The aim of the performed experiments was to analyse relationships occurring between endophytic

bacteria from the Herbaspirillum genus and Sinorhizobium meliloti Bp nodule bacteria and to examine the

condition of plants subjected to coinoculation with the above-mentioned strains in in vitro conditions. In experiments examining the impact of Herbaspirillum frisingense on Sinorhizobium meliloti BP, the stimulation of

growth of inoculated bacteria from the Sinorhizobium genus was recorded in all three combinations (48-hour

culturing, sediment and supernatant). On the other hand, the examination of interactions between the Sinorhizobium meliloti strain and Herbaspirillum frisingense strain revealed that in the case of culture and supernatant, an

antagonistic action was recorded. Besides, it was found that such coinoculation exerted a beneficial influence on

the process of seed lucerne symbiosis and yielding as confirmed by increased numbers of root nodules, higher

nitrogenase activity and greater plant mass.

The main source of information on the abundance of polymetallic nodules (APN) is the results of direct seafloor sampling, mainly using box corers. Due to the vast spread of nodule occurrence in the Pacific, the distances between successive sampling sites are significant. This makes it difficult to reliably estimate the nodule resources, especially in parts of the deposit with small areas corresponding to the areas scheduled for extraction in the short term (e.g. within one year). It seems justified to try to increase the accuracy of nodule resource estimates through the use of information provided by numerous photos of the ocean floor taken between sampling stations. In particular, the percentage of nodule coverage of the ocean floor (NC), the data on fraction distribution of nodules (FD) and the coverage of nodules with sediments (SC) are important here. In the presented study, three regression models were used to predict the nodule abundance from images: simple linear regression (SLR), multiple regression (MR), and general linear model (GLM). The GLM provides the most accurate prediction of nodule abundance (APN) due to the ability of this model to simultaneously take into account both quantitative variable (NC) and qualitative variables (FD, SC). The mean absolute errors of APN prediction are in the range of 1.0–1.7 kg/m2, which is 7–13% of the average nodule abundance determined for training or testing data sets. This result can be considered satisfactory for predicting the abundance in ocean floor areas covered only by photographic survey.

Arsenic is the only beneficial impurity for copper electrorefining through inhibiting anode passivation and the formation of floating slimes. The behaviour of copper anodes with different content of arsenic were studied at high current density (>280 A/m ²). It showed that low arsenic anodes (As < 300 ppm) easily generated anode passivation, floating slimes and cathode nodules during the electrorefining proccess. The floating slimes, electrolyte, cathode and anode were observed and analyzed. As result, low arsenic anodes were more likely to be passivated due to their microstructure defects and irregular microstructure. Increasing electrolyte temperature and addition of glycerol were propitious to reduce low arsenic anodes’ passivation. The floating slimes occured when the concentration of As(III) in electrolyte decreased to 1 g/L, and they would be precipitated by polyacrylamide. All measures greatly improved the cathode quality at current density of 300 A/m ².