The Size Ellect on the Infrared Spectra of Condensed Media Under Conditions of ID, 2D and 3D Dielectric Confinement

A general expression for the dielectric loss spectrum of an absorbing composite medium was obtained from a Maxwell-Garnett general equation. This expression was simplified for the cases of one, two and three dimensional dielectric confinement in both ordered and disordered thin layers, rods (wires) and spheres of absorbing medium which are considered as mesoparticles or mesoscopic molecules. This theoretical approach was verified experimentally using high purity organic liquids with strong absorption bands in the infrared range. Three organic liquids, namely benzene, chloroform and carbon disulphide, were measured in various dielectric confinement configurations using Fourier Transform Infrared (FTIR) spectroscopy with a Grazing Angle attachment GATRTM. A significant shift of the resonant absorption band of liquid mesoparticles was observed for various dielectric confinement geometries which is in good agreement with theoretical predictions. Possible applications of this work include investigations of industrial smoke, toxic aerosols and liquid droplets

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants