EDS DAQ - #6
Conversation
Co-authored-by: Ronnie Guymon <244881888+ronnie-guymon@users.noreply.github.com>
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@claude I want to be able to process the data from EDS in a way where I can do the mapping and elemental composition in a software other than TEAM. What is all the information that can be exported directly from the microscope (x-ray incidents, parameters, dead time, etc)? How is post processing typically done in research settings, what data is exported, what software is used? |
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Claude finished @ronnie-guymon's task in 2m 27s —— View job Todo
Short answerYou can get everything you need out of TEAM to do mapping and quantification elsewhere. The key export is the spectrum-image datacube ( 1. What TEAM can export directly
Practical notes:
2. How research groups typically post-processThe dominant open-source stack is Python:
A common division of labor: HyperSpy for maps and phase decomposition (spatial questions), DTSA-II for point-spectrum composition (accuracy questions). 3. Suggested recipe for this projectimport hyperspy.api as hs
s = hs.load("mymap.spd") # picks up mymap.spc / mymap.ipr automatically
s.set_elements(["Al", "Si", "Mg", "Fe"])
maps = s.get_lines_intensity() # background-corrected intensity maps
s.decomposition(True) # PCA (denoise / pick n components)
s.decomposition(algorithm="NMF", output_dimension=3) # phase unmixing
s.sum().save("sum_spectrum.msa") # → DTSA-II for standards-based quantThe Happy to add a Sources: RosettaSciIO EDAX format docs · HyperSpy EDS user guide · eXSpy · NIST DTSA-II · Multivariate analysis of SEM/EDS phase maps · ML quantification of SEM-EDX · EDAX APEX · HyperSpy EDAX import discussion |
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@claude do those programs show the wt% of elements accurately? Is it possible to strip a peak based on an energy reading? for example if I wanted to analyze a small Mg peak that was placed next to a large Al peak, how possible and accurate would it be? |
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Claude finished @ronnie-guymon's task in 2m 0s —— View job Todo
Short answerYes on both counts, with caveats. Accurate wt% depends far more on how you quantify (standards-based vs. standardless) than on which program you use — DTSA-II with standards is demonstrably accurate; standardless (what TEAM gives you by default) is not. And yes, overlapping peaks can be separated, but the accurate way is peak fitting/deconvolution, not literal energy-window stripping. Your specific case — small Mg Kα (1.254 keV) next to large Al Kα (1.487 keV) — is a mild overlap by EDS standards and is very tractable if you collect enough counts. 1. Do these programs give accurate wt%?It depends on the quantification protocol, not the software brand:
Accuracy also presumes the physics assumptions hold: flat, polished sample; known geometry; adequate counts. Rough surfaces break ZAF corrections regardless of software. 2. Can you strip a peak based on energy?You can (background-strip then subtract a scaled reference peak at a given energy), but simple energy-window ROI stripping is exactly the method that fails for a small peak beside a big one — the big peak's tail leaks into the small peak's window and biases it. What research software actually does, and what you want, is simultaneous fitting:
3. Your specific case: small Mg next to large AlThe numbers are favorable:
Bottom line: export the Sources: Newbury & Ritchie, high-accuracy SEM/SDD-EDS (PMC4555346) · NIST DTSA-II · HyperSpy EDS user guide · SEM-EDS spectral overlaps & deconvolution · EDS deconvolution of overlapping peaks (M&M 2023) · PyMCA |
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@claude I am really interested in learning about how to create standards for EDS. If I were to create my own standards for a novel powder, that has been 3d printed via LPBF, how would I go about that? Use information based on what professionals do |
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Claude finished @ronnie-guymon's task in 2m 20s —— View job Todo
Short answerAn EDS "standard" is just a flat-polished, microscopically homogeneous piece of material whose composition is known from an independent (non-EDS) technique. Professionals rarely make a standard shaped like the unknown — you would not use loose powder or as-built LPBF material as the standard itself, because as-built LPBF violates all three requirements (porosity, Mg evaporation loss, sub-µm Si segregation). Instead the professional route is: (1) use purchased pure-element standards for the k-ratio protocol, and (2) if you want a matrix-matched check standard from your novel alloy, consolidate it into dense homogeneous bulk (remelt or HIP + homogenization anneal), have its bulk composition certified by ICP-OES, then qualify its micro-homogeneity yourself with a statistical grid of analyses. 1. What makes something a "standard" (professional criteria)From EPMA practice (the discipline EDS quantification inherits from):
