δ2H and δ18O of waters by TC/EA

I] Changes made to setup

II] Memory effects

III] Injection problems

IV] Data processing

I] Changes made to setup

NOTE: This page needs to be updated when we get a little more time. For now, here is a brief summary of some of the changes we have made that are not listed further down in this page (posted to isogeochem list on Dec 1, 2006):

We now use an SGE 0.5uL syringe with a wide diameter (23 gauge I think) and conical tip and find that this syringe works much better than the Hamilton one. In addition to more reliable injections and fewer problems with seizing plungers, it is less expensive and has a syringe needle/plunger replacement kit that can be purchased for about $28. Only catch is that you need to replace septa a little more frequently due to the larger diameter needle. Injection volumes are 150nL.

All of the information listed below concerning memory effects were observed with the Hamilton syringe and may no longer be applicable with the SGE one.

We now use a postinjection delay of 15 seconds instead of the 8 seconds mentioned in the page.

Our glassy carbon tube does not have a narrow diameter hole on top so we cut a piece of graphite tubing that did have a narrow diameter hole (about 4mm diameter hole) and just set it on top of the glassy carbon tube.

We wrap the bottom fitting with about two to three layers of teflon tape to get the seal (just need to feel that it is snug in the glassy carbon tube).

We now have a 7mm I.D. glassy carbon tube and the peak shapes are a little more narrow. I need to throw a screen shot of a data file up there eventually.

We operate the TC/EA at 1400�C (no longer 1450�C) and the GC is at 90 to 110�C.

Below is the page that still needs to be updated...

II] Memory effects

Waters can by analyzed using the TC/EA (Thermo-Chemical Elemental Analyzer) at 1450�C. With our current method, samples are placed into small vials. For small sample sizes it is recommended to use vials with 150mL inserts. These are good for sample sizes down to 50mL or less. For these very small sample sizes it is highly recommended that the user perform a series of tests in advance to make sure that their sample transfer and storage techniques do not adversely effect the isotope ratio of their samples. Please consult with us for this if you have any questions.

There are several issues that arise with the use of a single syringe to inject a variety of samples via a septum into the TC/EA. Most notable is that of sample memory: ie. some remnant of the previously injected sample remains somewhere in the system and biases the measurement for the next few injections. The best way to see such effects and their duration is to perform a series of injections of standards that have extremely different isotope ratios. If the memory effects from standards with extreme values can be nullified, then it stands to reason that such problems will also be negated with real samples.

Here is an illustration of sample memory:

1 memory.GIF

We tried varying the method used to prime and clean the syringe but to no avail. This memory effect even remained after flushing the syringe with relatively large volumes of the next sample. We finally concluded that the memory effect that we are seeing is coming from the injection port septum. Our solution was to perform three 180 nL injections of the sample in rapid succession into the TC/EA, after waiting sufficient time for the resulting analysis gases to elute from the system we perform four analyses of the sample or standard. Ideally this gives up 4 measurements which we can average. Until recently, we were experiencing many "failed" injections (perhaps 10-20%) which could not be used. The precision of the measurement for samples is not given as the precision of these injections, instead we report the standard deviation of the mean value measured for some laboratory standards which were run in replicates throughout the sequence.

Here is an example of the file obtained with the three rapid injections to clear the sample memory:

2 memoryclear.jpg

This is the result of our solution on sample memory. There is one single measurement that came out notably poor. It is the first run of the third sample (ie. the 9th point in the plot). Can you see it?

for hydrogen:

3 MemoryCheck.gif

for oxygen:

4 MemoryCheck_18-O.gif

III] Injection Problems

Here is an example of a very nice injection of 300nL of water:

5 TC-EA_300uL_water.jpg

Here is an example of a very nice injection of 300nL of water:

5 TC-EA_300uL_water.jpg

Conditions for the above data are:

  • TC/EA furnace at 1450°C

  • GC at 80°C

  • Carrier gas flow = 100mL/min (measured at the exit from the GC)

  • GC column  = 5A packed column, 1/4" O.D. by about 50cm.

