Agnieszka Jastrzebska

Recent advances in graphene family materials toxicity investigations

Recently, graphene family materials (GFMs) have been introduced among all fields of science and still get numerous attention. Also, the applicability of these materials in many areas makes them very attractive. GFMs have attracted both academic and industrial interest as they can produce a dramatic improvement in materials properties at very low filler content. This article presents recent findings on GFMs toxicity properties based on the most current literature. This article studies the effects of GFMs on bacteria, mammalian cells, animals, and plants. This article also reviews in vitro and in vivo test results as well as potential anticancer activity and toxicity mechanisms of GFMs. The effect of functionalization of graphene on pacifying its strong interactions with cells and associated toxic effects was also analyzed. The authors of the article believe that further work should focus on in vitro and in vivo studies on possible interactions between GFMs and different living systems. Further research should also focus on decreasing GFMs toxicity, which still poses a great challenge for in vivo biomedical applications. Consequently, the potential impact of graphene and its derivatives on humans and environmental health is a matter of academic interest. However, potential hazards sufficient for risk assessment first need to be investigated.

The ecotoxicity of graphene family materials: current status, knowledge gaps and future needs

Recently, graphene family materials (GFMs) have been introduced among all fields of science and still get numerous attention. Also, the applicability of these materials in many areas makes them very attractive. GFMs have attracted both academic and industrial interest as they can produce a dramatic improvement in materials properties at very low filler content. The aim of this review is to identify, summarize, and present the first available information on the influence of GFMs on soil and water environment as well as identify the knowledge gaps and indicate the directions for the next generation of the original scientific investigations. The paper also presents our first preliminary impact assessment and potential pathways of GFMs distribution in the environment. We used as an example the reduced graphene oxide/Al2O3 nanocomposite (RGO/Al2O3) that has been previously designed and synthesized by us. Authors believe that further work should focus on improvement of characterization methodology applicable for ecotoxicity analyses and possible interactions between GFMs and different living ecosystems. Consequently, the potential impact of graphene and its derivatives on environmental health is a matter of academic interest. However, potential hazards sufficient for risk assessment and concerned with GFMs usage in consumer products first need to be investigated and identified. Further research should focus on gathering knowledge on GFMs properties for life cycle analyses, which still poses a great challenge for scientists.

Modeling time series with fuzzy cognitive maps

Fuzzy Cognitive Maps are recognized knowledge modeling tool. FCMs are visualized with directed graphs. Nodes represent information, edges represent relations within information. The core element of each Fuzzy Cognitive Map is weights matrix, which contains evaluations of connections between maps nodes. Typically, weights matrix is constructed by experts. Fuzzy Cognitive Map can be also reconstructed in an unmanned mode. In this article authors present their own, new approach to time series modeling with Fuzzy Cognitive Maps. Developed methodology joins Fuzzy Cognitive Map reconstruction procedure with moving window approach to time series prediction. Authors train Fuzzy Cognitive Maps to model and forecast time series. The size of the map corresponds to the moving window size and it informs about the length of historical data, which produces time series model. Developed procedure is illustrated with a series of experiments on three real-life time series. Obtained results are compared with other approaches to time series modeling. The most important contribution of this paper is description of the methodology for time series modeling with Fuzzy Cognitive Maps and moving windows.

Design of Fuzzy Cognitive Maps for Modeling Time Series

This study elaborates on a comprehensive design methodology of fuzzy cognitive maps (FCMs). Here, the maps are regarded as a modeling vehicle of time series. It is apparent that whereas time series are predominantly numeric, FCMs are abstract constructs operating at the level of abstract entities referred to as concepts and represented by the individual nodes of the map. We introduce a mechanism to represent a numeric time series in terms of information granules constructed in the space of amplitude and change of amplitude of the time series, which, in turn, gives rise to a collection of concepts forming the corresponding nodes of the FCMs. Each information granule is mapped onto a node (concept) of the map. We identify two fundamental design phases of FCMs, namely 1) formation of information granules mapping numeric data (time series) into activation levels of information granules (viz., the nodes of the map), and 2) optimization of information granules at the parametric level, viz., learning (estimating) the weights between the nodes of the map. The learning is typically realized in a supervised mode on a basis of some experimental data. A construction of information granules is realized with the aid of fuzzy clustering, namely fuzzy C-means. The optimization is realized with the use of particle swarm optimization. The proposed approach is illustrated in detail by a series of experiments using a collection of publicly available data.

Morphology, structure, and photoactivity of two types of graphene oxide-TiO2 composites

Two types of graphene oxide-TiO2 composites were prepared: one by including graphene oxide flakes in the TiO2 sol, followed by thermal treatment (GI composite) at 300°C, and the second by including graphene oxide flakes in the calcined (at 500°C) TiO2 xerogel (GII composite). The composites were characterized by SEM, TEM-EDS, TEM-SADP, STEM-HAADF, HRTEM coupled with FT, XRD, and XPS. Photocatalysis results were fitted to different kinetic models (pseudo-first and pseudo-second kinetics, intraparticle Weber-Morris diffusion, film diffusion, and external mass transfer). The results showed that by introducing graphene oxide flakes in the TiO2 sol, followed by thermal treatment at 300°C (GI composite), an efficient graphene oxide-TiO2 catalyst with high specific surface area, heterogeneity, and many graphitized areas can be obtained. Complete crystallization of the composite is not the key issue for the best photoactivity achievement. The rate limiting step in the photocatalytic process is the photooxidation of SA molecules on the TiO2 surface.

