Quality analysis of selective microparticle deposition on electrically programmable surfaces.

Image processing and pattern analysis can evaluate the deposition quality of triboelectrically charged microparticles on charged surfaces. The image processing method presented in this paper aims at controlling the quality of peptide arrays generated by particle based solid phase Merrifield combinatorial peptide synthesis. Incorrectly deposited particles are detected before the amino acids therein are coupled to the growing peptide. The calibration of the image acquisition is performed in a supervised training step in which all parameters of the quality analyzing algorithm are learnt given one representative image. Then, the correct deposition pattern is determined by a linear support vector machine. Knowing the pattern, contaminated areas can be detected by comparing the pattern with the actual deposition. Taking into account the resolution of the image acquisition system and its magnification factor, the number and size of contaminating particles can be calculated out of the number of connected foreground pixels.

Nuclear multifragmentation, percolation, and the fisher droplet model: common features of reducibility and thermal scaling

It is shown that the Fisher droplet model, percolation, and nuclear multifragmentation share the common features of reducibility (stochasticity in multiplicity distributions) and thermal scaling (one-fragment production probabilities are Boltzmann factors). Barriers obtained, for cluster production on percolation lattices, from the Boltzmann factors show a power-law dependence on cluster size with an exponent of 0.42+/-0.02. The EOS Collaboration Au multifragmentation data yield barriers with a power-law exponent of 0.68+/-0.03. Values of the surface energy coefficient of a low density nuclear system are also extracted.