2. First: you may not need to make anythingFor AlSi10Mg-type chemistry, the Newbury–Ritchie protocol needs only pure-element or simple compound standards: pure Al, pure Si, pure Mg, pure Fe, etc. Pure-element spectra also serve as the fitting references for the Mg/Al overlap discussed earlier. These are bought as polished multi-material mounts from Astimex, Geller MicroAnalytical (ISO-17025, NIST/NPL-traceable), or SPI; free/loaner options include NIST SRM glasses, Smithsonian microbeam standards, and USGS reference materials (directory at FIGMAS; a university probe lab's block, e.g. UW–Madison's, shows what a working set looks like). One standard block (~$1–3k) + DTSA-II gets you the documented ±3%-relative accuracy tier (Standard Bundles in DTSA-II). A custom standard from your own alloy is then a validation/QC material, not a prerequisite. 3. Making a custom standard from your novel powder — the professional workflowStep 0 — decide what material state to certify. The powder and the printed part have different compositions: Mg (boiling point 1091 °C) measurably evaporates in the LPBF melt pool, so parts run Mg-lean relative to feedstock (micropore/post-treatment study noting Mg/Cu loss and Si enrichment). If your goal is checking printed-part analyses, certify material made from a printed coupon; certify the powder separately (by ICP) if you want to track feedstock→part drift — that drift is itself a CALIBER-relevant measurement. Step 1 — consolidate to dense, homogeneous bulk. Loose powder can't be a standard (particle geometry breaks the flat-surface assumption), and as-built LPBF material has gas/keyhole porosity and a sub-µm cellular Si network — heterogeneous right at the ~1 µm interaction-volume scale. Two professional routes:
Step 2 — independent bulk certification. Send pieces of the same billet to a commercial or university lab for ICP-OES after acid digestion (metals) and combustion/inert-gas-fusion analysis for O/N/C if relevant (alloy chemistry analysis; technique comparison for commercial alloys). Ideally two labs or two techniques (ICP + XRF) — agreement is your certification; NIST-grade CRMs go further with round-robins, overkill for an in-house working standard. Step 3 — mount, polish, coat. Standard metallographic prep: flat polish to ≤1 µm finish, no etching, clean, carbon-coat identically to your unknowns (NIST prep guidance). Step 4 — qualify micro-homogeneity statistically. This is the step amateurs skip and professionals don't: run a grid (e.g. 10×10 points across the mount, plus several fields at different depths of a sectioned piece) at high counts, and require the observed relative standard deviation of each element's intensity to approach the counting-statistics limit — excess scatter = heterogeneity, and the number of points sets how small a heterogeneity you can rule out (EPMA homogeneity-testing methodology). You can do this entirely with your own EDS + the exported spectra. Step 5 — document and maintain. Record certified values + uncertainties, prep/coating details, and the homogeneity data; store desiccated; re-polish/re-coat and spot-check periodically. Load the spectra into DTSA-II as a standard bundle so quantification picks them up automatically (Standard Bundles paper). 4. How the pieces fit for CALIBER
Cost/effort reality check: purchased block ≈ order-and-go; custom billet ≈ one melt/HIP run + ~$100–300 of commercial ICP + an afternoon of homogeneity mapping. That combination is essentially what a professional microprobe lab would set up for a new alloy system. Sources: Homogeneity testing of microanalytical reference materials (Talanta) · ISO 14595 CRMs for EPMA · NIST sample prep for EPMA · Standards-Based Quantification in DTSA-II, Part I · NIST Standard Bundles for DTSA-II · NIST DTSA-II · Astimex · Geller MicroAnalytical · FIGMAS standards directory · UW–Madison standards block · Mg loss / porosity in LPBF AlSi10Mg · Alloy chemistry techniques comparison · SGS alloy chemistry analysis |
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@claude help me understand step 4 better. When you say 10x10 points across the mount, would that mean methodically analyzing 100 different points while I'm at the microscope? Like focusing on 100 different points and collecting data from each of them? |
