Microwave-assisted selective synthesis of mono- and bistriazines with π-conjugated spacers and study of the optoelectronic properties.

A series of mono- and bistriazine derivatives were selectively prepared in high yields using microwave irradiation. Donor substituents were attached on the triazine ring, including pyrazolyl-substituted anilines and o-, m-, and p-phenylenediamine as π-conjugated spacers. This method was used to build σ-π-σ-A-σ-D systems for monotriazines and D-σ-A-σ-π-σ-A-σ-D systems for bistriazines. A study of the optoelectronic properties was performed by UV-vis and fluorescence spectroscopy and cyclic voltammetry. The monotriazines do not show any emission, but the bistriazines are blue emitters and show an interesting solvatochromic effect with large Stokes shifts of more than 10,000 cm(-1) in some cases and quantum yields up to 23%. The optoelectronic properties depend on the conjugation and the position and donor character of the substituents and spacers. Cyclic voltammetry was used to determine the energy levels (HOMO and LUMO) in the bistriazines. An increase in the energy of the HOMO and a decrease in the energy of the LUMO were observed upon extending the conjugation. The title compounds showed interesting properties for use in optoelectronic devices, especially as blue emitters.

Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data.

Designing public health responses to outbreaks requires close monitoring of population-level health indicators in real-time. Thus, an accurate estimation of the epidemic curve is critical. We propose an approach to reconstruct epidemic curves in near real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between March and April 2020. We address two data collection problems that affected the reliability of the available real-time epidemiological data, namely, the frequent missing information documenting when a patient first experienced symptoms, and the frequent retrospective revision of historical information (including right censoring). This is done by using a novel back-calculating procedure based on imputing patients' dates of symptom onset from reported cases, according to a dynamically-estimated "backward" reporting delay conditional distribution, and adjusting for right censoring using an existing package, NobBS , to estimate in real time (nowcast) cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number ( R t ) in real-time. At each step, we evaluate how different assumptions affect the recovered epidemiological events and compare the proposed approach to the alternative procedure of merely using curves of case counts, by report day, to characterize the time-evolution of the outbreak. Finally, we assess how these real-time estimates compare with subsequently documented epidemiological information that is considered more reliable and complete that became available later in time. Our approach may help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health surveillance systems in other locations.