Detection of particles with a cloud chamber

Cloud chambers were essential devices in early nuclear and particle physics research. Superseded by more modern detectors in actual research, they still remain very interesting pedagogical apparatus. This thesis attempts to give a global view on this topic. To do so, a review of the physical foundations of the diffusion cloud chamber, in which an alcohol is supersaturated by cooling it with a thermal reservoir, is carried out. Its main results are then applied to analyse the working conditions inside the chamber. The analysis remarks the importance of using an appropriate alcohol, such as isopropanol, as well as a strong cooling system, which for isopropanol needs to reach −40ºC. That theoretical study is complemented with experimental tests that were performed with what is the usual design of a home-made cloud chamber. An effective setup is established, which highlights details such as a grazing illumination, a direct contact with the cooling reservoir through a wide metal plate, or the importance of avoiding vapour removal. Apart from that, video results of different phenomena that cloud chamber allow to observe are also presented. Overall, it is aimed to present a physical insight that pedagogical papers usually lack.

Analysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)

In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed