morphology, roughness, wettability, corrosion resistance, coating-substrate adhesion, antibacterial properties, and osteoblast cell adhesion and proliferation. The results showed that the titanium surface formed nanotubes (inner diameter of 97 ± 12 nm, length of 342 ± 36 nm) subsequently covered with silver nanoparticles (with a diameter of 88 ± 8 nm). Further, the silver-decorated nanotubes were tightly coated with biopolymer films. Most of the applied modifications increased both the roughness and the surface contact angle of the samples. The deposition of biopolymer coatings resulted in reduced burst release of silver. The coated samples revealed potent antimicrobial activity against both Gram-positive and Gram-negative bacteria. Total elimination (99.9%) of E. coli was recorded for a sample with CS/P4VP coating. Cytotoxicity results using hFOB 1.19, a human osteoblast cell line, showed that after 3 days the tested modifications did not affect the cellular growth according to the titanium control. The proposed innovative multilayer antibacterial coatings can be successful for titanium implants as effective postoperative anti-inflammation protection.

  • Damage detection in 3D printed plates using ultrasonic wave propagation supported with weighted root mean square calculation and wavefield curvature imaging
    • Erwin Wojtczak
    • Magdalena Rucka
    • Angela Andrzejewska
    2024 Full text

    3D printing (additive manufacturing, AM) is a promising approach to producing light and strong structures with many successful applications, e.g., in dentistry and orthopaedics. Many types of filaments differing in mechanical properties can be used to produce 3D printed structures, including polymers, metals or ceramics. Due to the simplicity of the manufacturing process, biodegradable polymers are widely used, e.g., polylactide (polylactide – PLA) with a practical application for manufacturing complex-shaped elements. The current work dealt with the application of ultrasonic guided waves for non-destructive damage detection and imaging in AM plates. Two specimens with defects were manufactured from PLA filament. Different sizes of damage areas were considered. The specimens were tested using the guided wave propagation technique. The waves were excited using a PZT actuator and recorded contactless with the scanning laser Doppler vibrometry (SLDV) in a set of points located at one surface of the sample. The collected signals were processed with two methods. The first was the weighted root mean square (WRMS) algorithm. Different values of the calculation parameters, namely, averaging time and weighting factor were considered. The WRMS damage maps for both samples were prepared to differentiate between intact and damaged areas. The second approach was wavefield curvature imaging (WCI) which allowed the determination of damage maps based on the curvature of the wavefront. The compensation of wave signals was performed to enhance the quality of results. It was observed that the size of the defect strongly influenced the efficiency of imaging with both methods. The limitations of the proposed approaches were characterized. The presented results confirmed that guided waves are promising for non-destructive damage imaging in AM elements.

  • Damage of a post-tensioned concrete bridge – Unwanted cracks of the girders
    • Bartosz Sobczyk
    • Łukasz Pyrzowski
    • Mikołaj Miśkiewicz
    2024 ENGINEERING FAILURE ANALYSIS

    The cracking of a post-tensioned T-beam superstructure, which was built using the incremental launching method, is analyzed in the paper. The problem is studied in detail, as specific damage was observed in the form of longitudinal cracks, especially in the mid-height zone of the girder at the interface of two assembly sections. The paper is a case study. A detailed inspection is done and non-destructive testing results of the girders are briefly discussed. The attention is especially focused on advanced and comprehensive numerical simulations of the bridge mechanical behavior. Linear and nonlinear static calculations are performed employing the Finite Element Method at global and local levels of precision, enabling deep insight into the bridge response during all the stages of bridge construction and after it is opened to traffic. The crack propagation process in local analyses is described by the application of the Concrete Damage Plasticity law, the parameters of which were carefully chosen. The predicted damage patterns closely resemble those observed at the site. The results reveal, that the girder damage process was initiated when centric prestressing was applied, because vertical reinforcement of the assembly section front-end surface was not designed. However, the damage did not compromise the safety of the bridge. Finally, the repair methods employed are described and also a discussion is presented on how to prevent the occurrence of such cracking.