  • no dilution on the conflo

  • injection volume = 300nL (using a 10uL syringe holder on the PAL equipped with a 1.2uL syringe. Set PAL to inject 2.5uL, this results in 2.5/10 * 1.2uL = 0.3uL)

  • Air volume = 0

  • No solvent precleaning

  • Sample precleaning = 2

  • fill speed = 5.0uL/s

  • fill strokes = 6

  • pullup delay = 500ms

  • preinjection delay = 0ms

  • postinjection delay = 8 sec (note, we found that a long postinjection delay is necessary to allow time for water to come off of the syringe needle. A short delay more frequently resulted in significant peak tailing.)

  • injection speed  = 5uL/s   (note: slowing down the injection rate from the default of 50uL/s to this value has dramatically improved the quality of the injection)

  • postclean solvent = 2 (note that our solvent bottle is empty, this is just an attempt to dry the syringe a little bit)

    Although each run is only 5 minutes, a sequence list will be very long to allow for the multiple sample injections as well as the "memory clearing" of the first rapid injections. Here is a template sequence list for our current setup. It is very long. The user should replace the items labelled spl1, spl2, etc. on the left side of the window with their real sample names. This will update the sequence template in the columns to the right. A full tray of 98 samples currently takes up 760 lines in the sequence. When we find more time to improve our injection technique we hope to reduce the number of replicate injections to 3 per sample and also get rid of the memory clearing step.

6 poorinjection1.jpg

The above injection was fairly common when using a short postinjection delay.

7 poorinjection2.jpg

This is probably the most common "poor" injection we have seen. It is easy to find these in the exported results due to the unusually high amplitude of the m/z 2 peak. With these injections, even though the CO peak doesn't look so bad, the measured δ18O is usually far from what is measured with good injections. After changing the pre-injection delay from 500ms to 0ms and the injection speed from 50uL/s to 5uL/s these poor injections have disappeared.

8 poorinjection3.jpg

This injection was less common and appears to also have been a result of a short post-injection delay.

Precision and Accuracy

Since we finally figured out how to get the nice, reproducible injections (Aug 9, 2006) we are getting very good precision and accuracy. The section below on data processing will show how we achieve these results. In brief, our first data set showed the following results. Note that errors reported are two sigma.

precision and accuracy.jpg

All results above are given on the VSMOW scale. Our water working standards were first calibrated against SMOW/SLAP last year. The outstanding quality of these numbers came as a surprise to us. However, even though our standards look great it is necessary to ask about data quality for samples. These standards are bottled waters (and antarctic water). They will experience minimal matrix effects and so it is still necessary to perform replicates of individual samples in order to get an appreciation for the precision of the numbers for samples. Ideally, we'd also have check standards that have matrices similar to those of the standards. That is not the case yet, but we are hoping to make some in the future.

Here is the data that led to the results posted in the table above. This data is part of a set. The entire data set will be posted on the week of Aug 14th after it has all finished running. At that point we will be able to check the variation of replicates of samples as well as of more check standards. UPDATE THIS

A comment on statistics: The numbers given above are means and standard deviations of three measurements taken at approximately 8 hour intervals. However, it should be recalled that each measurement in our data is itself the mean of four injections at one given time. So, technically, we are actually showing the standard deviation of the means of three measurements, ie. the standard error.

Here is another way to look at the numbers above:

9 12injections.gif

IV] Setting it up

The hardware setup: What you need to do this

You need a TC/EA or similar device that is capable of converting water to H2 and/or CO. We use a PAL autosampler by CTC Analytics with a 10uL syringe holder and a 1.2uL syringe (sold by Hamilton specifically for the PAL). We use 2mL vials either filled with minimal headspace or equipped with 150uL inserts (for small sample sizes).

Thermo Electron sells a "liquid injection kit" for ~$2500. This includes a liquid injection port adapter for the top of the TC/EA, a graphite tube with a narrow top to place on top of the existing glassy carbon tube, a couple of septa (not very good ones), the 1.2uL syringe and maybe one or two other small items. We initially used this setup and obtained very good precision for hydrogen (< 1.5 �) but had inconclusive results for oxygen as our small CO tank ran out and it took three months to get a replacement one what was around natural abundance. As a side note, the "research grade" CO sold by Air Liquide has a δ18O and δ13C of about -5 to -10 � vs VSMOW and VPDB respectively.

