This work was partially supported by the National Science Centre (NCN) within SONATA14 2018/31/D/ST5/00399 and OPUS10 2015/19/B/ST7/02163 projects.

1. Defect selective etching
<ol>
	<li>Solid etchant preparation
	<ol>
		<li>Prepare&#160;eutectic mixture of strong bases of potassium hydroxide (KOH) and sodium hydroxide (NaOH), together with magnesium oxide (MgO), by dissolving and mixing the composing alkali hydroxides and metal oxide in distilled water. Keep stoichiometric quantities of 53.6/37.3/9.1 at % of NaOH, KOH, and MgO, respectively13. MgO addition increases the etchant viscosity such that it remains on the Ga-polar surface and does not flow over the edges to the N-polar surface13,14. All chemicals used should be practical grade quality.</li>
		<li>Heat the mixture in a flask on a hot plate to 200 &#176;C and agitate by magnetic stirring for 1 h (above the melting point of the KOH-NaOH eutectic point).</li>
		<li>Cool the mixture to ~100 &#176;C by reducing the temperature of a hot plate to completely evaporate the remaining liquid. This step depends on flask size and water volume, so it may require several minutes up to 1 h.</li>
		<li>Transfer the solid etchant (denoted as E+M) into a dried bottle, avoiding exposure to moisture. 
		CAUTION: KOH and NaOH may cause skin irritation and eye damage. Work with gloves and goggles. The protocol can also be paused here.</li>
	</ol>
	</li>
	<li>Defect selective etching
	<ol>
		<li>Prepare a clean GaN surface for the analysis. GaN that is epitaxially grown on sapphire is used in the following steps12.</li>
		<li>Place the GaN sample on a hot plate heated to ~450 &#176;C. Place the thermocouple near the sample to precisely read the real temperature.</li>
		<li>Place a piece of solid E+M etchant on top of the GaN and leave for 3 min.</li>
		<li>Take the sample from the hot plate and place in a beaker with hot HCl for 3-5 min to eliminate remaining E+M.</li>
		<li>Remove the sample from HCl and insert in a beaker with deionized (DI) water and ultrasonic bath for 5-10 min.</li>
		<li>Dry the sample by N2 blowing. 
		CAUTION: HCl may cause skin irritation and eye damage. Work with gloves and goggles. Avoid getting burnt. The protocol can also be paused here.</li>
	</ol>
	</li>
</ol>
2. Scanning electron microscopy (SEM) observation
<ol>
	<li>Mark the sample (e.g., with an L-shaped scratch using a diamond pen cutter).</li>
	<li>Mount the sample on a metal stub dedicated to the SEM model to be used, using a conductive adhesive (i.e., double-sided carbon conductive tape or a similar material). Use gloves during sample preparation and transfer to avoid grease contamination from hands.</li>
	<li>Add a piece of the tape from step 2.2 to connect the sample surface with a metal stub to prevent charge buildup on the specimen surface. Alternatively, a sputtered coating with conductive material (~10 nm thick) can be applied to prevent charging effects.</li>
	<li>Acquire at least three high-resolution SEM micrographs (ideally, a minimum of five) of a top view of the sample. Each image should display an area of at least 25&#160;x 25 &#181;m. Avoid taking images from the surface regions with macroscopic surface defects. Figure 1 presents a typical result.</li>
	<li>Note the exact position of each picture with a respect to the L-shaped marker. 
	NOTE: The protocol can be paused here.</li>
</ol>
3. Secondary ion mass spectrometry measurements
<ol>
	<li>Tool calibration
	<ol>
		<li>Calibrate the SIMS equipment using negative polarity, Cs primary ions with 7-13 keV impact energy. Align the secondary and primary beams. Keep the beam as small as possible (at least 1 &#181;m in diameter), as the lateral resolution is predetermined by the size of the beam.</li>
		<li>Prepare five-seven settings for beams with various ion current density. For simplicity, keep the size of the beam intact, and change the beam current . Measure the beam current and size of the beam15. In the following steps, beam currents of 5 nA, 10 nA, 15 nA, 20 nA, 30 nA, and 50 nA and a spot size of 1&#160;&#181;m are used.</li>
		<li>Use a 50&#160;x 50 &#181;m raster size and 35&#160;x 35 &#181;m analysis area for the following steps. Choose 256 x 256 pixels for spatial resolution. If not specified otherwise, use a standard integration time for each signal (typically 1-2 s).</li>
	</ol>
	</li>
	<li>Primary source stability
	<ol>
		<li>Choose a setting with a moderate beam current (15-20 nA).</li>
		<li>Obtain&#160;series of images using 30Si2- secondary ion for a blank silicon wafer. For each image, integrate the signal for 5-10 min.</li>
		<li>Perform pixel-to-pixel comparisons of all images with the first image. If &#62;5% of pixels show a &#62;5% difference from the first image, this indicates that the beam became unstable. Note the timespan of the beam stability.</li>
	</ol>
	</li>
	<li>Measurement 
	NOTE: The following steps are performed within a stable timespan of the beam.
	<ol>
		<li>Follow the procedure described by Peres et al. to estimate the background level of oxygen contamination in the measurement chamber7. For each measurement, there is no need to obtain the absolute values of oxygen concentration, as the intensity ratio of 16O-&#160;and 69Ga- signals is sufficient.</li>
		<li>Use beam settings prepared in step 3.1.2. Perform at least five measurements for each beam setting. Obtain a depth profile using 16O- secondary ion, reach a ~200 nm depth, and measure the intensity of 69Ga- secondary ion by integrating the signal for 10-15 s. Do not perform this in regions where SEM images have been obtained.</li>
		<li>Plot the intensity ratio of 16O- and 69Ga- signals as a function of the inversed primary current density (there is no need to calculate absolute values). A good linear fit is expected (here, R2 = 0.997). Estimate the vacuum background contribution as shown in Figure 2.</li>
		<li>Choose an intense beam (30 nA) for the following steps. Obtain an image that will be used for flat-field correction. Use a 30Si2- secondary ion for a blank silicon wafer. Integrate the signal for 5-10 min. Figure 3 presents a typical result.</li>
		<li>Perform depth profile measurements in the same regions where SEM images were aquired. Using a 16O-&#160;secondary ion, integrate the signal for 3-5 s for each data point. 
		NOTE: The protocol can be paused here.</li>
	</ol>
	</li>
</ol>
4. Data treatment
<ol>
	<li>Reconstruct a 3D image from a depth profile.</li>
	<li>Perform flat-field correction: pixel-to-pixel normalize each 16O- ion image using a reference image obtained in step 3.3.4. Figure 4A presents raw data and Figure 4B presents the image after the flat-field correction.</li>
	<li>Estimate vacuum background contribution from a plot obtained in step 3.3.2. There is no need to calculate absolute values; however, note the specific percentage of total counts that can be attributed to vacuum background contribution. A value between 90-95% is typical for such an experiment.</li>
	<li>Subtract the vacuum background contribution: randomly eliminate 90-95% of the registered 16O- counts. Figure 4C presents a typical result for a single plane.</li>
	<li>Plot the remaining counts as a 3D image. Figure 5 presents a typical result.</li>
	<li>Integrate signals from all data points and superimpose the 2D image on the previously obtained SEM image using any image editor software with layer support. Treat the SEM image as background. A layer containing SIMS results should only contain actual counts as colored pixels (delete the white regions in between). Add ~30% of transparency to this layer. Figure 6 presents a typical result</li>
</ol>