  • Data Domain Adaptation in Federated Learning in the Breast Mammography Image Classification Problem
    • Łukasz Erimus
    • Aleksandra Borowska
    • Adrian Jaromin
    • Agnieszka Lewko
    • Jacek Rumiński
    2024

    We are increasingly striving to introduce modern artificial intelligence techniques in medicine and elevate medical care, catering to both patients and specialists. An essential aspect that warrants concurrent development is the protection of personal data, especially with technology's advancement, along with addressing data disparities to ensure model efficacy. This study assesses various domain adaptation techniques and federated learning to determine optimal integration strategies for enhanced security and the challenges posed by diverse datasets. Experiments utilized deep learning models, three domain adaptation methods, and a federated learning framework, focusing on mammography imaging for breast cancer detection. Results indicate a notable improvement of up to 20% with domain adaptation and an additional 10% with federated learning integration.

  • Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention
    • Jean-Marie Lepioufle
    • Philipp Schneider
    • Paul David Hamer
    • Rune Odegard
    • Islen Vallejo
    • Tuan-Vu Cao
    • Amir Taherkordi
    • Marek Wójcikowski
    2024 Environmental Data Science

    In environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori quality information about the sensor device without using complex and resource-demanding data assimilation techniques. Both ordinary kriging and the general regression neural network (GRNN) are integrated into this attention with their learnable parameters based on deep learning architectures. We evaluate this method using three static phenomena with different complexities: a case related to a simplistic phenomenon, topography over an area of 196 km2 and to the annual hourly NO2 concentration in 2019 over the Oslo metropolitan region (1026 km2 ). We simulate networks of 100 synthetic sensor devices with six characteristics related to measurement quality and measurement spatial resolution. Generally, outcomes are promising: we significantly improve the metrics from baseline geostatistical models. Besides, distance attention using the Nadaraya–Watson kernel provides as good metrics as the attention based on the kriging system enabling the possibility to alleviate the processing cost for fusion of sparse data. The encouraging results motivate us in keeping adapting distance attention to space-time phenomena evolving in complex and isolated areas.

  • Data on LEGO sets release dates and worldwide retail prices combined with aftermarket transaction prices in Poland between June 2018 and June 2023
    • Wiktor Oczkoś
    • Bartosz Podgórski
    • Wiktoria Szczepańska
    • Tomasz Maria Boiński
    2024 Full text Data in Brief

    The dataset contains LEGO bricks sets item count and pricing history for AI-based set pricing prediction. The data spans the timeframe from June 2018 to June 2023. The data was obtained from three sources: Brickset.com (LEGO sets retail prices, release dates, and IDs), Lego.com official web page (ID number of each set that was released by Lego, its retail prices, the current status of the set) and promoklocki.pl web page (the retail prices for Poland, prices from aftermarket transactions). The data was merged based on the official LEGO set ID. With high granularity of the data (averaged monthly prices per LEGO set) the dataset permits the computation of variables at the set level and could support both aggregate and time-series analyses whereas the sparseness of the data permits the analysis of collector behavior allowing pinpointing of expected qualities from the purchased products and their resale potential. This may be useful to a broad range of researchers and data scientists using statistical methods and machine-learning techniques for price prediction.

  • Data science: Not one size fits all. When building models, you need to get your claim categories right from the beginning
    • Piotr Lebiedź
    2024 19th Century music

    When it comes to insurance modelling, there is plenty of material and training on how to build statistical models. We can use these resources to learn about generalised linear models and gradient boosting machines (see feature, overleaf), understanding their advantages and weak points. The same applies to different transformations and techniques, such as splines, variables mapping, geographical classification, finding significant interactions and mitigating adverse selection. The statistics background and modelling best practices are similar across various industries, so a general data science approach is usually good enough for entry-level actuaries – especially given that, in insurance pricing, we usually use commonly known distributions such as Tweedie, Poisson or gamma. But there is one insurance-specific area in predictive modelling: how to structure our actuarial analyses in the first place. Pricing actuaries tend to put a lot of effort into building the most accurate statistical models and optimising their Gini scores, root mean squared error and/or Akaike information criterion, but it’s equally (if not more) important to understand the product and structure risk modelling in the first place. So, how should we split our risk models?

  • Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review
    • Farzin Kazemi
    • Torkan Shafighfard
    • Doo-Yeol Yoo
    2024 ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

    Fiber-reinforced concrete (FRC) is extensively used in diverse structural engineering applications, and its mechanical properties are crucial for designing and evaluating its performance. The compressive, flexural, splitting tensile, and shear strengths of FRCs are among the most important attributes, which have been discussed more extensively than other properties. The accurate prediction of these properties, which are required for design criteria, has been a challenge because of their high uncertainties. Statistical and empirical models have been extensively utilized. However, such models require extensive experimental work and can produce incorrect outcomes when there are complicated interactions among the qualities of concrete, the makeup of the blend, and the curing environment. To address this issue, machine learning (ML) methods have been increasingly applied in recent years to solve complex structural engineering problems. Predictive models can provide a strong solution for time-consuming numerical simulations and expensive experiments. This study explores the ML techniques applied in this context and provides a comprehensive analysis of artificial intelligence methods used for predicting the mechanical properties of FRCs. It also highlights the key observations, challenges, and future trends in this field. This study serves as a valuable resource for researchers in selecting accurate models that match their applications. It also encourages material engineers to become familiar with and employ ML methods to design FRC mixtures appropriately.

  • Data-driven Models for Predicting Compressive Strength of 3D-printed Fiber-Reinforced Concrete using Interpretable Machine Learning Algorithms
    • Muhammad Arif
    • Faizullah Jan
    • Aïssa Rezzoug
    • Muhammad Ali Afridi
    • Muhammad Luqman
    • Waseem Akhtar Khan
    • Marcin Kujawa
    • Hisham Alabduljabbar
    • Majid Khan
    2024 Case Studies in Construction Materials

    3D printing technology is growing swiftly in the construction sector due to its numerous benefits, such as intricate designs, quicker construction, waste reduction, environmental friendliness, cost savings, and enhanced safety. Nevertheless, optimizing the concrete mix for 3D printing is a challenging task due to the numerous factors involved, requiring extensive experimentation. Therefore, this study used three machine learning techniques, including Gene Expression Programming (GEP), Multi-Expression Programming (MEP), and Decision Tree (DT), to forecast the compressive strength of 3D printed fiber-reinforced concrete (3DP-FRC). The dataset comprises 299 data points with sixteen variables gathered from experimental research studies. For training the model, 70% of the dataset was used, while the remaining 30% was reserved for model testing. Several statistical metrics were utilized to evaluate the accuracy and applicability of the models. In addition, SHapley Additive exPlanations (SHAP), partial dependence plots, and individual conditional expectations approach were employed for the interpretability of the models. The proposed GEP, MEP, and DT models indicated enhanced efficacy, exhibiting correlation coefficient (R) scores of 0.996, 0.987, and 0.990, with mean absolute errors (MAE) of 1.029, 4.832, and 2.513, respectively. Overall, the established GEP model demonstrated exceptional performance compared to MEP and DT, showcasing high prediction precision in assessing the strength of 3DP-FRC. Moreover, a simple empirical formulation has been devised using GEP to predict the compressive strength, offering a simplified and efficient approach for predicting 3DP-FRC strength. The SHAP approach identified water, silica fume, fiber diameter, curing age, and loading directions as leading controlling parameters in predicting strength of 3DP-FRC. In summary, the proposed models can potentially minimize both the computational workload and the need for experimental trials in formulating the mixed design of 3D-printed concrete.

  • Dataset Characteristics and Their Impact on Offline Policy Learning of Contextual Multi-Armed Bandits
    • Piotr Januszewski
    • Dominik Grzegorzek
    • Paweł Czarnul
    2024 Full text

    The Contextual Multi-Armed Bandits (CMAB) framework is pivotal for learning to make decisions. However, due to challenges in deploying online algorithms, there is a shift towards offline policy learning, which relies on pre-existing datasets. This study examines the relationship between the quality of these datasets and the performance of offline policy learning algorithms, specifically, Neural Greedy and NeuraLCB. Our results demonstrate that NeuraLCB can learn from various datasets, while Neural Greedy necessitates extensive coverage of the action-space for effective learning. Moreover, the way data is collected significantly affects offline methods’ efficiency. This underscores the critical role of dataset quality in offline policy learning.