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Claude finished @ronnie-guymon's task in 51s —— View job Todo
Short answerConceptually yes — it's 100 separate spectrum acquisitions at 100 locations. But in practice, no, you don't sit there manually focusing and clicking 100 times. You focus once, define the grid, and let the software drive the stage/beam unattended. It's roughly an hour of hands-off acquisition, and there's an even lazier route: just collect maps and slice them into "points" afterward in software. What one "point" actually isA point analysis means the beam is parked at a stationary spot (or rastered over a tiny box) while the detector accumulates a spectrum for a fixed live time — say 30–60 s at moderate dead time, enough for a few hundred thousand counts. No per-point focusing is involved: the mount is flat-polished and the stage stays at one working distance, so focus set once at the start holds across the whole grid. (This is exactly why the flat-polish requirement in step 3 matters — it's what makes the grid comparable point-to-point.) How the 100 points actually get collectedThree ways, in decreasing order of effort:
The reason the grid is spread "across the mount" (and ideally on a sectioned face too) is to sample different length scales: point-to-point scatter within a field catches µm-scale segregation, field-to-field scatter across millimeters catches macrosegregation from the melt/anneal. What you do with the 100 spectraFor each element, extract the peak intensity from every spectrum and compute the observed relative standard deviation across the 100 values. Compare it to the counting-statistics limit (≈ 1/√N for N peak counts): if the observed scatter matches what Poisson statistics alone predicts, the material is homogeneous at your measurement precision; excess scatter = real compositional variation. More counts per point makes the test more sensitive — with ~10⁶ counts per point you can detect inhomogeneity below ~1% relative. This is the statistical protocol in the microanalytical reference-material homogeneity paper from the earlier reply, and the whole computation can be done from exported Bottom line: it's one focusing session plus an hour of automated acquisition (or a couple of maps you slice up afterward), followed by a straightforward statistics pass on the exported spectra — not 100 manual point-and-shoot measurements. --- · Branch |
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@claude I have a meeting with Mike today to get trained for EDS using their EDAX system. I want to use eXSpy. What data should I ask to collect in order to use eXSpy, and what other questions should I ask that would help in developing a process to accurately characterize Mg wt%? |
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Claude finished @ronnie-guymon's task in 1m 43s —— View job Todo
Short answerFor eXSpy you want three things out of the session: (1) the 1. Data to ask to collect (the eXSpy checklist)
Metadata to verify is present in the exports (open the
Same-day sanity check (do this before leaving or that evening — catching a broken export while you still have scope access is the whole point): import hyperspy.api as hs
s = hs.load("map.spd") # auto-finds map.spc / map.ipr
print(s.metadata) # verify beam_energy, live_time, elevation/azimuth
s.set_elements(["Al", "Si", "Mg", "Fe"])2. Questions to ask Mike, aimed at accurate Mg wt%Detector & DAQ (these map directly onto
Calibration & standards (the accuracy questions):
Geometry & sample prep:
Export & automation logistics:
3. Suggested first-session acquisition plan
That single session gives you everything needed to prototype the full offline pipeline: model-fit the Mg/Al region in eXSpy using the pure-element peak positions, and later feed the same Sources: RosettaSciIO EDAX format docs · eXSpy EDS user guide · NIST DTSA-II · Newbury & Ritchie 2015 (PMC4555346) --- · Branch |
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@claude Our time was limited during the training yesterday, but I found that we can export the .msa file and the TIFF file (I can take notes for the parameters that aren't recorded). The counts can be saved as a .xls or .csv file, I didn't see anything that is .spd, .spc, or .ipr. I can ask Mike about that in an email. If amp time is the same as processing time, 7.68 is the longest amp time that was available. We can do 5 or 10 kV, From what I understand we want the overvoltage to be at least double the energy of the K-alpha line, and since Si has the largest K-alpha line of about 1.74, 5kV should be enough. He said there wasn't really a downside to using more overvoltage than needed. He recommended a dead time of around .30, and that is done by increasing or decreasing the beam current. Energy calibration is done weekly. There is point analysis where I think Team does multiple point analysis, but we only had half the normal time to get through our training so I'll have to talk to him another time to figure that out. |