<ol>
	<li>Wittmaack, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-sensitivity+depth+profiling+of+arsenic+and+phosphorus+in+silicon+by+means+of+SIMS.">High-sensitivity depth profiling of arsenic and phosphorus in silicon by means of SIMS.</a> Applied Physics Letters. 29, 552 (1976).</li><li>Ber, B. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Secondary+ion+mass+spectroscopy+investigations+of+magnesium+and+carbon+doped+gallium+nitride+films+grown+by+molecular+beam+epitaxy.">Secondary ion mass spectroscopy investigations of magnesium and carbon doped gallium nitride films grown by molecular beam epitaxy.</a> Semiconductor Science and Technology. 13, 71-74 (1998).</li><li>Chiou, C. Y., Wang, C. C., Ling, Y. C., Chiang, C. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Secondary+ion+mass+spectrometry+analysis+of+In-doped+p-type+GaN+films.">Secondary ion mass spectrometry analysis of In-doped p-type GaN films.</a> Applied Surface Science. 203-204, 482-485 (2003).</li><li>Emziane, M., Durose, K., Halliday, D. P., Bosio, A., Romeo, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+situ+oxygen+incorporation+and+related+issues+in+CdTe/CdS+photovoltaic+devices.">In situ oxygen incorporation and related issues in CdTe/CdS photovoltaic devices.</a> Journal of Applied Physics. 100, 013513 (2006).</li><li>Matsunaga, T., Yoshikawa, S., Tsukamoto, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Secondary+ion+yields+of+C,+Si,+Ge+and+Cs+surface+density+and+concentration+in+SIMS.">Secondary ion yields of C, Si, Ge and Cs surface density and concentration in SIMS.</a> Surface Science. 515, 390-402 (2002).</li><li>Gnaser, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SIMS+detection+in+the+1012+atoms+cm&#8722;3+range.">SIMS detection in the 1012 atoms cm&#8722;3 range.</a> Surface and Interface Analysis. 25, 737-740 (1997).</li><li>Peres, P., Merkulov, A., Choi, S. Y., Desse, F., Schuhmacher, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterization+of+LED+materials+using+dynamic+SIMS.">Characterization of LED materials using dynamic SIMS.</a> Surface and Interface Analysis. 45, 437-440 (2013).</li><li>Arslan, I., Browning, N. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Role+of+Oxygen+at+Screw+Dislocations+in+GaN.">Role of Oxygen at Screw Dislocations in GaN.</a> Physical Review Letters. 91, 165501 (2003).</li><li>Hawkridge, M. E., Cherns, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oxygen+segregation+to+dislocations+in+GaN.">Oxygen segregation to dislocations in GaN.</a> Applied Physics Letters. 87, 221903 (2005).</li><li>Arslan, I., Bleloch, A., Stach, E. A., Ogut, S., Browning, N. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+EELS+to+observe+composition+and+electronic+structure+variations+at+dislocation+cores+in+GaN.">Using EELS to observe composition and electronic structure variations at dislocation cores in GaN.</a> Philosophical Magazine. 86, 4727-4746 (2006).</li><li>Hautakangas, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gallium+and+nitrogen+vacancies+in+GaN:+Impurity+decoration+effects.">Gallium and nitrogen vacancies in GaN: Impurity decoration effects.</a> Physica B: Condensed Matter. 376-377, 424-427 (2006).</li><li>Micha&#322;owski, P. P., Z&#322;otnik, S., Rudzi&#324;ski, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three+dimensional+localization+of+unintentional+oxygen+impurities+in+gallium+nitride.">Three dimensional localization of unintentional oxygen impurities in gallium nitride.</a> Chemical Communications. 55, 11539 (2019).</li><li>Kamler, G., Weyher, J. L., Grzegory, I., Jezierska, E., Wosi&#324;ski, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Defect-selective+etching+of+GaN+in+a+modified+molten+bases+system.">Defect-selective etching of GaN in a modified molten bases system.</a> Journal of Crystal Growth. 246, 21-24 (2002).</li><li>Zhuang, D., Edgar, J. H., Strojek, B., Chaudhuri, J., Rek, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Defect-selective+etching+of+bulk+AlN+single+crystals+in+molten+KOH/NaOH+eutectic+alloy.">Defect-selective etching of bulk AlN single crystals in molten KOH/NaOH eutectic alloy.</a> Journal of Crystal Growth. 262, 89-94 (2004).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> SC-Ultra, User's Guide. , (2005).</li><li>Weyher, J. v. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Orthodox+etching+of+HVPE-grown+GaN.">Orthodox etching of HVPE-grown GaN.</a> Journal of Crystal Growth. 305, 384-392 (2007).</li></ol>

