Suppr超能文献

用于油漆横断面特征化的图谱和线性成像傅里叶变换红外微光谱学的性能评估。

Performance evaluation of mapping and linear imaging FTIR microspectroscopy for the characterisation of paint cross sections.

机构信息

Microchemistry and Microscopy Art Diagnostic Laboratory, University of Bologna, via Guaccimanni 42, 48100 Ravenna, Italy.

出版信息

Anal Bioanal Chem. 2010 Jan;396(2):899-910. doi: 10.1007/s00216-009-3269-8. Epub 2009 Nov 12.

Abstract

Different Fourier transform infrared microspectroscopic techniques, using attenuated total reflection (ATR) mode and single-element mercury-cadmium-telluride (MCT) detector (mapping) or multielement MCT detector (raster scanning), are compared with each other for the characterisation of inorganic compounds and organic substances in paint cross sections. All measurements have been performed on paint cross sections embedded in potassium bromide, a transparent salt in the mid-infrared region, in order to better identify the organic materials without the interference of the usual embedding resin. The limitations and advantages of the different techniques are presented in terms of spatial resolution, data quality and chemical information achieved. For all techniques, the chemical information obtained is found to be nearly identical. However, ATR mapping performed with a recently developed instrumentation shows the best results in terms of spectral quality and spatial resolution. In fact, thin organic layers (approximately 10 microm) have been not only identified but also accurately located. This paper also highlights the recent introduction of multielement detectors, which may represent a good compromise between mapping and imaging systems.

摘要

不同的傅里叶变换红外微光谱技术,采用衰减全反射(ATR)模式和单元素汞镉碲(MCT)探测器(映射)或多元素 MCT 探测器(光栅扫描),用于在油漆横截面上对无机化合物和有机物质进行特征分析。所有测量均在嵌入溴化钾(中红外区透明盐)的油漆横截面上进行,以便在没有通常的嵌入树脂干扰的情况下更好地识别有机材料。根据所获得的空间分辨率、数据质量和化学信息,介绍了不同技术的局限性和优势。对于所有技术,获得的化学信息几乎相同。然而,使用最近开发的仪器进行的 ATR 映射在光谱质量和空间分辨率方面显示出最佳效果。实际上,已经不仅识别出而且还准确地定位了薄的有机层(约 10 微米)。本文还强调了多元素探测器的最新引入,它可能是映射和成像系统之间的良好折衷。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验