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Claude finished @ronnie-guymon's task in 3m 9s —— View job Todo
Short answerGood session — most of what you learned is right, and 1. Amp time = process time — yes, use 7.68 µsThey're the same knob (EDAX calls it amp time). 7.68 µs is the longest option on EDAX pulse processors, i.e. the best energy resolution setting — exactly what you want for pulling the small Mg Kα out from under the Al Kα tail. The trade-off is a low throughput ceiling: at 7.68 µs you'll hit 30% dead time at a fairly modest beam current. That's fine — you're choosing resolution over speed deliberately. Just compensate with longer live time per spectrum (aim for ≥10⁶ total counts on quant spectra). 2. Overvoltage — the rule, corrected, and the real downsides
3. Dead time ~30% via beam current — agreed, with two notes0.30 (30%) matches the 20–30% guidance from earlier in this thread; adjusting it via beam current is standard. Two additions: (1) since long amp time caps throughput, expect the current that gives 30% DT to be low — that's fine; (2) for standards-based k-ratios later, the beam current must be the same (or measured) between standard and unknown — so once you find a current that gives ~30% DT, log it (or the spot-size/aperture settings that produced it) and reuse it, and ask whether there's a Faraday cup to actually measure it. 4. Exports: what you found is enough — and where the .spd trio hides
5. Weekly energy calibration — fine, with a session-start habitWeekly is a reasonable cadence. Still worth a 30-second sanity check each session: acquire a quick spectrum from anything with known lines (your Al alloy works — Al Kα should sit at 1.487 keV) and confirm peak centroids land within a few eV. At 5 kV with the Mg/Al deconvolution, a small calibration drift shifts fitted peak positions and quietly biases the fit, so this cheap check protects your most sensitive measurement. Suggested settings card for your next session
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@claude is there a way to strip the Al peak at all using eXSpy? What eXSpy parameters or other options are there to get a good Mg qunatification? show me a eXSpy script of what you would do to find the Mg wt%. I don't have any real data for you rn, but I can get some during an EDS session to give later to try out that script. |
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Claude finished @ronnie-guymon's task in 3m 41s —— View job Todo
Short answerYes — in eXSpy you "strip" the Al peak by fitting a model (one Gaussian per X-ray line + background) and subtracting the fitted Al components from the spectrum. But the important subtlety: the stripped spectrum is for your eyes; the Mg number comes from the fit itself. Once the model is fit, 1. How "stripping" works in eXSpy
2. What actually buys you Mg accuracy in eXSpy
3. The scriptWorks on point spectra ( import hyperspy.api as hs
# ---------- Part A: fit the sample and strip the Al peaks ----------
s = hs.load("sample_AlSi10Mg.msa", signal_type="EDS_SEM")
# kV and live_time usually come from the .msa header — patch gaps from your notes
s.set_microscope_parameters(
beam_energy=5.0, # kV (only needed if absent from header)
elevation_angle=35.0, # take-off angle — ask Mike for the real value
azimuth_angle=0.0,
)
s.add_elements(["Mg", "Al", "Si"]) # everything that emits in the fit window
s.add_lines() # selects Ka lines; Kb handled as tied sub-lines
m = s.create_model() # one Gaussian per line + background
m.set_signal_range(0.9, 2.5) # keV — the Mg/Al/Si region
m.fit()
m.fit_background() # refine background on line-free channels
m.fit()
m.plot(plot_components=True) # inspect fit + residual before trusting anything
# The "strip": subtract the fitted Al components (Al_Ka + Al_Kb) from the data
al_components = [c for c in m if c.name.startswith("Al")]
s_stripped = s - m.as_signal(component_list=al_components,
out_of_range_to_nan=False)
s_stripped.plot() # Mg Ka now sits on the bare background
# Overlap-corrected Mg intensity — comes from the FIT, not the stripped spectrum
i_mg = m.get_lines_intensity(["Mg_Ka"])[0].data
# ---------- Part B: k-ratio against a pure Mg standard ----------
std = hs.load("pure_Mg.msa", signal_type="EDS_SEM")
std.add_elements(["Mg"])
std.add_lines()
ms = std.create_model()
ms.set_signal_range(0.9, 2.5)
ms.fit()
ms.fit_background()
ms.fit()
