Showing papers by "Wrocław University of Technology published in 2022"

Routes for Metallization of Perovskite Solar Cells

Abstract: The application of metallic nanoparticles leads to an increase in the efficiency of solar cells due to the plasmonic effect. We explore various scenarios of the related mechanism in the case of metallized perovskite solar cells, which operate as hybrid chemical cells without p-n junctions, in contrast to conventional cells such as Si, CIGS or thin-layer semiconductor cells. The role of metallic nano-components in perovskite cells is different than in the case of p-n junction solar cells and, in addition, the large forbidden gap and a large effective masses of carriers in the perovskite require different parameters for the metallic nanoparticles than those used in p-n junction cells in order to obtain the increase in efficiency. We discuss the possibility of activating the very poor optical plasmonic photovoltaic effect in perovskite cells via a change in the chemical composition of the perovskite and through special tailoring of metallic admixtures. Here we show that it is possible to increase the absorption of photons (optical plasmonic effect) and simultaneously to decrease the binding energy of excitons (related to the inner electrical plasmonic effect, which is dominant in perovskite cells) in appropriately designed perovskite structures with multishell elongated metallic nanoparticles to achieve an increase in efficiency by means of metallization, which is not accessible in conventional p-n junction cells. We discuss different methods for the metallization of perovskite cells against the background of a review of various attempts to surpass the Shockley-Queisser limit for solar cell efficiency, especially in the case of the perovskite cell family.

A Mobile Robot-Based System for Automatic Inspection of Belt Conveyors in Mining Industry

Abstract: Mechanical systems (as belt conveyors) used in the mining industry, especially in deep underground mines, must be supervised on a regular basis. Unfortunately, they require high power and are spatially distributed over a large area. Till now, some elements of the conveyor (drive units) have been monitored 24 h/day using SCADA systems. The rest of the conveyor is inspected by maintenance staff. To minimize the presence of humans in harsh environments, we propose a mobile inspection platform based on autonomous UGV. It is equipped with various sensors, and in practice it is capable of collecting almost the same information as maintenance inspectors (RGB image, sound, gas sensor, etc.). Till now such experiments have been performed in the lab or in the mine, but the robot was controlled by the operator. In such a scenario the robot is able to record data, process them and detect, for example, an overheated idler. In this paper we will introduce the general concept of an automatic robot-based inspection for underground mining applications. A framework of how to deploy the inspection robot for automatic inspection (3D model of the tunnel, path planing, etc.) are defined and some first results from automatic inspection tested in lab conditions are presented. Differences between the planned and actual path are evaluated. We also point out some challenges for further research.

Design and Construction of Foundations for Industrial Facilities in the Areas of Former Post-Mining Waste Dumps

Abstract: This article presents the characteristics of the heaps resulting from coal exploitation in terms of the possibility of their development for industrial facilities. The chances of soil improvement and the existing threats were indicated, emphasising the risk of self-ignition. The most effective technologies are dynamic or impulse compaction, which allows deep soil improvement and the obtaining of an appropriately rigid and load-bearing subsoil. The homogeneity of the soil’s mechanical properties that form the subsoil is also essential, which guarantees compliance with the serviceability limit state. A very important aspect of the investment process in the post-mining waste dumps is the risk of auto-ignition of the accumulated material. Considerations and analyses are presented on the example of the implementation of Panatonni service, warehouse, and production halls in Ruda Śląska. The performance of impulse compaction allowed for the safe construction of industrial halls. In particular, the tests carried out on the thermal state of the dumps confirmed the lack of an unacceptable risk of endogenous fire in the dump mass.

Graph Barlow Twins: A self-supervised representation learning framework for graphs

Abstract: The self-supervised learning (SSL) paradigm is an essential exploration area, which tries to eliminate the need for expensive data labeling. Despite the great success of SSL methods in computer vision and natural language processing, most of them employ contrastive learning objectives that require negative samples, which are hard to define. This becomes even more challenging in the case of graphs and is a bottleneck for achieving robust representations. To overcome such limitations, we propose a framework for self-supervised graph representation learning — Graph Barlow Twins, which utilizes a cross-correlation-based loss function instead of negative samples. Moreover, it does not rely on non-symmetric neural network architectures — in contrast to state-of-the-art self-supervised graph representation learning method BGRL. We show that our method achieves as competitive results as the best self-supervised methods and fully supervised ones while requiring fewer hyperparameters and substantially shorter computation time (ca. 30 times faster than BGRL).