The authors have nothing to disclose.

Issues of nonuniformity of the detector and vacuum background contribution are easy to solve by flat-field correction and subtraction of parasitic counts, respectively. The subtraction procedure is not perfect, as it may subtract a contribution where oxygen has been agglomerated. In contrast, at the other position, it will leave the background count unaffected; thus, some artificial counts may still be present while some real counts are reduced. Nevertheless, it is efficient and sensitive enough to provide acceptable results.
The primary beam instability is the most problematic, as deterioration of the primary beam parameters will blur the secondary ion image; thus, no reliable information about the sample can be obtained. Section 3.2 in the protocol is particularly important. For instance, for a well-aligned beam, the first 30Si2- secondary ion image reflects nonuniformity of the detector, but after some time, the image will start to change. This is caused by deterioration of primary beam parameters (i.e., primary current loss, defocusing, position drift, etc.). It is therefore important to estimate the timespan of beam stability. It is advised to start the experiment 2-3 h after initialization of the beam, as it is typically more stable.
If the experiment is performed within a stable timespan of the beam and the result is still not satisfactory, it is advised to consider the quality of the primary beam. For a small primary beam, it is more challenging to confirm sufficient quality by observing a secondary ion image only. It is therefore advised to perform atomic force microscopy roughness tests at the crater bottom after sputtering ~1 &#181;m of a very flat material (i.e., a blank silicon wafer). If the root mean squared roughness is above 1 nm, then further optimization of the primary beam is required.
The size of the beam limits the lateral resolution of this method. SIMS can image features that are smaller than the beam size, but the secondary ion image will inherit the shape and size of the primary ion beam. If a distance between two features is smaller than the size of the beam, the secondary ion image will blur them together. Despite these issues, the method allows users to obtain a realistic 3D distribution of impurities/dopants in solid state samples. Furthermore, any spatial segregation of atoms can be correlated to the position of defects and interfaces.
For GaN-based structures (i.e., oxygen-decorated), dislocations acting as local nonradiative recombination centers are responsible for n-type conductivity. For other materials any inhomogeneity of the dopant/contaminating atoms distribution may have major impacts on the performance of a device. Thus, the protocol is particularly useful for failure analysis and optimization of growth and processing procedures.

