Effect of surface roughness on optical heating of metals

Abstract

Heating by absorption of light is a commonly used technique to ensure a fast temperature increase of metallic samples. The rate of heating when using optical heating depends critically on the absorption of light by a sample. Here, the reflection and scattering of light from UV to IR by surfaces with different roughness of iron-based alloy samples (Fe, 1 wt-% Cr) is investigated. A combination of ellipsometric and optical scattering measurements is used to derive a simplified parametrisation which can be used to obtain the absorption of light from random rough metal surfaces, as prepared through conventional grinding and polishing techniques. By modelling the ellipsometric data of the flattest sample, the pseudodielectric function of the base material is derived. Describing an increased roughness by a Maxwell-Garnett model does not yield a reflectivity which follows the experimentally observed sum of scattered and reflected intensities. Therefore, a simple approach is introduced, based on multiple reflections, where the number of reflections depends on the surface roughness. This approach describes the data well, and is subsequently used to estimate the fraction of absorbed energy. Using numerical modelling, the effect on the heating rate is investigated. A numerical example is analysed, which shows that slight changes in roughness may result in big differences of the energy input into a metallic sample, with consequences on the achieved temperatures. Though the model oversimplifies reality, it provides a physically intuitive approach to estimate trends.

Modelling and experiment of selective oxidation and nitridation of binary model alloys at 700 °C – The systems Fe, 1 wt.%{Al, Cr, Mn, Si}

“…A small hole with a diameter of 1.2 mm was drilled on one front face, where the sample was mounted on the thermocouple. The sample surfaces were mechanically ground by using grinding papers from 400 grit down to 2500 grit size, which ensures an optimal compromise between sample roughness and the efficiency of sample heating in an infra-red furnace (socalled ''optical heating'') [34]. All samples were ultrasonically cleaned in ethanol and dried in a cold stream of dry air.…”

Quantifying the Pathway and Predicting Spontaneous Emulsification during Material Exchange in a Two Phase Liquid System

“…1 ) are normalized through determination of the molten oxide entering a fully liquid state, as viewed through the HT-CSLM video output. This time varied from between 5- and 25 seconds after reaching the programmed temperature of 1873 K. Reasons for this are expectedly due to variation in HT-CSLM performance, chamber maintenance, as well as effective interaction with the IR radiation due to differences in the sample surface topology (surface roughness for example 22 ) and powder packing density. An example video of the FeAl-SiO 2 system is supplied to view with article.…”

A comprehensive study of infrared reflectivity of poly(3,4-ethylenedioxythiophene) model layers with different morphologies and conductivities

“…Such a behavior of conducting PEDOT layers can be compared to that of metallic surfaces. Indeed, their surface roughness significantly influences their optical properties due to scattering phenomena [57][58][59][60][61]. Both experimental and theoretical studies have been conducted to assess the absorption coefficient of rough metal surfaces.…”