This work presents the synthesis, photophysical characterization, and application of ATRP polymerization initiators to produce photoactive solid-state fluorescent polystyrene-based polymers. These compounds present absorption in the UV region (∼340 nm) and emission in the blue-green regions with large Stokes shift (∼10 000 cm–1) due to a proton transfer process in the excited state (ESIPT). Theoretical calculations at TD-DFT level of theory with the CAM-B3LYP functional were also performed in order to study the geometry and charge distribution of these compounds in their ground and excited electronic states. In the calculations, the cc-pVDZ basis set was used to geometry optimization in S0 and S1 states and jun-cc-pVTZ basis set was used to describe the excited states. The results show an absorption maxima and emission in good agreement with the experimental values. As a proof-of-concept, ultraviolet absorbing and photoactive polymers based on polystyrene were successfully produced by ATRP polymerization applying the synthesized dye initiators. Moreover, these initiators allowed an intense fluorescence emission in the solid state with large Stokes shift; improved parameters for the synthesis, polymerization, and polydispersity if compared to already reported ESIPT initiator, as well as higher polymer photostability if compared to the pristine polystyrene.

A series of nickel(ii) complexes [NiBr2(N^Se)2] (Ni1–Ni5) based on bidentate N^Se ligands were prepared by reacting arylselenyl–pyrazolyl ligands (L1–L5) with NiBr2(DME) (DME = 1,2-dimethoxyethane).

Designing reliable computer-aided diagnosis (CADx) systems based on data extracted from breast images and patient data to provide a second opinion to radiologists is still a challenging and yet unsolved problem. This paper proposes two benchmarking datasets (one of them representative of low resolution digitized Film Mammography images and the other one representative of high resolution Full Field Digital Mammography images) aimed to (1) modeling and exploring machine learning classifiers (MLC); (2) evaluating the impact of mammography image resolution on MLC; and (3) comparing the performance of breast cancer CADx methods. Also, we include a comparative study of four groups of image-based descriptors (intensity, texture, multi-scale texture and spatial distribution of the gradient), and combine them with patient's clinical data to classify masses. Finally, we demonstrate that this combination of clinical data and image descriptors is advantageous in most CADx scenarios.

The photolysis mechanisms of 1H-1,2,3-triazole and 1H-1,2,3-benzotriazole were elucidated by employing multiconfigurational methods (CASSCF and CASPT2) and non-adiabatic molecular dynamics.