Secondary ion mass spectrometry (SIMS) is a well-known technique used for contamination monitoring with excellent detection limits1,2,3,4,5,6. Vacuum background contribution can be problematic for light elements (e.g., hydrogen, carbon, nitrogen, oxygen), which may be present in the form of residual gases in a measurement chamber. Peres et al. previously established a technique to estimate background contribution; thus, a realistic concentration of contaminating atoms can be determined7.
In many materials, the distribution of contaminating atoms is not uniform. The case of gallium nitride (GaN) is particularly interesting, as it is predicted that oxygen mainly decorates screw and mixed dislocations8,9,10,11. Considering that most analytical methods lack sensitivity or spatial resolution to detect low concentration contaminating atoms, it is essential to develop a SIMS measurement procedure that is capable of 3D localization of segregated impurities12.
While many SIMS spectrometers are equipped with position sensitive detectors, a direct three-dimensional (3D) reconstruction of a depth profile is insufficient to obtain realistic distribution of oxygen atoms in a GaN sample. Imperfection of the detector may distort the image and prevent researchers from obtaining a realistic distribution of contaminating atoms. However, a large problem is vacuum background contribution, as usually &#62;90% of registered oxygen counts originate from residual gases present in the analysis chamber. Presented here is a method to identify and adequately solve each of these challenges.
Nonuniformity of the detector can be tested on a blank silicon wafer. Even a long integration time can lead to the observation of some secondary ion image nonuniformity, due to varying sensitivity of each channel in a microchannel plate detector. Therefore, flat-field correction is needed to obtain high-quality images of 3D distributions of segregated atoms.
Vacuum background contribution is related to a flux of contaminating atoms from the vacuum adsorbed onto the analyzing area. Considering that the process is dynamic (i.e., the sample surface is constantly sputtered by the primary beam), it can be assumed that each point of the analyzed area has the same probability to adsorb these oxygen atoms. Furthermore, they are almost immediately sputtered and do not have enough time to segregate. Therefore, a statistical approach is the most efficient. Random elimination of 90% (or more) of oxygen counts should reveal regions where oxygen is agglomerated.
It should be noted that the stability of the primary beam is crucial for this type of experiment. After some time, the intensity and homogeneity of the beam deteriorates, which reduces the quality of the image. It is therefore essential to estimate a timespan of stable operation of the beam and perform all experiments before the beam becomes unstable. The protocol can be easily used for other materials and detected elements at which nonuniform distribution is expected. It is particularly interesting to combine this with wet chemical etching, which reveals the positions and types of dislocation. Thus, the position of agglomerated impurities can be correlated to the position of defects.

<table><tbody><tr><td>Heating plate with ceramic top plate</td><td>IKA - Werke GmbH</td><td>3644200</td><td>for defect selective etching; yellow MAG HP 7</td></tr><tr><td>Hydrochloric acid (HCl) solution 35-38%</td><td>Chempur</td><td>115752837</td><td>for etchant removal; pure p.a.; CAS: 7647-01-0</td></tr><tr><td>Magnesium oxide (MgO)</td><td>Chempur</td><td>116140200</td><td>for eutectic solid etchant prepration; pure p.a.; CAS: 1309-48-4</td></tr><tr><td>Potassium hydroxide (KOH)</td><td>POCH S.A.</td><td>746800113</td><td>for eutectic solid etchant prepration; pure p.a.; CAS: 1310-58-3</td></tr><tr><td>Sodium hydroxide (NaOH)</td><td>POCH S.A.</td><td>810925112</td><td>for eutectic solid etchant prepration; pure p.a.; CAS: 1310-73-2</td></tr><tr><td>Secondary ion mass spectrometer</td><td>CAMECA</td><td>IMS SC Ultra</td><td></td></tr><tr><td>Scanning electron microscope</td><td>Hitachi</td><td>SU8230</td><td></td></tr></tbody></table>

3d depth profile reconstruction of segregated impurities using secondary ion mass spectrometry

The presented protocol combines excellent detection limits (1 ppm to 1 ppb) using secondary ion mass spectrometry (SIMS) with reasonable spatial resolution (~1 &#181;m). Furthermore, it describes how to obtain realistic three-dimensional (3D) distributions of segregated impurities/dopants in solid state materials. Direct 3D depth profile reconstruction is often difficult to achieve due to SIMS-related measurement artifacts. Presented here is a method to identify and solve this challenge. Three major issues are discussed, including the i) nonuniformity of the detector being compensated by flat-field correction; ii) vacuum background contribution (parasitic oxygen counts from residual gases present in the analysis chamber) being estimated and subtracted; and iii)&#160;performance of all steps within a stable timespan of the primary ion source. Wet chemical etching is used to reveal the position and types of dislocation in a material, then the SIMS result is superimposed on images obtained via scanning electron microscopy (SEM). Thus, the position of agglomerated impurities can be related to the position of certain defects. The method is fast and does not require sophisticated sample preparation stage; however, it requires a high-quality, stable ion source, and the entire measurement must be performed quickly to avoid deterioration of the primary beam parameters.

