Effect of microstructural features on the corrosion behavior of severely deformed Al–Mg–Si alloy

The aim of this study was to determine the influence of severe plastic deformation processing and the changes in microstructure resulting therefrom on the corrosion resistance of an Al–Mg–Si alloy. The alloy was processed using incremental equal channel angular pressing, which caused a reduction in grain size from 15 to 0.9 µm. The grain refinement was accompanied by an increase in the number of grain boundaries and dislocations, and by changes in grain orientation. However, there was no change in the size and number of intermetallic particles, which presumably resulted in a constant number of galvanic couplings. Electrochemical experiments revealed only slight differences between the samples before and after processing. Higher potential transients/oscillations upon immersion and increased corrosion currents in the vicinity of corrosion potential point to slightly higher reactivity of the most refined material. This indicates that intermetallic particles are the most crucial microstructural elements in terms of corrosion resistance. Their impact exceeds that of grain boundaries, in particular, at the stage of corrosion initiation. The development of corrosion attack is controlled more by the microstructure of the matrix as the grain refinement resulted in a less pronounced corrosion attack in comparison with the coarse-grained sample.     

Thermal transport and chemical conversion in single reacting sorbent particles

The effects of particle size and carbon dioxide concentration on chemical conversion in engineered spherical particles undergoing calcium oxide looping are investigated. Particles are thermochemically cycled in a furnace under different carbon dioxide concentrations. Changes in composition due to chemical reactions are measured using thermogravimetric analysis. Gas composition at the furnace exit is evaluated with mass spectroscopy. A numerical model of thermal transport phenomena developed previously is adapted to match the physical system investigated in the present study. The model is used to elucidate effects of reacting medium characteristics on particle temperature and reaction extent. Experimental and numerical results show that (1) an increase in particle size results in a decrease in carbonation extent, and (2) the carbonation step consists of fast and slow reaction regimes. The reaction rates in the fast and slow carbonation regimes increase with increasing carbon dioxide concentration. The effect of carbon dioxide concentration and the distinction between the fast and slow regimes become more pronounced with increasing particle size.     

Stem Photosynthesis—A Key Element of Grass Pea (Lathyrus sativus L.) Acclimatisation to Salinity

Grass pea (Lathyrus sativus) is a leguminous plant of outstanding tolerance to abiotic stress. The aim of the presented study was to describe the mechanism of grass pea (Lathyrus sativus L.) photosynthetic apparatus acclimatisation strategies to salinity stress. The seedlings were cultivated in a hydroponic system in media containing various concentrations of NaCl (0, 50, and 100 mM), imitating none, moderate, and severe salinity, respectively, for three weeks. In order to characterise the function and structure of the photosynthetic apparatus, Chl a fluorescence, gas exchange measurements, proteome analysis, and Fourier-transform infrared spectroscopy (FT-IR) analysis were done inter alia. Significant differences in the response of the leaf and stem photosynthetic apparatus to severe salt stress were observed. Leaves became the place of harmful ion (Na⁺) accumulation, and the efficiency of their carboxylation decreased sharply. In turn, in stems, the reconstruction of the photosynthetic apparatus (antenna and photosystem complexes) activated alternative electron transport pathways, leading to effective ATP synthesis, which is required for the efficient translocation of Na⁺ to leaves. These changes enabled efficient stem carboxylation and made them the main source of assimilates. The observed changes indicate the high plasticity of grass pea photosynthetic apparatus, providing an effective mechanism of tolerance to salinity stress.     

Organofluorine Hydrazone Derivatives as Multifunctional Anti-Alzheimer's Agents with CK2 Inhibitory and Antioxidant Features

A set of novel hydrazone derivatives were synthesized and analyzed for their biological activities. The compounds were tested for their inhibitory effect on the phosphorylating activity of the protein kinase CK2, and their antioxidant activity was also determined in three commonly used assays. The hydrazones were evaluated for their radical scavenging against the DPPH, ABTS and peroxyl radicals. Several compounds have been identified as good antioxidants as well as potent protein kinase CK2 inhibitors. Most hydrazones containing a 4-N(CH₃)₂ residue or perfluorinated phenyl rings showed high activity in the radical-scavenging assays and possess nanomolar IC₅₀ values in the kinase assays.     