An alternative setup using a sort of "wrap around" helium flow discussed in the literature (Gehre et al. Rapid Commun Mass Spectrom., 2004, 18, 2650-2660) reported very good precision for both δ2H (< 0.5 �) and δ18O (< 0.1 �). With funding from John Sabo's group, here at ASU, we purchased the required bottom feed adapter and glassy carbon tube from IVA Analytical, in Germany. One thing that this kit didn't come with was a narrow-topped tube, as described in the literature. We had to take our narrow-topped graphite tube that came with the initial Thermo liquid injection kit and cut it to fit on top of the glassy carbon tube. If one does not do this then peak shapes end up very poor due to the large dead volume at the top of the reactor.

More to come... if you have any questions about this please contact the lab manager.

The software setup

We program the PAL autosampler with one method for the "memory clearing" part (internal no 8 in the sequence screenshot below) and one for a typical sample run (internal no 7 in the sequence screenshot below). In the memory clearing part, the only non-zero method parameters are:

sample volume = 1.5uL (with our 1.2uL syringe this translates to a 180 nL volume)

pullup delay = 500 ms

injection speed = 5 uL/s

postinjection delay = 6 s

The rest is set to zero

The sample run PAL method parameters are given further up in this page.

Here are some screen shots showing the time events for the isodat methods for the sample runs or the memory clearing. Also there is one screen shot of an active sequence. Note that we run 24 hours' worth of a sequence and then perform a new H3 factor measurement. In the future we may not need to do this as the sequence setup (sandwiching samples between bracketing standards) will cancel out any slowly drifting H3 factor.

Here is an active sequence list. Line 400 of the sequence is about 24 hours after the start of this section of the list (we hope to improve throughput later on). Note that the active sequence starts and ends with bracketing standards.

10 sequencelist_crop.JPG

Here is the timing events for the quick succession of sample injections meant to clear any sample memory. Note that the 30 delay in between each injection gives the PAL enough time to go back to its home position. Also, the 290 second end time was selected so that we were sure the CO had completely eluted from the column before proceeding to the next line in the sequence.

11 sampleflushtiming_crop.JPG

Here is what the time events look like for a normal sample analysis. The autosampler takes a while to do the precleaning so we trigger it right at the beginning of the run.

12 sampleruntiming_crop.JPG

V] Data Processing

Probably the most important thing to know about a reported number is how it was obtained. There are many steps in the processing of stable isotope data into final results. It is best to make minimal manipulations of the data and to be explicit. If one is dropping values for some reason, then that reason should be stated. An example (with real data) of how we currently process results from the TC/EA is given here.

Download this file in order to better see what is being done in the tutorial below.

1) Reprocess the data using the "H_and_O" export template.

2) With all data processing, the first thing to do is to copy the raw data into a second worksheet. Call the new worksheet "H_1".

13 01-copy.GIF

3) In the new worksheet, highlight the "identifier 2" column then go to Data > sort and say ok to expand to all data. Select sort by "identifier 2" and delete all of the data with the identifier "clear memory".

14 02-sort_ID2.GIF
15 03-delete_clear_memory.GIF

4) highlight the "Rt" column (retention time) and choose "A to Z" sort option. This will separate the hydrogen data from the CO data. We will process results for each isotope separately.

16 04-sort_by_Rt.GIF

5) Copy the current worksheet to a new one called "O_1". In this new worksheet, delete all of the lines with results for hydrogen isotopes (line 2 to somewhere in the middle). In this same worksheet, delete the two empty columns for hydrogen results.

17 05-Cleanup_O.GIF
18 06-delete_H_columns.GIF

6) Now go back to the H_1 worksheet and delete the rows of data corresponding to oxygen isotope data. Delete the columns related to oxygen isotopes. Your final product should look like this:

19 07-H_data.GIF

7) Highlight the "Rt" column again and select the "Z to A" sort button. This will put the reference gas peaks at the end of the page. Find the line where the results for the reference gas peaks occur and insert 5 lines to separate them from the sample data. (Hint: the ref gas peaks all have a δ2H of 0).

20 08-Separate_refgaspeaks.GIF

8) Add a header in the leftmost cell above the ref gas peaks that says "ref gas peaks". Now copy this worksheet into a new one and name it "H_2".