Very clear pillar-shaped structures should be observed in the 3D image. More oxygen should be agglomerated in a region closer to the surface, since the etching process introduces more oxygen that can diffuse through the sample. Figure 7 presents a 3D image of raw data and an animation of how the reduction procedure reveals the final result. Figure 4C also presents a typical result for a single plane.
The SIMS image superimposed on the SEM image reveals that oxygen is agglomerated along cores of largest etch pits. These can be attributed to mixed/screw dislocations15. It should be noted that if the core is smaller than the size of a primary beam, the secondary image will inherit the size and shape of the primary beam. In suboptimal experiments, a random distribution of oxygen counts can be seen (Figure 8). Figure 9 presents a situation in which the beam becomes unstable during the experiment. Specifically, the quality is high for a region so close to the surface, but it gradually deteriorates during the experiment.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61065/61065fig1.jpg" /> 
Figure 1: SEM micrographs of etch pits revealed on GaN surface using E+M etch. The etching parameters were set&#160;at 450 oC for 3 min.The inset depicts a magnified micrograph with revealed hexagonal pits generated on dislocation cores. The two biggest pits (&#62;500 nm) represent dislocations with screw component of the Burgers vector. This figure has been reproduced with permission12. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61065/61065fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61065/61065fig2.jpg" /> 
Figure 2: Average O-&#160;concentration vs. inverse primary current. Vacuum background contribution can be estimated from the plot. Error bars represent the standard deviation&#160;of each data set (five measurements). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61065/61065fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61065/61065fig3.jpg" /> 
Figure 3: A typical 30Si2- secondary ion image for a blank silicon wafer. Intensity differences are caused by nonuniformity of the detector. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61065/61065fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61065/61065fig4v2.jpg" /> 
Figure 4: Typical plane view of oxygen counts distribution measured in 3D mode. Shown are images (A) from raw data,&#160;(B) after flat-field correction, and (C) after subtraction of vacuum background contribution. This figure has been adapted with permission12. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61065/61065fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61065/61065fig5.jpg" /> 
Figure 5: 3D view of oxygen counts in a 5 &#181;m x 5 &#181;m x 1 &#181;m cuboid. For better visibility, the z-scale is elongated. See Supplemental Figure 1 for animation. This figure has been adapted with permission12. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61065/61065fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/61065/61065fig6.jpg" /> 
Figure 6: Lateral distribution of oxygen secondary ions (blue pixels) projected on&#160;SEM micrograph. Despite SIMS-related artifacts (lateral resolution determined by size of the primary beam), a clear correlation between the positions of largest pits and oxygen are observed. This figure has been adapted with permission12. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61065/61065fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/61065/61065fig7.jpg" /> 
Figure 7: Animation showing how the reduction procedure is performed. At the beginning of the procedure, all counts are present, then for each layer, 90% of counts are randomly eliminated. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61065/61065fig7.mp4" target="_blank">Please click here to download this animation.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/61065/61065fig8.jpg" /> 
Figure 8: Random distribution of oxygen counts in suboptimal experiment. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61065/61065fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/61065/61065fig9.jpg" /> 
Figure 9: Experiment performed with an unstable beam. The quality deceases with sputtering depth.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61065/61065fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Figure 1.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61065/Supplemental_Figure_1.mp4" target="_blank">Please click here to download this figure.</a>

Watch this Scientific Journal Video about 3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry at JoVE.com

3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry

The presented method describes how to identify and solve measurement artifacts related to secondary ion mass spectrometry as well as obtain realistic 3D distributions of impurities/dopants in solid state materials.

The presented protocol combines excellent detection limits (1 ppm to 1 ppb) using secondary ion mass spectrometry (SIMS) with reasonable spatial ...

3d-depth-profile-reconstruction-segregated-impurities-using-secondary

Nanyang Technological University

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JoVE Journal

Chemistry

1.6K Views.  Łukasiewicz Research Network-Institute of Electronic Materials Technology. De gepresenteerde methode beschrijft hoe meetartefacten met betrekking tot secundaire ionenmassaspectrometrie kunnen worden geïdentificeerd en opgelost, evenals het verkrijgen van realistische 3D-verdelingen van onzuiverheden/doteermiddelen in vastestofmaterialen. 

