The Turkevich synthesis method of Au nanoparticles (AuNPs) was adopted for direct fabrication of SiO2@Au and TiO2@Au core-shell nanostructures. In this method, chloroauric acid was reduced with trisodium citrate in the presence of amine-functionalized silica or titania submicroparticles. Core-shells obtained in this way were compared to structures fabricated by mixing of Turkevich AuNPs with amine-functionalized silica or titania submicroparticles. It was found that by modification of reaction conditions of the first method, such as temperature and concentration of reagents, control over gold coverage on silicon dioxide particles has been achieved. Described method under certain conditions allows fabrication of semicontinuous gold films on the surface of silicon dioxide particles. To the best of our knowledge, this is the first report describing use of Turkevich method to direct fabrication of TiO2@Au core-shell nanostructures.

The paper presents the results of studies on bryophyte flora of the Molenda forest reserve located in the Central Poland. Fifteen species of liverworts and 73 species of mosses were found. Twenty one of these species are considered to be partially protected by law, three endangered and ten rare in the region. Epigean bryophyte constitute the largest ecological group in the bryophyte flora, consisting of 55 species (62% of total flora), followed by epixylic species which are represented by 48 species (54%). The most interesting were taxa threatened and regionally rare, such as Nowellia curvifolia (Dicks.) Mitt., Callicla­dium haldanianum (Grev.) H.A. Crum, Dicranoweisia cirrata (Hedw.) Lindb., Orthodicranum tauricum (Sapjegin) Smirnova, Orthodontium lineare Schwägr., Orthotrichum pulchellum Brunt., O. stramineum Hornsch. ex Brid., Sciu­ro-hypnum refle­xum (Starke) Ignatov & Huttunen, Ulota bruchii Hornsch. ex Brid. and U. crispa (Hedw.) Brid.

<p>Within the EU funded Horizon 2020 project ROBOMINERS (www.robominers.eu) we were challenged to consider how autonomous robotic mining could be integrated with geological modelling. How would an autonomous robotic miner know where and whether an orebody is worth extracting? The orebody and all related geological aspects would need to be modelled in a comprehensible, self-containing format the robot can use directly.  To this end we envisioned a robot that would know where the orebody is, its important characteristics, and would have the ability to interpret the orebody in real-time and update its geological knowledge of the orebody as it excavates.</p><p>The modelled orebody could only be approximate at the robot scale (estimated at 1m maximum diameter!) as detailed information would be lacking. This led to re-evaluating existing geological modelling practices to see how they would fit within the robotic mining concept.</p><p>In our work we developed a novel approach to traditional geological modelling by combining three essential elements:</p><ul><li>Replacement of blocks in block modelling with tetrahedra</li> <li>Functional modelling framework to create model descriptors</li> <li>Machine Learning</li> </ul><p>A tetrahedron is the most basic 3D element, similar to a triangle being the most basic 2D element to represent objects. Tetrahedra can be made to accurately reflect a boundary and are therefore always either inside or outside of that boundary. They are commonly used in Finite Element Methods (FEM) and have found their way into geophysics, geomechanics and flow modelling, but until now, not into geological modelling. Another major advantage is that a tetrahedral grid can be constructed at multi-resolution scales. However, it also means geological features need to be described in a way that allows them to be represented at those scales (e.g. mine scale versus robot scale).</p><p>One method to deal with these scale issues is to use a functional representation: representing geological features with (mathematical) functions. With functions, a value from that function at ANY point in 3D space can be retrieved to see if  that point is either inside or outside of a unit. Functions have been used under the Implicit Modelling (IM) banner. However, the functions can be also seen as classifiers between regions. Machine Learning’s (ML) core functionality is to provide is a mechanism for classifying data and estimate values or labels to unknown points. In our work, were therefore integrated high performance ML algorithms into IM.</p><p>With these three key elements we developed a system that can represent a complete 3D geological model in a consistent and ordered way by describing it rather than actually creating it. The model description can then create an optimized FEM model at any resolution when needed, even though the descriptor does not change. The ultimate aim is that the descriptors and functions will be used directly by the robot to optimize its path planning, without needing large data transfers.</p>