21 09-refgaspeaks.GIF

This shows the new worksheet:

22 10-H_2.GIF

9) In the "H_2" worksheet, delete all of the reference gas peak data. Also, sort the data by line number and delete the extraneous columns so that the final product looks like this:

23 11-H_2-clean.GIF

10) One very important factor in ensuring data quality with this method is the injection. To test the reproducibility of the injection we plot peak amplitude, peak area, peak width, and retention time as a function of the line number in the sequence. In particular we are looking for any misidentified peak tails, or spikes or dips. Here is what you should have on the screen when done:

24 12-H_2-plots.GIF

In the plots above one can see that there is a single data point with a retention time around 100 seconds, whereas the rest are around 85 seconds. Similarly, there is a single peak with an ampl 2 that is well above the norm and another with a very low amplitude. The peakwidth plot also shows three points which stand out. Place the cursor over the odd point and you will see the sequence line and value for that point:

25 13-Rt-plots.GIF

When checking each plot in this data set, we see the high retention time for line 236, low peak width for lines 190, 236, and 236 (yes, there are two points for line 236: that suggests a peak tail was misidentified), high/low peak amplitude and area for line 236 and 236. Again two points for the same sequence line. This pretty much confirms a split peak and the very high amplitude suggests a messed up injection. Here is what the chromatogram looks like for sequence line 236:

26 14_line-236.GIF

One can see from the chromatogram that the tail of the hydrogen peak has been integrated as well, hence the extra hydrogen peak in the plots. Although there is nothing clearly wrong with the peak shapes here, for consistency we will not include results from this data file as the injection was clearly of a much larger volume than for the rest of the analysis. Here is what the injection at line 235 looked like (note that it is the same water sample):

27 15_line-235.GIF

Above is the injection just prior to the "odd" one at line 236. The peaks are a little more narrow, and in particular, the hydrogen isotope ratio differs by about 10 � from the value at line 236.

Now that we have dealt with the oddity at line 236, we can go and take a look at the data file for line 190, which gave rise to an unusually short peak width. Here is what the chromatogram looks like:

28 16_line-190.GIF

Comparing this data file to the one just above it makes clear that the integration has premuturely identified the peak end a little early. In general, this variation has only a small effect on the final value, however, we can modify the method file to get a more consistent peak width if we want: Click on the "edit method" button (seen in upper left corner of the above image) and go to the "peak detect at H2" tab. Change the peak end slope from the default value of 0.4 mV/sec to 0.05mV/sec as seen below:

29 17_line-190-edit-method.GIF

Click on "ok". Then click on the "reevaluate data" button that is located right next to the "edit method" button. Here is the result:

30 18_line-190-reevaluated.GIF

The peak width is now 39 seconds, which matches well with all other results in this sequence. The d2H measured here has changed from -10.39 in the earlier image to -10.56 here. That amount is not particularly significant for hydrogen isotopes, however, for consistency we should incorporate this value in the spreadsheet.

11) Copy the current results into a new worksheet and call it "H_3". Delete the plots in this new worksheet and go flag any results which should not be used. In the case of this data file delete the line corresponding to the peak tail for sequence line 236 and flag the d2H for the unusually large peak in line 236 in yellow.

31 19_flag-line-236.GIF

Also, find the hydrogen isotope ratio for sequence line 190 and change the δ2H to -10.564 �. Flag this value with a different color and put a comment in the cell next to it explaining why it is flagged:

32 20_flag-line-190.GIF

12) Delete the extraneous columns so that the final product looks like this:

33 21_H_3_clean.GIF

 As can be seen in the data above, there is still some memory effect between samples. It is most notable between the PNZ and DSW-ANT working standards, which span a wide range of isotope ratios. In processing the data we will only use results acquired after the first injection. For the working standards that means that we'll use the mean of the second and third injection. Since we are only performing two injections per sample in this data set, we'll only be using the value of the second injection. As we have replicate analyses incorporated into the sequence, we will still have a good estimate of the analytical uncertainty of the measurements.

13) Label column E as "Mean d2H" and calculate the mean value for the set of injections after the first one for the standards and samples. Be sure not to include the flagged value for line 236.