  • Daylight metrics and requirements: A review of reference documents for architectural practice
    • Amanda Pinheiro, Moura
    • Claudia David Naves
    • Natalia Sokół
    • Justyna Martyniuk-Pęczek
    2024 Full text

    Daylight has always been part of architectural practice since architects have used it to define spaces and create complex structures. Daylighting is, nowadays, seen as key strategy for sustainability, energy efficiency and resilience in buildings. This article aims to investigate daylight requirements in reference documents for architectural practice through the collection and qualitative analysis of documents. 117 reference documents were analysed and divided into standards, rating systems, building and urban codes, regulations and guidelines. Results show that static and dynamic metrics are common within standards and rating systems while building and urban codes and regulations often use metrics based on building and urban geometry. Among standards and rating systems, Daylight Factor (DF) is still one of the most used metrics, even if dynamic metrics offer advanced analyses; building and urban codes and regulations are very specific for each location, with a predominant use of geometric metrics; and guidelines can use both types of metrics.

  • D-Band High Gain Planer Slot Array Antenna using Gap Waveguide Technology
    • Ali Farahbakhsh
    • Davood Zarifi
    • Ashraf Uz Zaman
    2024 IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION

    A D-band high gain slot array antenna with corporate-fed distribution network based on gap waveguide structures is proposed at 140GHz. To overcome the fabrication challenges at such high frequency, the gap waveguide technology is deployed in which good electrical contact between different parts of the waveguide structure is not required. The proposed sub-array has four radiating slots that are excited by a groove gap cavity and the cavity is coupled to an E-plane groove gap waveguide via a rectangular coupling slot. A wideband and low-loss corporate feeding network based on the combined ridge gap waveguide and E-plane groove gap waveguide is designed for this case and the whole array antenna consists of 16×16 radiating slots. A standard WR6 waveguide flange is embedded at the bottom side of the feeding structure to excite the array antenna. To evaluate the design, a prototype is fabricated in Aluminum using standard CNC milling technique. The measurement results show that an impedance bandwidth of 20% (124.1-151.7 GHz), a peak gain of 31.5 dBi and maximum efficiency of 94% are achieved for the 16×16-element array antenna. The results show that the proposed array antenna has an excellent performance among the previously published D-band planar array antennas and could be a promising candidate to be used in the development of D-band front-end modules.

  • Dc Leakage Current in Isolated Grid-Connected dc Nanogrid - Origins and Elimination Methods
    • Mohammadreza Azizi
    • Oleksandr Husev
    • Oleksandr Veligorskyi
    • Marek Turzyński
    • Ryszard Strzelecki
    2024

    The LV dc system is a relatively new trend in the distribution sector, which seems to grow widely in the near future due to its promising advantages. In this context, LV dc protection and grounding are challenging issues. Although the galvanically isolated connection mode of dc nanogrid to the ac grid has high reliability, the leakage current can still be injected into the ac grid through the interwinding capacitors and the insulation resistance between the primary and secondary windings of the transformer. The way of grounding the dc nanogrid can also be a determining factor in the leakage current and its dc components. This study deals with the leakage current in the galvanically isolated dc nanogrid. Then, it examines the dc leakage current and its relationship with the dc nanogrid grounding and finally provides solutions to remove the dc components in the leakage current.

  • Dead time effects compensation strategy by third harmonic injection for a five-phase inverter
    • Krzysztof Łuksza
    • Dmytro Kondratenko
    • Arkadiusz Lewicki
    2024 Full text Archives of Electrical Engineering

    This paper proposes a method for compensation of dead-time effects for a fivephase inverter. In the proposed method an additional control subsystem was added to the field-oriented control (FOC) scheme in the coordinate system mapped to the third harmonic. The additional control loop operates in the fixed, orthogonal reference frame ( α - β coordinates) without the need for additional Park transformations. The purpose of this method is to minimize the dead-time effects by third harmonic injection in two modes of operation of the FOC control system: with sinusoidal supply and with trapezoidal supply. The effectiveness of the proposed control method was verified experimentally on a laboratory setup with a prototype five-phase interior permanent magnet synchronous machine (IPMSM). All experimental results were presented and discussed in the following paper.

