For a series of copper(II) porphyrins, we report EPR data from solid solutions as well as E 0 values for the first ring oxidation, emission spectra, and luminescence lifetimes in methylene chloride. Although the EPR parameters are fairly constant, the potentials vary by almost 700 mV, and the room-temperature lifetimes range from 300 ns for Cu(TCl2PP) to 15 ns for Cu(TMeOPP), where TCl2PP denotes 5,10,15,20-tetra(2‘,6‘-dichlorophenyl)porphyrin and TMeOPP denotes 5,10,15,20−tetra(4‘-methoxyphenyl)porphyrin. The data show that the variation in the lifetime of the emitting π−π* state is not due to the thermal population of another excited state of either d−d or charge-transfer parentage. However, the results are consistent with a model originally introduced by Asano et al. who proposed that an important vibronic distortion occurs in the emitting trip-doublet and trip-quartet states when the excitation involves the a2u orbital of the porphyrin (Asano, M.; Kaizu, Y.; Kobayashi, H. J. Chem. Phys. 1988, 89, 6567−6576). In view of the fact that the distortion is unique to the copper systems, we suggest that it involves movement toward a sitting-atop structure, consistent with the role the d10 configuration is likely to have in the excited-state wave function.

Feed-forward neural networks have been trained to identify and quantify heavy metals in mixtures under conditions where there were significant complications due to intermetallic compound formation. The networks were shown to be capable of (i) correlating voltammetric responses with individual heavy metals in complex mixtures, (ii) determining the relationship between responses and concentrations (including nonlinear relationships due to overlapping peaks and intermetallic compound formation), and (iii) rapidly determining concentrations of individual components from mixtures once trained. Using simulated data, modeled after complex interactions experimentally observed in samples containing Cu and Zn, it has been demonstrated that networks containing two layers of neurons (a nonlinear hidden layer and a linear output layer) can be trained to calculate concentrations under a variety of complicated situations. These include, but are not limited to, cases where the response of the intermetallic compound formed is observed as a shoulder of one of the pure metals and cases where the response of the intermetallic compound formed is not observed in the potential window. In addition, the network described above was trained to simultaneously determine concentrations of four metals (Cu, Pb, Cd, and Zn) in a concentration range where all responses were complicated by intermetallic compound formation (1-500 ppb).