Video: 3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry

Preparation of Homogeneous MALDI Samples for Quantitative Applications

This protocol demonstrates a simple sample preparation to reduce spatial heterogeneity in ion signals during matrix-assisted laser desorption/ionization (MALDI) mass spectrometry. The heterogeneity of ion signals is a severe problem in MALDI, which results in poor data reproducibility and makes MALDI unsuitable for quantitative analysis. By regulating sample plate temperature during sample preparation, thermal-induced hydrodynamic flows inside droplets of sample solution are able to reduce the heterogeneity problem. A room-temperature sample preparation chamber equipped with a temperature-regulated copper base block that holds MALDI sample plates facilitates precise control of the sample drying condition. After drying of sample droplets, the temperature of sample plates is returned to room temperature and removed from the chamber for subsequent mass spectrometric analysis. The areas of samples are examined with MALDI-imaging mass spectrometry to obtain the spatial distribution of all components in the sample. In comparison with the conventional dried-droplet method that prepares samples under ambient conditions without temperature control, the samples prepared with the method demonstrated herein show significantly better spatial distribution and signal intensity. According to observations using carbohydrate and peptide samples, decreasing substrate temperature while maintaining the surroundings at ambient temperature during the drying process can effectively reduce the heterogeneity of ion signals. This method is generally applicable to various combinations of samples and matrices.

A protocol for reducing spatial heterogeneities of ion signals in MALDI mass spectrometry by regulating substrate temperature during sample drying processes is demonstrated.

This protocol demonstrates a simple sample preparation to reduce spatial heterogeneity in ion signals during matrix-assisted laser desorption/ionization ...

Biochemistry

Sheathless Capillary Electrophoresis&#8211;Mass Spectrometry for Metabolic Profiling of Biological Samples

In metabolomics, a wide range of analytical techniques is used for the global profiling of (endogenous) metabolites in complex samples. In this paper, a protocol is presented for the analysis of anionic and cationic metabolites in biological samples by capillary electrophoresis&#8211;mass spectrometry (CE-MS). CE is well-suited for the analysis of highly polar and charged metabolites as compounds are separated on the basis of their charge-to-size ratio. A recently developed sheathless interfacing design, i.e., a porous tip interface, is used for coupling CE to electrospray ionization (ESI) MS. This interfacing approach allows the effective use of the intrinsically low-flow property of CE in combination with MS, resulting in nanomolar detection limits for a broad range of polar metabolite classes. The protocol presented here is based on employing a bare fused-silica capillary with a porous tip emitter at low-pH separation conditions for the analysis of a broad array of metabolite classes in biological samples. It is demonstrated that the same sheathless CE-MS method can be used for the profiling of cationic metabolites, including amino acids, nucleosides and small peptides, or anionic metabolites, including sugar phosphates, nucleotides and organic acids, by only switching the MS detection and separation voltage polarity. Highly information-rich metabolic profiles in various biological samples, such as urine, cerebrospinal fluid and extracts of the glioblastoma cell line, can be obtained by this protocol in less than 1 hr of CE-MS analysis.

A protocol for metabolic profiling of biological samples by capillary electrophoresis&#8211;mass spectrometry using a sheathless porous tip interface design is presented.

In metabolomics, a wide range of analytical techniques is used for the global profiling of (endogenous) metabolites in complex samples. In this paper, a ...

In Situ Characterization of Hydrated Proteins in Water by SALVI and ToF-SIMS

This work demonstrates in situ characterization of protein biomolecules in the aqueous solution using the System for Analysis at the Liquid Vacuum Interface (SALVI) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). The fibronectin protein film was immobilized on the silicon nitride (SiN) membrane that forms the SALVI detection area. During ToF-SIMS analysis, three modes of analysis were conducted including high spatial resolution mass spectrometry, two-dimensional (2D) imaging, and depth profiling. Mass spectra were acquired in both positive and negative modes. Deionized water was also analyzed as a reference sample. Our results show that the fibronectin film in water has more distinct and stronger water cluster peaks compared to water alone. Characteristic peaks of amino acid fragments are also observable in the hydrated protein ToF-SIMS spectra. These results illustrate that protein molecule adsorption on a surface can be studied dynamically using SALVI and ToF-SIMS in the liquid environment for the first time.

In Situ Characterization of Hydrated Proteins in Water by SALVI and ToF-SIMS

This work presents a protocol for liquid handling and sample introduction to a microchannel for in situ time-of-flight secondary ion mass spectrometry analysis of protein biomolecules in an aqueous solution.

This work demonstrates in situ characterization of protein biomolecules in the aqueous solution using the System for Analysis at the Liquid Vacuum ...

Imaging of Biological Tissues by Desorption Electrospray Ionization Mass Spectrometry

Mass spectrometry imaging (MSI) provides untargeted molecular information with the highest specificity and spatial resolution for investigating biological tissues at the hundreds to tens of microns scale. When performed under ambient conditions, sample pre-treatment becomes unnecessary, thus simplifying the protocol while maintaining the high quality of information obtained. Desorption electrospray ionization (DESI) is a spray-based ambient MSI technique that allows for the direct sampling of surfaces in the open air, even in vivo. When used with a software-controlled sample stage, the sample is rastered underneath the DESI ionization probe, and through the time domain, m/z information is correlated with the chemical species' spatial distribution. The fidelity of the DESI-MSI output depends on the source orientation and positioning with respect to the sample surface and mass spectrometer inlet. Herein, we review how to prepare tissue sections for DESI imaging and additional experimental conditions that directly affect image quality. Specifically, we describe the protocol for the imaging of rat brain tissue sections by DESI-MSI.