  • Decisional-DNA-Based Digital Twin Implementation Architecture for Virtual Engineering Objects
    • Syed Imran Shafiq
    • Cesar Sanin
    • Edward Szczerbicki
    2024 Full text CYBERNETICS AND SYSTEMS

    Digital twin (DT) is an enabling technology that integrates cyber and physical spaces. It is well-fitted for manufacturing setup since it can support digitalized assets and data analytics for product and process control. Conventional manufacturing setups are still widely used all around the world for the fabrication of large-scale production. This article proposes a general DT implementation architecture for engineering objects/artifacts used in conventional manufacturing. It will empower manufacturers to leverage DT for real-time decision-making, control, and prediction for efficient production. An application scenario of Decisional-DNA based anomaly detection for conventional manufacturing tools is demonstrated as a case study to explain the architecture.

  • Decoding imagined speech for EEG-based BCI
    • Carlos A. Reyes-García
    • Alejandro A. Torres-García
    • Tonatiuh Hernández-del-Toro
    • Jesus Garcia Salinas
    • Luis Villaseñor-Pineda
    2024

    Brain–computer interfaces (BCIs) are systems that transform the brain's electrical activity into commands to control a device. To create a BCI, it is necessary to establish the relationship between a certain stimulus, internal or external, and the brain activity it provokes. A common approach in BCIs is motor imagery, which involves imagining limb movement. Unfortunately, this approach allows few commands. As an alternative, this chapter presents another approach, an internal language-related stimulus known as imagined speech, which is the action of imagining the diction of a word without emitting any sound or articulating any movement. This neuroparadigm is more intuitive, less subjective, and ambiguous, which are very relevant advantages; however, the cost to properly process the brain signal is not trivial. This chapter describes the main components of an EEG-based imagined speech BCI, along with key works, emerging trends, and challenges in this research area. Regarding the challenges, we present four of them in the pursuit of decoding imagined speech. The first challenge involves accurately recognizing isolated words. The second one is the automatic selection of a subset of EEG channels aiming to reduce computational cost and provide evidence of promising locations for studying imagined speech. The third challenge introduces an innovative approach to addressing scenarios where a new word needs to be added to the vocabulary after the computational model has been trained. Lastly, the fourth challenge concerns the online recognition of words from continuous EEG signals. Despite advances in the area, there is still much work to be done. Important initial steps have been taken in terms of the application of novel techniques for preprocessing, artifact removal, feature extraction, and classification which are the stages to be taken to process the collected signal. Additionally, the community has shared datasets and organized evaluation forums to accelerate the search for solutions.

  • Decoding soundscape stimuli and their impact on ASMR studies
    • Tomasz Piernicki
    • Sahar Seifzadeh
    • Bożena Kostek
    2024 International Journal of Electronics and Telecommunications

    This paper focuses on extracting and understanding the acoustical features embedded in the soundscape used in ASMR (Autonomous Sensory Meridian Response) studies. To this aim, a dataset of the most common sound effects employed in ASMR studies is gathered, containing whispering stimuli but also sound effects such as tapping and scratching. Further, a comparative analytical survey is performed based on various acoustical features and two-dimensional representations in the form of mel spectrogram. A special interest is in whispering sounds uttered in different languages. That is why whispering sounds are compared in the language context, and the characteristics of speaking and whispering are investigated within languages. The results of the 2D analyses are shown in the form of similarity measures, such as Normalized Root Mean Squared Error (NRMSE), PSNR (peak signal-to-noise ratio), and SSIM (structural similarity index measure). The summary is produced, showing that the analytical aspect of the inherently experiential nature of ASMR is highly affected by the subjective, personal experience, so the evidence behind triggering certain brain waves cannot be unambiguous.

  • Deep eutectic solvent-based shaking-assisted extraction for determination of bioactive compounds from Norway spruce roots
    • Alina Kalyniukova
    • Alica Varfalvyová
    • Justyna Płotka-Wasylka
    • Tomasz Majchrzak
    • Patrycja Makoś-Chełstowska
    • Ivana Tomášková
    • Vítězslava Pešková
    • Filip Pastierovič
    • Anna Jirošová
    • Vasil Andruch
    2024 Full text Frontiers in Chemistry

