An SPR-Based In Situ Methane Sensor for the Aqueous and Gas Phase.

This paper presents a fast, low-cost, precise methane sensor based on refractive index changes in a cryptophane A (CryptA)-doped polystyrene membrane. For the realization of this sensor, we built a surface plasmon resonance sensor with a refractive index resolution of 4.31 × 10-6 and investigated the optimal membrane thickness, i.e., a polymer layer of sufficient sensitivity with the lowest response time. For a membrane thickness of 760 nm, a limit of detection of 135 ppm and a response time constant of 45 s were found. Despite a comparable refractive index resolution and a higher CryptA content, the limit of detection is 3 orders of magnitude larger than that of a reported prototype. The sensor is capable of quantifying methane in the gas or aqueous phase. However, temperature, humidity, and ethanol vapor highly influence the signal. Although the principle of CryptA-based methane measurement is a promising, fast, and low-cost method, it will not be able to compete with state-of-the-art sensing in its current state.

Sinking velocity of sub-millimeter microplastic.

Sinking experiments were conducted using irregularly shaped polyamide (PA), polymethyl methacrylate (PMMA), and polyethylene terephthalate (PET) particles sized 6 to 251 µm. Certified PS spheres were used to validate experiments and showed that the effect of particle size on terminal sinking velocity is well reproduced by the method. As expected sinking velocities of irregularly shaped particles were considerably lower than theoretical values for spheres of the same size range calculated via several approximations available in the literature. Despite the influence of particle shape, the dependence of terminal sinking velocity on particle size can reasonably well be described by a quadratic linear regression, with an average determination of 63%. To generalize results we present a model that predicts terminal sinking velocity as a function of particle size and particle excess density over the fluid. Improving the predictive power of this model requires further experiments with a range of particle characteristics.