The first paper produced directly from my PhD research was published last month in the journal Applied Spectroscopy. Automated Analysis of Carbon in Powdered Geological and Environmental Samples by Raman Spectroscopy describes a method I developed for collecting and analysing Raman Spectroscopy data, along with Niels Hovius, Albert Galy, Vasant Kumar and James Liu.
I will discuss Raman Spectroscopy in depth in a future post on this site, but the short version is that Raman allows me to determine the crystal structure of pieces of carbon within my samples. A river or marine sediment sample can be sourced from multiple areas, and mixed together during transport. Trying to work out where a sample was sourced from can prove very difficult. However, these source areas often contain carbon of different crystalline states; if I can identify the carbon particles within a sample then the sources of that sample, even if they have been mixed together, can be worked out. The challenge in this procedure is that there can be lots of carbon particles within a sample, and each one might be subtly different. To properly identify each mixed sample, lots of data is required, which can laborious to process.
My paper describes how lots of spectra can be collected efficiently from a powdered sediment sample. By flattening the powder between glass slides and scanning the sample methodically under the microscope, around ten high-quality spectra can be collected in an hour, meaning five to ten samples can be analysed in a day. Powdered samples are much easier to study than raw, unground, sediment, and I have shown that the grinding process does not interfere with the structure of the carbon particles, therefore it is a valid processing technique.
Once the data has been collected, I have devised a method for automatically processing the collected spectrum using a computer, which removes the time-consuming task of identifying and measuring each peak by hand. The peaks that carbon particles produce when analysed by Raman Spectroscopy have been calibrated by other workers to the maximum temperature that the rock experienced, and this allows me to classify each carbon particle into different groupings. These can then be used to compare various samples, characterise the source material and then spot it in the mixed samples.
Delegating as much analysis as possible to a computer ensures that each sample is treated the same, with no bias on the part of the operator, and also cuts down the time required to process each sample, which means that more material can be studied. The computer script used to analyse the samples is freely available and therefore other researchers can apply this to their data, enabling a direct comparison with any samples that I have worked on. This technique will hopefully prove useful to more than just my work in the future, and anyone interested in using it is welcome to contact me. While the paper discusses my application of the technique to Taiwanese sediments, I have already been using it to study Arctic Ocean material as well.
The paper itself is available from the journal via a subscription, and is also deposited along with the computer script in the University of Manchester’s open access library.