ZnO Nanoplatelets with Controlled Thickness: Atomic Insight into Facet-Specific Bimodal Ligand Binding Using DNP NMR

Colloidal nanoplatelets (NPLs) and nanosheets with controlled thickness have recently emerged as an exciting new class of quantum-sized nanomaterials with substantially distinct optical properties compared to 0D quantum dots. Zn-based NPLs are an attractive heavy-metal-free alternative to the so far most widespread cadmium chalcogenide colloidal 2D semiconductor nanostructures, but their synthesis remains challenging to achieve. The authors describe herein, to the best of their knowledge, the first synthesis of highly stable ZnO NPLs with the atomically precise thickness, which for the smallest NPLs is 3.2 nm (corresponding to 12 ZnO layers). Furthermore, by means of dynamic nuclear polarization-enhanced solid-state ¹⁵N NMR, the original role of the benzamidine ligands in stabilizing the surface of these nanomaterials is revealed, which can bind to both the polar and non-polar ZnO facets, acting either as X- or L-type ligands, respectively. This bimodal stabilization allows obtaining hexagonal NPLs for which the surface energy of the facets is modulated by the presence of the ligands. Thus, in-depth study of the interactions at the organic–inorganic interfaces provides a deeper understanding of the ligand–surface interface and should facilitate the future chemistry of stable-by-design nano-objects.     

Effectiveness of strategic environmental assessment in Poland

The paper presents the results of an evaluation of the effectiveness of SEA obtained as a result of a survey with the participation of stakeholders. In Poland, several hundred SEAs are conducted annually at the local and regional levels and several at the national level. Although the survey demonstrated that SEA is evaluated as effective or quite effective, the respondents pointed to several irregularities in the non-procedural dimension of effectiveness. The main ones in terms of substantive effectiveness are that SEA is rarely used to help develop plans/programmes and procedures are highly politicised. Moreover, low effectiveness in terms of variant assessment and cumulative impacts and nearly non-existent monitoring of the actual effects of implementing plans/programmes influence substantive effectiveness significantly. The respondents pointed to the problem resulting from the fact that plan-makers are unwilling to take into account the SEA recommended changes. They also emphasised that there are certain attempts to put pressure on SEA consultants to make the conclusions less stringent. Respondents indicate that in Poland the costs associated with conducting SEA often outweigh the profits. The research shows that the society is properly informed about SEA, but public involvement is still low.     

Machine Learning-Based Predictions of Customers’ Decisions in Car Insurance

Predicting customer decisions allows companies to obtain higher profits due to better resource management. The accuracy of those predictions can be currently boosted by the application of machine learning algorithms.

We propose a new method to predict a car driver’s decision about taking a replacement car after a vehicle accident happens. We use feature engineering to create attributes of high significance. The generated attributes are related to time (e.g., school holidays), place of collision (e.g., distance from home), time and conditions (e.g., weather), vehicles (e.g., vehicle value), addresses of both the victim and the perpetrator. Feature engineering involves external sources of data.

Five machine learning methods of classification are considered: decision trees, multi-layer perceptrons, AdaBoost, logistic regression and gradient boosting. Algorithms are tested on real data from a Polish insurance company. Over 80% accuracy of prediction is achieved. Significance of the attributes is calculated using the linear vector quantization method.

Presented work shows the applicability of machine learning in the car insurance market.     

Biological Water Treatment by a Hybrid Fluidized-Bed Bioreactor: Theoretical Study

A hybrid fluidized-bed bioreactor for water purification was proposed and analyzed. It is a novel type of bioreactor characterized by hitherto unknown stationary and dynamic features. Steady-state characteristics of this hybrid bioreactor with external liquid circulation are presented. A quantitative analysis of steady-state properties of the bioreactor was performed with the aid of an original mathematical model developed for a double-substrate aerobic microbiological process. A steady-state analysis of aerobic processes characterized by different oxygen demand was performed. The effect of essential parameters was evaluated, including carbonaceous substrate concentration in the feed stream to the apparatus, aeration intensity, total residence time of a liquid in the bioreactor, and height of the bed of fine carrier particles.