Spectroscopy and Structural Investigation of Iron Phosphorus Trisulfide—FePS ₃

Abstract: Lamellar structures of transition metal phosphorus trisulfides possess strong intralayer bonding, albeit adjacent layers are held by weak van der Waals interactions. Those compounds received enormous interest due to their unique combination of optical and long-range magnetic properties. Among them, iron phosphorus trisulfide (FePS3) gathered special attention for being a semiconductor with an absorption edge in the near-infrared, as well as showing an Ising-like anti-ferromagnetism. A successful growth of centimeter size bulk FePS3 crystals with a chemical yield above 70% is reported, whose crystallographic structure and composition are carefully identified by advanced electron microscopy methodologies, including atomic resolution elemental mapping, along with photoelectron spectroscopy. The knowledge on the optical activity of FePS3 is extended utilizing temperature-dependent absorption and photoacoustic spectroscopies, while measurements are corroborated with density-functional theory calculations. Temperature-dependent experiments show a small and monotonic band-edge energy shift down to 115 K and expose the interconnected importance of electron-phonon coupling. Most of all, the correlation between the optical behavior and the magnetic phase transition is revealed, which shows the practical utilization of temperature-dependent optical absorption to investigate magnetic interactions.

Boosting the performance of anomalous diffusion classifiers with the proper choice of features

Abstract: Understanding and identifying different types of single molecules' diffusion that occur in a broad range of systems (including living matter) is extremely important, as it can provide information on the physical and chemical characteristics of particles' surroundings. In recent years, an ever-growing number of methods have been proposed to overcome some of the limitations of the mean-squared displacements approach to tracer diffusion. In March 2020, the Anomalous Diffusion (AnDi) Challenge was launched by a community of international scientists to provide a framework for an objective comparison of the available methods for anomalous diffusion. In this paper, we introduce a feature-based machine learning method developed in response to Task 2 of the challenge, i.e. the classification of different types of diffusion. We discuss two sets of attributes that may be used for the classification of single-particle tracking data. The first one was proposed as our contribution to the AnDi Challenge. The latter is the result of our attempt to improve the performance of the classifier after the deadline of the competition. Extreme gradient boosting was used as the classification model. Although the deep-learning approach constitutes the state-of-the-art technology for data classification in many domains, we deliberately decided to pick this traditional machine learning algorithm due to its superior interpretability. After the extension of the feature set our classifier achieved the accuracy of 0.83, which is comparable with the top methods based on neural networks.

Sub parts-per-billion detection of ethane in a 30-meters long mid-IR Antiresonant Hollow-Core Fiber

Abstract: In this work, we present the first demonstration of a gas sensor utilizing the Wavelength Modulation Spectroscopy technique and a mid-IR guiding Antiresonant Hollow-Core Fiber with a record length of 30 m. The self-fabricated ARHCF was used as an air-tight, low-volume absorption cell delivering an extended laser-gas molecules interaction path within the sensor’s setup, which enabled it to efficiently detect ethane at 2996.88 cm−1. Benefiting from the unique guidance properties of the ARHCF, the sensor reached a minimum detection limit of 670 parts-per-trillion by volume for 14 s integration time, which is at a level comparable to the bulk optics-based systems utilizing more complex detection techniques or multipass cells with tens of meters long optical path lengths. The obtained results confirm that ARHCF-based gas sensors can successfully compete with or even outperform bulk optics-based mid-IR gas sensor configurations. The obtained results show the potential for the development of novel, low-volume, compact, and reliable types of gas detectors allowing precise analysis of various gaseous substances at their trace concentration level.

FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings

Abstract: Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances in representation on graphs have centered on both homogeneous and heterogeneous graphs, where the latter presenting the challenges associated with multi-typed nodes and/or edges. In this paper, we consider the additional challenge of evolving graphs. We ask the question of whether the advances in representation learning for static graphs can be leveraged for dynamic graphs and how? It is important to be able to incorporate those advances to maximize the utility and generalization of methods. To that end, we propose the Framework for Incremental Learning of Dynamic Networks Embedding (FILDNE), which can utilize any existing static representation learning method for learning node embeddings while keeping the computational costs low. FILDNE integrates the feature vectors computed using the standard methods over different timesteps into a single representation by developing a convex combination function and alignment mechanism. Experimental results on several downstream tasks, over seven real-world datasets, show that FILDNE is able to reduce memory (up to 6x) and computational time (up to 50x) costs while providing competitive quality measure gains (e.g., improvements up to 19 pp AUC on link prediction and up to 33 pp mAP on graph reconstruction) with respect to the contemporary methods for representation learning on dynamic graphs.

