Radicals from the atmospheric pressure pyrolysis and oxidative pyrolysis of hydroquinone, catechol, and phenol

The burning of tobacco creates various types of free radicals that have been reported to be biologically active. Some radicals are transient but can initiate catalytic cycles that generate other free radicals. Other radicals are environmentally persistent and can exist in total particulate matter (TPM) for extended periods. In spite of their importance, little is known concerning the precursors of these radicals or under what pyrolysis/combustion conditions they are formed. We performed studies of the formation of radicals from the gas-phase pyrolysis and oxidative pyrolysis of hydroquinone (HQ) and catechol (CT) between 750 and 1000 °C and phenol from 500 to 1000 °C. The initial electron paramagnetic resonance (EPR) spectra were complex, indicating the presence of multiple radicals. Using matrix annealing and microwave power saturation techniques, phenoxyl, cyclopentadienyl, and peroxyl radicals were identifiable, but only cyclopentadienyl radicals were stable above 750 °C.

Multivariate analysis of the dielectric response of materials modeled using networks of resistors and capacitors

We discuss the modeling of dielectric responses of electromagnetically excited networks which are composed of a mixture of capacitors and resistors. Such networks can be employed as lumped-parameter circuits to model the response of composite materials containing conductive and insulating grains. The dynamics of the excited network systems are studied using a state space model derived from a randomized incidence matrix. Time and frequency domain responses from synthetic data sets generated from state space models are analyzed for the purpose of estimating the fraction of capacitors in the network. Good results were obtained by using either the time-domain response to a pulse excitation or impedance data at selected frequencies. A chemometric framework based on a Successive Projections Algorithm (SPA) enables the construction of multiple linear regression (MLR) models which can efficiently determine the ratio of conductive to insulating components in composite material samples. The proposed method avoids restrictions commonly associated with Archie’s law, the application of percolation theory or Kohlrausch-Williams-Watts models and is applicable to experimental results generated by either time domain transient spectrometers or continuous-wave instruments. Furthermore, it is quite generic and applicable to tomography, acoustics as well as other spectroscopies such as nuclear magnetic resonance, electron paramagnetic resonance and, therefore, should be of general interest across the dielectrics community.