Nodes Selection Criteria for Fuzzy Cognitive Maps Designed to Model Time Series

The article introduces three concepts’ rejection/selection criteria for Fuzzy Cognitive Map-based method of time series modeling and prediction. Proposed criteria are named entropy index, membership index and global distance index. Concepts’ selection strategies facilitate Fuzzy Cognitive Map design procedure. Proposed criteria allow to simplify, otherwise very complex models, and achieve a reasonable balance between complexity and accuracy.

Time Series Modeling with Fuzzy Cognitive Maps: Simplification Strategies

The article is focused on the issue of complexity of Fuzzy Cognitive Maps designed to model time series. Large Fuzzy Cognitive Maps are impractical to use. Since Fuzzy Cognitive Maps are graph-based models, when we increase the number of nodes, the number of connections grows quadratically. Therefore, we posed a question how to simplify trained FCM without substantial loss in map’s quality. We proposed evaluation of nodes’ and weights’ relevance based on their influence in the map. The article presents the method first on synthetic time series of different complexity, next on several real-world time series. We illustrate how simplification procedure influences MSE. It turned out that with just a small increase of MSE we can remove up to \(\frac{1}{3}\) of nodes and up to \(\frac{1}{6}\) of weights for real-world time series. For regular data sets, like the synthetic time series, FCM-based models can be simplified even more.

Nano-titanium oxide doped with gold, silver, and palladium — synthesis and structural characterization

Nano-titania doped with noble metals (Au/TiO2, Ag/TiO2, Pd/TiO2) has been synthesized by mild hydrolysis of the mixture of metal salts or complexes and titanium isopropoxide ((iPr-O)4Ti). After thermal decomposition of the obtained precursors, nanomaterials were formed. Morphological characterization of the nanomaterials was provided by scanning electron microscopy (SEM) and stereological analysis, determining the BET specific surface area, and BJH nanoporosity (pore volume, pore size). It has been found that the structure of nanomaterials (size of nanoparticles and agglomerates) depended strongly on the method of the (iPr-O)4Ti hydrolysis. A minor dependence on the kind of solvents and precursors of noble metals was observed. The presence of doping metal nanoparticles was confirmed by transmission electron microscopy (TEM) and energy dispersive X-ray spectroscopy (EDX). Nanomaterial phases were identified by X-ray diffraction (XRD). According to the XRD patterns, Ag/TiO2 and Pd/TiO2 products with doping metals in their oxidized form contain Ag-Ti and Pd-Ti phases. Peaks of the metal oxides Ag2O and PdO are absent in the XRD patterns. The average size of TiO2 nanoparticles is situated in the region of 20–60 nm, whereas metals are present as about 10–15 nm sized particles and fine nanoparticles.

UV Light-Assisted Degradation of Methyl Orange, Methylene Blue, Phenol, Salicylic Acid, and Rhodamine B: Photolysis Versus Photocatalyis

Methyl orange (MO), methylene blue (MB), phenol (F), salicylic acid (SA), and rhodamine B (ROD) were used as substrates during the photodegradation experiments in the absence and in the presence of nanostructured Ag/titania-silica. The catalyst was characterized by scanning electron microscopy (SEM), scanning transmission electron microscope high-angle annular dark field (STEM-HAADF), stereological analysis, nitrogen adsorption-desorption, and X-ray photoelectron spectroscopy (XPS) measurements. The results were fitted on pseudo-first and pseudo-second kinetic order models. The film diffusion was also determined. The photolysis degrades MO and F to a greater extent than the photocatalysis. The degradation of SA occurred at the same rate either by photolysis or by photocatalysis. MB was best removed by photocatalysis. With regard to the photocatalysis, the highest rates of film diffusion were obtained for MB, F, and ROD, meaning that these molecules crossed the film to arrive at the catalyst surface more rapidly than the others. For MO and MB, the results followed the pseudo-first-order kinetic model while for SA, F, and ROD, the pseudo-second-order kinetic model was more appropriate.

Rejecting Foreign Elements in Pattern Recognition Problem

Standard assumption of pattern recognition problem is that processed elements belong to recognized classes. However, in practice, we are often faced with elements presented to recognizers, which do not belong to such classes. For instance, paper-to-computer recognition technologies (e.g. character or music recognition technologies, both printed and handwritten) must cope with garbage elements produced at segmentation level. In this paper we distinguish between elements of desired classes and other ones. We call them native and foreign elements, respectively. The assumption that we have only native elements results in incorrect inclusion of foreign ones into desired classes. Since foreign elements are usually not known at the stage of recognizer construction, standard classification methods fail to eliminate them. In this paper we study construction of recognizers based on support vector machines and aimed on coping with foreign elements. Several tests are performed on real-world data.