A novel, sensitive potentiometric hydrocarbon sensor for high-vacuum applications.

A potentiometric device based on interfacing a solid electrolyte oxygen ion conductor with a thin platinum film acts as a robust, reproducible sensor for the detection of hydrocarbons in high- or ultrahigh-vacuum environments. Sensitivities in the order of approximately 5 x 10(-10) mbar are achievable under open circuit conditions, with good selectivity for discrimination between n-butane on one hand and toluene, n-octane, n-hexane, and 1-butene on the other hand. The sensor's sensitivity may be tuned by operating under constant current (closed circuit) conditions; injection of anodic current is also a very effective means of restoring a clean sensing surface at any desired point. XPS data and potentiometric measurements confirm the proposed mode of sensing action: the steady-state coverage of Oa, which sets the potential of the Pt sensing electrode, is determined by the partial pressure and dissociative sticking probability of the impinging hydrocarbon. The principles established here provide the basis for a viable, inherently flexible, and promising means for the sensitive and selective detection of hydrocarbons under demanding conditions.

On the relationship between hidden Markov Models and convex functional transforms for simulating scanning probe Microscopy

Models for simulating Scanning Probe Microscopy (SPM) may serve as a reference point for validating experimental data and practice. Generally, simulations use a microscopic model of the sample-probe interaction based on a first-principles approach, or a geometric model of macroscopic distortions due to the probe geometry. Examples of the latter include use of neural networks, the Legendre Transform, and dilation/erosion transforms from mathematical morphology. Dilation and the Legendre Transform fall within a general family of functional transforms, which distort a function by imposing a convex solution.In earlier work, the authors proposed a generalized approach to modeling SPM using a hidden Markov model, wherein both the sample-probe interaction and probe geometry may be taken into account. We present a discussion of the hidden Markov model and its relationship to these convex functional transforms for simulating and restoring SPM images.©2009 SPIE.