34 22_H_3_values.GIF

14) Copy this worksheet into "H_4". Highlight all of the data (easiest way is to click on the empty box above the row numbers and to the left of the column letters). Now click on "paste special" > "values". Sort the data by the mean d2H:

35 23_H_4_sorted.GIF

15) Delete the data that does not have a "mean d2H" value. Delete the column headed as d2H/1H and sort the data by line number.

36 24_H_4_delete.GIF
37 25_H_4_clean.GIF

16) Add a column header saying "known d2H" and another saying "d2H vs VSMOW". In the "known d2H" column, put the known values for the working standards (found here):

38 26_H_4_standards.GIF

17) Plot the "known d2H" values as a function of the "mean d2H" values for the bracketing standards (the PNZ and DSW-ANT in this case). Be sure to sandwich the data: see which data are highlighted in the screenshot below. For the plot, choose "xy scatter" and be sure that under the "data range" option (step 2 of 4) the "columns" box is checked. Use the title and axes labels shown in the image below. Also, be sure to remove the gridlines and legend.

39 27_H_4_norm1.GIF

18) In the final plot, right click on a data point and choose "add trendline". In the trendline options choose "show equation". I would also recommend right clicking in the area between the plot and the border and choosing "edit chart area", then go to the font option and turn of the autoscale. Shrink the plot and move it to the side.

40 28_H_4_norm1.GIF

19) Use the equation from the normalization plot to calculate the hydrogen isotope ratios for the samples and check standards:

41 29_H_4_norm1.GIF

20) Copy the normalization plot and paste it between the next set of bracketing standards. Change the title to normalization 2. Right click on the plot area and select "source data". Highlight the new set of data to use for the normalization:

42 30_H_4_norm2.GIF

(note that the screen capture option missed the outline that excel makes on the highlighted source data so I added the boxes to highlight them in the image above.)

21) Use the new equation to calculate the hydrogen isotope ratios for samples in this section of the sequence.

43 31_H_4_norm2.GIF

22) Repeat this process for all of the data. After that, copy this worksheet into another one called "H_results". In the new worksheet, highlight everything, select "copy" then select "paste special" > "values". Delete all of the plots as well as the column labeled "mean d2H".

44 32_H_results_clean.GIF

23) Sort the data by identifier 1. We want to place all of the results for standards on top of the sheet, and results for samples further down. To do this, cut the rows containing sample data and choose "insert cut cells" to paste it at the top of the sheet:

45 33_H_results_insertcutcells.GIF

24) Delete the rows corresponding to the bracketing standards (they should have no value for "d2H vs VSMOW"). Organize the data as shown below. Also, calculate the mean and standard deviation for the replicates of the standards.

46 34_H_results_organizing.GIF

25) Copy the values for the means and standard deviations for the working standards into the top table. Note: don't just copy or drag the formula over to the cell because of the next step: Delete the rows with the detailed results from the working standards so that your sheet looks like this:

47 35_H_results_organizing2.GIF

26) For the sample resutls, delete the section called "known d2H". Then sort the results for the samples by identifier 1. To do this, highlight just the data for the samples and go to "Data" > "Sort". Choose "identifier 1" and then in the second category choose "line":

48 36_H_results_organizing3.GIF

27) Sample that were run in triplicate in the sequence will have the word "replicate" in the "identifier 2" column. Calculate the mean and standard deviation for these items. (Note triplicate runs will all have the same "identifier 1"). Add a section between the standards and samples in which you list the replicates and their associated standard deviations.

49 38_H_results_summary.GIF

Analytical uncertainty for the replicate samples can be reported as the standard deviation of those replicate measurements. For the rest of the samples one should use the mean of the standard deviations of the replicates as the estimated uncertainty.

The DAS, MSW, and DMSW check standards serve to demonstrate the overall quality of the laboratory equipment and measurement. Ideally, these standards would be closely matched to the sample matrix. In general, the measured values should be within 2 standard deviations of the known value, however, unusually good precision does sometimes occur so if they are within 2 � of the known value (assumes a stdev of 1 �) then we will be happy with that result.

28) The same sort of data processing can be done with the oxygen isotopes.