Extraction of lyophilized olive mill wastewater using supercritical CO2 processes

Abstract: The olive growing in Tunisia has an economic dominance and agricultural importance. However, the huge extraction of olive oil generates a large quantity of olive mill wastewater (OMW), which is discharged to the surroundings. The highly polluting potential (organic load) of OMW threatens the environment and requires an urgent solution. Supercritical fluid extraction (SFE) is a green extraction method that can be applied to purify OMW and, at the same time, to isolate a high quality oil from this wastewater. In order to explore and to valorize the compositions of Olive mill wastewater (OMW), extraction in different solvents (supercritical CO2, hexane) was carried out and chemical composition of the extracted oils were established by GC-FID. The Tunisia OMW were collected from two different zones namely Sousse and Sfax. In this work, we have investigated the effects pressure (P) and temperature (T) on the yield and the quality of oil. The suitable conditions for the extraction of oil from lyophilized OMW by Supercritical carbon dioxide (SC-CO2) were found to be the pressure of 30 MPa and the temperature of 60 °C. In order to simulate the process, the model of broken and intact cells (Sovova’s model) was applied. The model well represented the experimental data. Oil yields ranged from 21.3 % to 33.87 % depending on the extraction solvent used. Monounsaturated fatty acids (MUFA) were the major compounds of the oils, based on the fatty acid analysis. Chromatographic analysis revealed that the chemical compositions vary from one region to another, extraction solvent as well as the conditions of pressure and temperature.

Analysing the export potentials of the Portuguese footwear industry by Data Envelopment Analysis

Abstract: Exports are widely believed to play a central role in economic development and firms' profitability, particularly in countries with small domestic markets. With that aim, governments and firms spend considerable resources on international promotion. Identifying the markets with the greatest potential for export growth is therefore crucial for an efficient allocation of public and private resources. In this paper, we propose a Data Envelopment Analysis framework to identify trading potentials with existing trading partner countries within an industry. To illustrate its applicability, we use data from the Portuguese footwear industry. Specifically, among the countries that currently import Portuguese footwear, we aim to identify those that have the greatest potential for increasing their imports from Portuguese footwear in terms of revenue. We further decompose this potential into price and quantity changes to provide strategic directions to the Portuguese footwear industry. For the analysis, we use panel data of 64 countries analyzed over the years 2011-2018. The results reveal that higher potentials lie among the countries of the European Economic Area. Overall, these potentials may be achieved through different price-quantity strategies.

Polish Adaptation and Validation of the Intuitive (IES-2) and Mindful (MES) Eating Scales—The Relationship of the Concepts with Healthy and Unhealthy Food Intake (a Cross-Sectional Study)

Abstract: Intuitive (IE) and mindful (ME) eating share internally focused eating, yet previous studies have shown that these concepts are not strongly correlated, which suggests that they might be differently related to food intake. The study aimed to adapt the original Intuitive (IES-2) and Mindful (MES) Eating Scales to the Polish language, to test their psychometric parameters and, further, to examine associations of IE and ME with an intake of selected food groups, i.e., healthy foods (fresh and processed vegetables, fresh fruit) and unhealthy foods (sweets, salty snacks). A cross-sectional study was conducted in 2020 in a group of 1000 Polish adults (500 women and 500 men) aged 18-65 (mean age = 41.3 ± 13.6 years). The factor structure was assessed with exploratory (EFA) and confirmatory (CFA) factor analysis as well as structural equation modeling (SEM). Measurement invariance across gender was assessed with multiple-group analysis. Internal consistency and discriminant validity of the two scales was tested. Spearman's correlation coefficient was used to examine the correlation between IES-2 and MES subscales with food intake. A 4-factor, 16-item structure was confirmed for IES-2, while EFA and CFA revealed a 3-factor, 17-item structure of MES. Both scales demonstrated adequate internal consistency and discriminant validity. Full metric and partial scalar invariance were found for IES-2, while MES proved partial invariances. "Awareness" (MES) and "Body-Food Choice Congruence" (IES-2) positively correlated with intake of healthy foods and negatively with the intake of unhealthy ones. "Eating For Physical Rather Than Emotional Reasons" (IES-2) and "Act with awareness" (MES) favored lower intake of unhealthy foods, whereas "Unconditional Permission to Eat" and "Reliance on Hunger and Satiety Cues" (IES-2) showed an inverse relationship. A greater score in "Acceptance" (MES) was conducive to lower intake of all foods except sweets. The results confirmed that adapted versions of the IES-2 and MES are valid and reliable measures to assess IE and ME among Polish adults. Different IE and ME domains may similarly explain intake of healthy and unhealthy foods, yet within a single eating style, individual domains might have the opposite effect. Future studies should confirm our findings with the inclusion of mediating factors, such as other eating styles, childhood experiences, dieting, etc.