Therefore, there is a strong interest in rapid and cost-effective methods for the analysis of hard tissue remains that can be applied in large numbers of skeletal findings and ideally on-site [1,2]. In the current work, several bone fragments and teeth were studied using a Laser Induced Breakdown Spectroscopy (LIBS) setup with nearly microscopic resolution [2]. The main objective of the investigation was to test various Machine Learning algorithms such as K-Nearest Neighbors, Random Forest, and Support Vector Machine, as well as a simple Artificial Neural Network algorithm concerning their ability to facilitate discrimination and classification of hard tissue remains. Despite strong similarities among spectra, the selection of specific spectral intervals with spectral lines corresponding to biogenic elements (such as Ca, Mg, Sr, Na, and P) was found to be a determining factor that increased the resulting classification accuracy. The use of the Artificial Neural Network algorithm produced significantly better results concerning classification compared to the Machine Learning models. The degree of classification achieved varied from decent to excellent, providing an overall good sorting accuracy regardless of the type of tissue analyzed. ## References  
* Moncayo et al. (2014) Moncayo, S.; Manzoor, S.; Ugidos, T.; Navarro-Villoslada, F.; Caceres, J. Discrimination of human bodies from bones and teeth remains by Laser Induced Breakdown Spectroscopy and Neural Networks. _Spectrochim. Acta Part B At. Spectrosc._**2014**, _101_, 21-25. * Siozos et al. (2021) Siozos, P.; Hausmann, N.; Holst, M.; Anglos, D. Application of laser-induced breakdown spectroscopy and neural networks on archaeological human bones for the discrimination of distinct individuals. _J. Archaeol. Sci. Rep._**2021**, _35_, 102769. * _18.19. Physicochemical and Adsorption Characteristics of Thiomethacrylate-Co-Divinylbenzene microspheres_
* _Bosacka_ *, M. Wasilewska, A. Chrzanowska, D. Sternik, A. Derylo-Marczewska and B. Podskoscielna Institute of Chemical Sciences, Maria Curie-Skodowska University, Maria Curie-Skoldowska Square 3, 20031 Lublin, Poland
* Correspondence: alicja.bosacka@poczta.umcs.lublin.pl  
Water pollution is a growing problem over the world due to the development of industry and globalization. Organic compounds are particularly difficult to remove from water due to the difficulty of their decomposition [1,2]. The adsorption efficiency is influenced by many factors: adsorbent textural properties, adsorbent surface chemistry (surface functional groups), and also adsorbate properties (molecular weight, ionization strength (pKa), functional groups or water solubility (hydrophobicity)) [3,4]. New or improved adsorbents are widely sought, therefore, novel two-component materials consisting of thiometacrylate and divinylbenzene phases are proposed. In this work, thiometacrylate-co-divinylbenzene microspheres were synthesized in a polymerization reaction at three different molar ratio phase compositions: 3.1, 2:1, and 1:1. The obtained materials were analyzed using several analytical techniques. The textural properties of the samples were investigated using scanning electron microscopy (SEM) and low-temperature adsorption-desorption analysis. Fourier transform infrared-attenuated total reflection (FT-IR-ATR) spectroscopy was used to assess the binding efficiency of both phases. Thermal stability of thiometacrylate-divinylbenzene materials was examined by thermal analysis (TG/DTG/DSC). Moreover, studies of adsorption kinetics were carried out in order to evaluate the adsorption efficiency of the obtained adsorbents towards selected aromatic adsorbates. ## References  
* Younas et al. (2021) Younas, F.; Mustafa, A.; Farooqi, Z.U.R. ; Wang, X.; Younas, S.; Mohy-Ud-Din, W.; Ashir Hameed, M.; Mohsin Abrar, M.; Maitlo, A.A.; Noreen, S.; et al. Current and Emerging Adsorbent Technologies for Wastewater Treatment: Trends, Limitations, and Environmental Implications. _Water_**2021**, _13_, 215.