Desorption electrospray ionization mass spectrometry (DESI-MS) is an ambient method by which samples, including biological tissues, can be imaged with minimal sample preparation. By rastering the sample below the ionization probe, this spray-based technique provides sufficient spatial resolution to discern molecular features of interest within tissue sections.

Mass spectrometry imaging (MSI) provides untargeted molecular information with the highest specificity and spatial resolution for investigating biological ...

Bioengineering

In Situ Characterization of Shewanella oneidensis MR1 Biofilms by SALVI and ToF-SIMS

Bacterial biofilms are surface-associated communities that are vastly studied to understand their self-produced extracellular polymeric substances (EPS) and their roles in environmental microbiology. This study outlines a method to cultivate biofilm attachment to the System for Analysis at the Liquid Vacuum Interface (SALVI) and achieve in situ chemical mapping of a living biofilm by time-of-flight secondary ion mass spectrometry (ToF-SIMS). This is done through the culturing of bacteria both outside and within the SALVI channel with our specialized setup, as well as through optical imaging techniques to detect the biofilm presence and thickness before ToF-SIMS analysis. Our results show the characteristic peaks of the Shewanella biofilm in its natural hydrated state, highlighting upon its localized water cluster environment, as well as EPS fragments, which are drastically different from the same biofilm's dehydrated state. These results demonstrate the breakthrough capability of SALVI that allows for in situ biofilm imaging with a vacuum-based chemical imaging instrument.

In Situ Characterization of Shewanella oneidensis MR1 Biofilms by SALVI and ToF-SIMS

This article presents a method for growing a biofilm for in situ time-of-flight secondary ion mass spectrometry for chemical mapping in its hydrated state, enabled by a microfluidic reactor, System for Analysis at the liquid Vacuum Interface. The Shewanella oneidensis MR-1 with green fluorescence protein was used as a model.

Bacterial biofilms are surface-associated communities that are vastly studied to understand their self-produced extracellular polymeric substances (EPS) ...

Atom Probe Tomography Analysis of Exsolved Mineral Phases

Element diffusion rates and temperature/pressure control a range of fundamental volcanic and metamorphic processes. Such processes are often recorded in lamellae exsolved from host mineral phases. Thus, the analysis of the orientation, size, morphology, composition and spacing of exsolution lamellae is an area of active research in the geosciences. The conventional study of these lamellae has been conducted by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), and more recently with focused ion beam (FIB)-based nanotomography, yet with limited chemical information. Here, we explore the use of atom probe tomography (APT) for the nanoscale analysis of ilmenite exsolution lamellae in igneous titanomagnetite from ash deposits erupted from the active Soufri&#232;re Hills Volcano (Montserrat, British West Indies). APT allows the precise calculation of interlamellar spacings (14&#8211;29 &#177; 2 nm) and reveals smooth diffusion profiles with no sharp phase boundaries during the exchange of Fe and Ti/O between the exsolved lamellae and the host crystal. Our results suggest that this novel approach permits nanoscale measurements of lamellae composition and interlamellar spacing that may provide a means to estimate the lava dome temperatures necessary to model extrusion rates and lava dome failure, both of which play a key role in volcanic hazard mitigation efforts.

Analysis of the morphology, composition, and spacing of exsolution lamellae can provide essential information to understand geological processes related to volcanism and metamorphism. We present a novel application of APT for the characterization of such lamellae and compare this approach to the conventional use of electron microscopy and FIB-based nanotomography.

Element diffusion rates and temperature/pressure control a range of fundamental volcanic and metamorphic processes. Such processes are often recorded in ...

Environment

Sample Preparation for Probe Electrospray Ionization Mass Spectrometry

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ratios (m/z), which are useful to deduce molecular weights and structures. While it is essentially a destructive analytical method, recent advancements in the ambient ionization technique have enabled us to acquire data while leaving tissue in a relatively intact state in terms of integrity. Probe electrospray ionization (PESI) is a so-called direct method because it does not require complex and time-consuming pretreatment of samples. A fine needle serves as a sample picker, as well as an ionization emitter. Based on the very sharp and fine property of the probe tip, destruction of the samples is minimal, allowing us to acquire the real-time molecular information from living things in situ. Herein, we introduce three applications of PESI-MS technique that will be useful for biomedical research and development. One involves the application to solid tissue, which is the basic application of this technique for the medical diagnosis. As this technique requires only 10 mg of the sample, it may be very useful in the routine clinical settings. The second application is for in vitro medical diagnostics where human blood serum is measured. The ability to measure fluid samples is also valuable in various biological experiments where a sufficient volume of sample for conventional analytical techniques cannot be provided. The third application leans toward the direct application of probe needles in living animals, where we can obtain real-time dynamics of metabolites or drugs in specific organs. In each application, we can infer the molecules that have been detected by MS or use artificial intelligence to obtain a medical diagnosis.