    Polyphenolic compounds play an essential role in plant growth, reproduction, and defense mechanisms against pathogens and environmental stresses. Extracting these compounds is the initial step in assessing phytochemical changes, where the choice of extraction method significantly influences the extracted analytes. However, due to environmental factors, analyzing numerous samples is necessary for statistically significant results, often leading to the use of harmful organic solvents for extraction. Therefore, in this study, a novel DESbased shaking-assisted extraction procedure for the separation of polyphenolic compounds from plant samples followed by LC-ESI-QTOF-MS analysis was developed. The DES was prepared from choline chloride (ChCl) as the hydrogen bond acceptor (HBA) and fructose (Fru) as the hydrogen bond donor (HBD) at various molar ratios with the addition of 30% water to reduce viscosity. Several experimental variables affecting extraction efficiency were studied and optimized using one-variable-at-a-time (OVAT) and confirmed by response surface design (RS). Nearly the same experimental conditions were obtained using both optimization methods and were set as follows: 30 mg of sample, 300 mg of ChCl:Fru 1:2 DES containing 30% w/w of water, 500 rpm shaking speed, 30 min extraction time, 10°C extraction temperature. The results were compared with those obtained using conventional solvents, such as ethanol, methanol and water, whereby the DES-based shaking-assisted extraction method showed a higher efficiency than the classical procedures. The greenness of the developed method was compared with the greenness of existing procedures for the extraction of polyphenolic substances from solid plant samples using the complementary green analytical procedure index (ComplexGAPI) approach, while the results for the developed method were better or comparable to the existing ones. In addition, the practicability of the developed procedure was evaluated by application of the blue applicability grade index (BAGI) metric. The developed procedure was applied to the determination of spruce root samples with satisfactory results and has the potential for use in the analysis of similar plant samples.

  • Deep eutectic solvents for the food industry: extraction, processing, analysis, and packaging applications – a review
    • Roberto Castro Munoz
    • Aslı Can Karaça
    • Mohammad Saeed Kharazmi
    • Grzegorz Boczkaj
    • Fernanda Jimena Hernández-Pinto
    • Shahida Anusha Siddiqui
    • Seid Mahdi Jafari
    2024 CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION

    Food factories seek the application of natural products, green feedstock and eco-friendly processes, which minimally affect the properties of the food item and products. Today, water and conventional polar solvents are used in many areas of food science and technology. As modern chemistry evolves, new green items for building eco-friendly processes are being developed. This is the case of deep eutectic solvents (DESs), named the next generation of green solvents, which can be involved in many food industries. In this review, we timely analyzed the progress on applying DES toward the development of formulations, extraction of target biomolecules, food processing, extraction of undesired molecules, analysis and determination of specific analytes in food samples (heavy metals, pesticides), food microbiology, and synthesis of new packaging materials, among many other applications. For this, the latest developments (over the last 2-3 years) have been discussed emphasizing innovative ideas and outcomes. Relevantly, we discuss the hypothesis and the key features of using DES in the mentioned applications. To some extent, the advantages and limitations of implementing DES in the food industry are also elucidated. Finally, based on the findings of this review, the perspectives, research gaps and potentialities of DESs are stated.

  • Deep eutectic solvents with solid supports used in microextraction processes applied for endocrine-disrupting chemicals
    • Jose Grau
    • Aneta Chabowska
    • Justyna Werner
    • Agnieszka Zgoła-Grześkowiak
    • Magdalena Fabjanowicz
    • Natalia Jatkowska
    • Alberto Chisvert
    • Justyna Płotka-Wasylka
    2024 TALANTA.The International Journal of Pure and Applied Analytical Chemistry

    The determination of endocrine-disrupting chemicals (EDCs) has become one of the biggest challenges in Analytical Chemistry. Due to the low concentration of these compounds in different kinds of samples, it becomes necessary to employ efficient sample preparation methods and sensitive measurement techniques to achieve low limits of detection. This issue becomes even more struggling when the principles of the Green Analytical Chemistry are added to the equation, since finding an efficient sample preparation method with low damaging properties for health and environment may become laborious. Recently, deep eutectic solvents (DESs) have been proposed as the most promising green kind of solvents, but also with excellent analytical properties due to the possibility of custom preparation with different components to modify their polarity, viscosity or aromaticity among others. However, conventional extraction techniques using DESs as extraction solvents may not be enough to overcome challenges in analysing trace levels of EDCs. In this sense, combination of DESs with solid supports could be seen as a potential solution to this issue allowing, in different ways, to determine lower concentrations of EDCs. In that aim, the main purpose of this review is the study of the different strategies with solid supports used along with DESs to perform the determination of EDCs, comparing their advantages and drawbacks against conventional DES-based extraction methods.