Abigail P. Lindstrom

Determination of Si in Standard Reference Material SRM 295x Silica-on-Filter

Respirable crystalline silica is an occupational hazard whose presence in the workplace is strictly regulated. A new series of Standard Reference Materials (SRMs) is being developed to assure the quality of silica measurements and to provide for accurate instrument calibration. SRMs 295x (the value x will designate a specific level of silica loading), Silica-on-Filter, have been prepared by gravimetric delivery of SRM 1878a Respirable Alpha Quartz onto individual filters. The silica on the filter has been verified indirectly by the measurement of the elemental silicon, taking advantage of the knowledge in the filter preparation. An HF acid digestion method has been developed for sample digestion, and a high-resolution inductively coupled plasma mass spectrometric method for the determination of silicon has been developed and validated.

Compton Scattering Artifacts in Electron Excited X-ray Spectra Measured with a Silicon Drift Detector

Artifacts are the nemesis of trace element analysis in electron-excited energy dispersive X-ray spectrometry. Peaks that result from nonideal behavior in the detector or sample can fool even an experienced microanalyst into believing that they have trace amounts of an element that is not present. Many artifacts, such as the Si escape peak, absorption edges, and coincidence peaks, can be traced to the detector. Others, such as secondary fluorescence peaks and scatter peaks, can be traced to the sample. We have identified a new sample-dependent artifact that we attribute to Compton scattering of energetic X-rays generated in a small feature and subsequently scattered from a low atomic number matrix. It seems likely that this artifact has not previously been reported because it only occurs under specific conditions and represents a relatively small signal. However, with the advent of silicon drift detectors and their utility for trace element analysis, we anticipate that more people will observe it and possibly misidentify it. Though small, the artifact is not inconsequential. Under some conditions, it is possible to mistakenly identify the Compton scatter artifact as approximately 1% of an element that is not present.

Homogeneity and Sample Preparation from Grams to Microns using NAA, uXRF, and SEM-EDS

When a sample is described as homogeneous, we mean that there is a lack of variation in a measured property over the volume of the sample. The notion of homogeneity is somewhat ill defined because all samples are inhomogeneous at sufficiently small length scales. So to be precise, one must say that a sample is homogeneous to a certain level of variation over a certain length scale. Whether a sample can be considered homogeneous depends upon the application and the length scale, mm or μm.

Use of a Laser Engraver in Relocations and Sample Preparation for SEM and Light Microscope Analysis.

In a multi-instrument laboratory, it is common to analyze micro-features on multiple different microscopes using a variety of electron and light optical technologies. Relocation is the ability to take a sample from one microscope to another and to identify the same micro-features quickly and efficiently. We use fiducial marks in our relocations – distinctive marks that can be readily identified using various different microscope technologies. The fiducial marks are located in each microscope and coordinate transformations defined that map a feature’s stage coordinate in one microscope to equivalent coordinates in another microscope. For a flat sample, 3 fiducial marks are sufficient to define a transform consisting of five degrees of freedom – a 2-dimension translation, a 2-dimension scale change and a rotation. We investigated a new way of making fiducial marks using a laser engraver. The laser engraver, an Epilog Fusion M2 [1], which works like a printer in Microsoft Windows-compatible applications, can be used to make sophisticated patterns easily and reproducibly. These patterns can be used in various different ways to assist with sample preparation including placing fiducial marks.

Quantitative Analysis Using Asymmetric Adaptive Pulse Processing

Previous work [1] demonstrated that pulse-by-pulse adaptive digital filtering improves the precision of X-ray quantitative analysis for a given sample electron dose, with no loss of accuracy compared to conventional pulse processing. The improvement stems from better energy resolution compared to short fixed digital filtering for the same throughput. The gain in precision is greatest for small peaks below about 5 keV near to or overlapping with larger peaks, such as Al in NIST K412 glass, which has favorable implications for analysis at low accelerating voltages.

Detecting Difficult Minor Elements in Particle Samples by SEM-EDS

One empirical way to develop confidence in detection limits is to run samples consisting of challenging materials. One class of challenging materials includes the series of NIST glasses containing varying amounts of Ti in a Ba, Si oxide matrix. Barium and titanium have a particularly challenging overlap in the characteristic line families that are typically used to quantify these elements, the Ti K and Ba L line families. The NIST glasses contain between 1.7 % and 16.2 % Ti by mass, 43.0 % Ba by mass with the remaining mass fraction consisting of Si and O.

Tweezergate: A Cautionary Tale about Sample Preparation

It is common at meetings and in journal articles to discuss successes and rare to admit to mistakes or failures. In contrast, the authors wish to present a cautionary tale about what can go wrong, and to present just how important careful sample preparation, knowing your tools and the diligent use of control samples can be. A routine analysis of environmental particles using automated scanning electron microscope based energy dispersive X-ray spectrometry (SEMEDS) produced some atypical particle compositions. Normally, unusual particles are of interest in such samples. Unfortunately, our initial excitement cooled when the analysis of the control sample showed similar particles. Both the sample and the control had been prepared by dabbing a carbon sticky tab mounted on an aluminum pin mount on the surface of a cotton wipe. The pin mount was analyzed using SEM-EDX automated particle analysis using a 3-second x-ray acquisition live-time (see Fig 1.) The particles of interest were unusual due to high Co content, which is typically not found in ordinary dust and soil samples. Based on the average composition results from the automated particle analysis, an excellent match for the alloy was found using a Google search. The particles were likely Elgiloy [1], a non-magnetic and corrosion resistant industrial alloy. Based on the analysis of the control, it was assumed that the particles were introduced somewhere in the sample preparation process. Further investigation showed that they were an excellent match for the tweezers that were used to manipulate both the sample and the control wipe during preparation (see Fig 2.) When investigated, it was discovered that the alloy used to manufacture the tweezers had changed between batches. A slightly different model with the same geometry, but of a different alloy, had been ordered. Nevertheless, the tweezers had been cleaned following our routine protocol before use by sonication in ethanol and it was assumed that they were clean. Further investigations revealed that sonication was not particularly effective in removing spalling particles from the surface of the tweezers. A variety of solvents and different cleaning procedures were tried, but none was able to fully eliminate the contamination. The sample preparation process requires the use of tweezers but these tweezers were a particularly poor choice because the tweezer’s alloy was an alloy that would have been of interest in the analysis. If it is not possible to totally eliminate contamination from tools used in the sample preparation process, an alternative approach is to select tools such that the particles they generate will not be mistaken for particles of interest and to know the materials of the tools so they can be eliminated from the data set. The importance of control samples and careful analysis of specimen blanks cannot be understated, as they were paramount to identifying this source of measurement error.