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Hydroxylamine may decompose explosively if processed and stored in certain conditions, posing critical safety issues that need to be carefully addressed. A key aspect is related to the characterization of chemical aspects involved in the explosive decomposition of hydroxylamine (HA), requiring accurate and detailed kinetic mechanisms. This work was devoted to the experimental and numerical characterization of the thermal decomposition of aqueous solutions of HA included in the range of 10% w to 50% w . The onset temperatures of thermal decomposition were determined in the range of 143-198 °C under heating rates of 2 and 5 °C min -1 , respectively. A reduced mechanism listing 13 species and 11 reactions involving nitrogen-containing species was produced and validated against experimental measurements. Reaction pathways ruling the decomposition of HA were identified. The hydrogen abstraction toward HNOH and H 2 NO dominates the primary steps of NH 2 OH decomposition. The generated mechanism was adopted for the definition of a dimensionless stability diagram for the safe use of HA. Finally, results show a self-accelerating behaviour for any temperature larger than 186 °C, defining a monitoring criterion for safe storage of hydroxylamine-solutions., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The ongoing COVID-19 epidemic highlights the need for effective tools capable of predicting the onset of infection outbreaks at their early stages. The tracing of confirmed cases and the prediction of the local dynamics of contagion through early indicators are crucial measures to a successful fight against emerging infectious diseases (EID). The proposed framework is model-free and applies Early Warning Detection Systems (EWDS) techniques to detect changes in the territorial spread of infections in the very early stages of onset. This study uses publicly available raw data on the spread of SARS-CoV-2 mainly sourced from the database of the Italian Civil Protection Department. Two distinct EWDS approaches, the Hub-Jones (H&J) and Strozzi-Zaldivar (S&Z), are adapted and applied to the current SARS-CoV-2 outbreak. They promptly generate warning signals and detect the onset of an epidemic at early surveillance stages even if working on the limited daily available, open-source data. Additionally, EWDS S&Z criterion is theoretically validated on the basis of the epidemiological SIR. Discussed EWDS successfully analyze self-accelerating systems, like the SARS-CoV-2 scenario, to precociously identify an epidemic spread through the calculation of onset parameters. This approach can also facilitate early clustering detection, further supporting common fight strategies against the spread of EIDs. Overall, we are presenting an effective tool based on solid scientific and methodological foundations to be used to complement medical actions to contrast the spread of infections such as COVID-19., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)