Assessing the penetration of chemicals into excised human skin by non-invasive confocal Raman microscopy

2017 ◽  
Vol 280 ◽  
pp. S272
Author(s):  
Nathalie Jung ◽  
Maike Windbergs
2013 ◽  
Vol 50 (5) ◽  
pp. 601-608 ◽  
Author(s):  
Mukul Ashtikar ◽  
Christian Matthäus ◽  
Michael Schmitt ◽  
Christoph Krafft ◽  
Alfred Fahr ◽  
...  

The Analyst ◽  
2018 ◽  
Vol 143 (20) ◽  
pp. 4990-4999 ◽  
Author(s):  
Maxim E. Darvin ◽  
Johannes Schleusener ◽  
Franziska Parenz ◽  
Olaf Seidel ◽  
Christoph Krafft ◽  
...  

Tattoo ink pigments were non-invasively identified in multicolored tattooed human skinin vivousing optical clearing enhanced confocal Raman microscopy.


2014 ◽  
Vol 23 (12) ◽  
pp. 942-944 ◽  
Author(s):  
Lutz Franzen ◽  
Juliane Anderski ◽  
Viktoria Planz ◽  
Karl-Heinz Kostka ◽  
Maike Windbergs

2012 ◽  
Vol 5 (5) ◽  
pp. 7017 ◽  
Author(s):  
Bernardino Virdis ◽  
Falk Harnisch ◽  
Damien J. Batstone ◽  
Korneel Rabaey ◽  
Bogdan C. Donose

2019 ◽  
Vol 25 (5) ◽  
pp. 653-661 ◽  
Author(s):  
Hequn Wang ◽  
Qihong Zhang ◽  
Guangru Mao ◽  
Oscar Conroy ◽  
Yelena Pyatski ◽  
...  

2019 ◽  
Vol 7 (3) ◽  
pp. 620-628 ◽  
Author(s):  
Sachin Nair ◽  
Jun Gao ◽  
Qirong Yao ◽  
Michael H G Duits ◽  
Cees Otto ◽  
...  

Abstract Confocal Raman microscopy is important for characterizing 2D materials, but its low throughput significantly hinders its applications. For metastable materials such as graphene oxide (GO), the low throughput is aggravated by the requirement of extremely low laser dose to avoid sample damage. Here we introduce algorithm-improved confocal Raman microscopy (ai-CRM), which increases the Raman scanning rate by one to two orders of magnitude with respect to state-of-the-art works for a variety of 2D materials. Meanwhile, GO can be imaged at a laser dose that is two to three orders of magnitude lower than previously reported, such that laser-induced variations of the material properties can be avoided. ai-CRM also enables fast and spatially resolved quantitative analysis, and is readily extended to 3D mapping of composite materials. Since ai-CRM is based on general mathematical principles, it is cost-effective, facile to implement and universally applicable to other hyperspectral imaging methods.


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