This article introduces sample preparation methods for a unique real-time analytical method based on the ambient mass spectrometry. This method lets us perform real-time analysis of the biological molecules in vivo without any special pretreatments.

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ...

Sample Preparation Strategies for Mass Spectrometry Imaging of 3D Cell Culture Models

Three dimensional cell cultures are attractive models for biological research. They combine the flexibility and cost-effectiveness of cell culture with some of the spatial and molecular complexity of tissue. For example, many cell lines form 3D structures given appropriate in vitro conditions. Colon cancer cell lines form 3D cell culture spheroids, in vitro mimics of avascular tumor nodules. While immunohistochemistry and other classical imaging methods are popular for monitoring the distribution of specific analytes, mass spectrometric imaging examines the distribution of classes of molecules in an unbiased fashion. While MALDI mass spectrometric imaging was originally developed to interrogate samples obtained from humans or animal models, this report describes the analysis of in vitro three dimensional cell cultures, including improvements in sample preparation strategies. Herein is described methods for growth, harvesting, sectioning, washing, and analysis of 3D cell cultures via matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) imaging. Using colon carcinoma 3D cell cultures as a model system, this protocol demonstrates the ability to monitor analytes in an unbiased fashion across the 3D cell culture system with MALDI-MSI.

Immortalized cancer cell lines can be grown as 3D cell cultures, a valuable model for biological research. This protocol describes mass spectrometry imaging of 3D cell cultures, including improvements in the sample preparation platform. The goal of this protocol is to instruct users to prepare 3D cell cultures for mass spectrometry imaging analysis.

Three dimensional cell cultures are attractive models for biological research. They combine the flexibility and cost-effectiveness of cell culture with ...

High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry

New analytical methods are urgently needed to enable high-throughput, yet comprehensive drug screening, given an alarming opioid and prescription drug crisis in public health. Conventional urine drug testing based on a two-tier immunoassay screen followed by a gas chromatography-tandem mass spectrometry (GC-MS/MS) or liquid chromatography-tandem mass spectrometry (LC-MS/MS) method are expensive and prone to bias while being limited to targeted panels of known drugs of abuse (DoA). Herein, we outline an improved method for drug surveillance that allows for the resolution and detection of an expanded panel of DoA and their metabolites when using multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS). Multiplexed separations of ten urine samples with a quality control by CE (&lt; 3 min/sample) in conjunction with full-scan data acquisition using a time-of-flight mass spectrometer (TOF-MS) under positive ion mode detection allows for the identification and quantification of DoA above recommended cut-off levels. An excellent resolution of drug isomers and isobars, including background interferences, are achieved when using MSI-CE-MS with an electrokinetic spacer between sample segments, where accurate mass/molecular formula together with the comigration of a matching deuterated internal standard and the detection of one or more bio-transformed metabolites facilitate DoA identification over a wider detection window. Additionally, urine samples can be analyzed directly without enzyme deconjugation for the rapid screening without complicated sample workup. MSI-CE-MS enables the surveillance of a broad spectrum of DoA that is required for the treatment monitoring of high-risk patients, including confirming prescribed drug adherence, revealing illicit drug use/substitution, and evaluating optimal dosage regimes as required for new advances in precision medicine.

Here we describe a high-throughput method for comprehensive drug surveillance that allows for the improved resolution and detection of large panels of drugs of abuse and their metabolites, with quality control based on multisegment injection-capillary electrophoresis-mass spectrometry.

New analytical methods are urgently needed to enable high-throughput, yet comprehensive drug screening, given an alarming opioid and prescription drug ...

Imaging Corrosion at the Metal-Paint Interface Using Time-of-Flight Secondary Ion Mass Spectrometry

Corrosion developed at the paint and aluminum (Al) metal-paint interface of an aluminum alloy is analyzed using time-of-flight secondary ion mass spectrometry (ToF-SIMS), illustrating that SIMS is a suitable technique to study the chemical distribution at a metal-paint interface. The painted Al alloy coupons are immersed in a salt solution or exposed to air only. SIMS provides chemical mapping and 2D molecular imaging of the interface, allowing direct visualization of the morphology of the corrosion products formed at the metal-paint interface and mapping of the chemical after corrosion occurs. The experimental procedure of this method is presented to provide technical details to facilitate similar research and highlight pitfalls that may be encountered during such experiments.

Time-of-flight secondary ion mass spectrometry is applied to demonstrate the chemical mapping and corrosion morphology at the metal-paint interface of an aluminum alloy after being exposed to a salt solution compared with a specimen exposed to air.

3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry

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