Index of content:
Volume 77, Issue 1, January 2006
77(2006); http://dx.doi.org/10.1063/1.2162432View Description Hide Description
A remote sensor for measuring on-road vehicles passing the sensor in real time is described. This sensor expands upon previous technology that measured carbon monoxide, carbon dioxide, and exhaust hydrocarbons in the IR and nitric oxide in the UV. The design adds the capability to measure nitrogen dioxide in the UV with one spectrometer and to measure and along with NO in a second UV spectrometer. With these units operating side by side, the major mobile source precursors to secondary aerosol production can be measured simultaneously and in real time. Detection limits for , , and are 1.2, 0.72, and 0.78 g pollutant per kilogram of fuel, respectively.
static magnetic field and temperature-controlled specimen environment for use with general-purpose optical microscopes77(2006); http://dx.doi.org/10.1063/1.2162433View Description Hide Description
We describe the addition of a simple, low-cost fixed magnetic field to a commercially available, variable-temperature sample environment suitable for optical microscopy. The magnetic field is achieved with the use of rare-earth permanent magnets and steel yoke assembly, packaged into a Linkam Scientific Instruments model THMS600 heating and cooling stage. We demonstrate its effectiveness with examples of magnetic ordering of a lipid/water system doped with paramagnetic ions in the presence and absence of the applied magnetic field and at different temperatures.
77(2006); http://dx.doi.org/10.1063/1.2165570View Description Hide Description
This article presents a low-cost portable electrochemical instrument capable of on-site identification of heavy metals. The instrument acquires metal-specific voltage and current signals by the application of differential pulse anodic stripping voltammetry. This technique enhances the analytical current and rejects the background current, resulting in a higher signal-to-noise ratio for a better detection limit. The identification of heavy metals is based on an intelligent machine-based method using a multilayer perceptron neural network consisting of three layers of neurons. The neural network is implemented using a 16 bit microcontroller. The system is developed for use in the field in order to avoid expensive and time-consuming procedures and can be used in a variety of situations to help environmental assessment and control.