i_mg_std = ms.get_lines_intensity(["Mg_Ka"])[0].data
# Dose normalization: counts / (live time × beam current).
# Live time is in the .msa header; beam current is from your notebook —
# if it was identical for sample and standard, it cancels out.
lt_s = s.metadata.Acquisition_instrument.SEM.Detector.EDS.live_time
lt_std = std.metadata.Acquisition_instrument.SEM.Detector.EDS.live_time
ib_s = ib_std = 1.0 # nA — fill in measured values if they differ
k_mg = (i_mg / (lt_s * ib_s)) / (i_mg_std / (lt_std * ib_std))
print(f"Mg k-ratio: {float(k_mg):.4f}")
print(f"Mg wt% (first-order, no matrix correction): {float(k_mg) * 100:.2f}")4. From k-ratio to real wt%
So the division of labor stands: eXSpy = fitting, stripping, maps, k-ratios; DTSA-II = the wt% number you'd defend. The script's printed k-ratio is also your session-to-session QC metric — on a homogeneous sample it should reproduce to within counting statistics. 5. When you have dataBring back: the AlSi10Mg Sources: eXSpy EDS user guide · eXSpy signals API (EDSSEMSpectrum vs EDSTEMSpectrum) · HyperSpy model fitting guide · NIST DTSA-II · Newbury & Ritchie 2015 (PMC4555346) --- · Branch |
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@claude I'm pretty sure I get k-ratios from team as well, I'm going to want to get the Team-generated k ratios and compare them to expsy's. Eventually, I will want to be able to get all the data/info I get from Team from either exspy or another open source method so it can be used in CALIBER. If I'm understanding right, the info (such as k-ratio, etc) generated from exspy is what will be used to do the wt% analysis in DTSA-II? From a CALIBER standpoint, I would mess around with the exspy script, changing parameters in order to get what I needed for a reliable wt% from DTSA-II? How would I know once I have the most optimal parameters before moving over the DTSA-II? If there is no way, then this could potentially be part of the feedback loop |
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Claude finished @ronnie-guymon's task in 2m 28s —— View job Todo
Short answerYour mental model is close, but one correction changes the workflow: you don't feed eXSpy's k-ratios into DTSA-II. DTSA-II takes the raw 1. The corrected pipelineSo "mess with the eXSpy script until DTSA-II gives a reliable wt%" isn't quite the coupling — the two fits are independent. What you tune the eXSpy script for is (a) agreement with DTSA-II's k-ratios on the same spectra, and (b) the map/automation work DTSA-II can't do. If you do want an all-open-source k-ratio→wt% path without DTSA-II's GUI: DTSA-II is fully scriptable in Jython (headless — it can sit inside a CALIBER pipeline as a batch step), and CalcZAF (MIT-licensed, John Donovan's EPMA utility) converts k-ratios to compositions with a choice of φ(ρz) models. Between eXSpy (Python), DTSA-II scripting, and CalcZAF, everything TEAM produces is reproducible open-source — TEAM ends up being only the acquisition/export front-end, which is the CALIBER end-state you described. 2. Comparing TEAM's k-ratios to eXSpy's — do it, with one caveatThe caveat: TEAM's eZAF (standardless) "k-ratios" are computed against factory-stored theoretical reference intensities, while your eXSpy k-ratio is against a pure-element spectrum you measured that session at the same current. Those are different denominators, so expect a systematic offset between the two — that alone doesn't mean either fit is wrong. What should agree tightly is: relative trends across samples, reproducibility scatter, and (if TEAM offers a standards-based quant mode using your measured standards) the k-ratios themselves. The genuinely apples-to-apples comparison is eXSpy vs. DTSA-II on the identical 3. How you know the eXSpy parameters are converged (before DTSA-II)You can't optimize against the unknown's true Mg content — you don't know it. But four tests need no ground truth:
The fifth test needs truth and is the decisive one: run a known material (a purchased certified standard, or later your certified in-house billet) through the full chain — eXSpy fit → DTSA-II wt% — and check recovery within the expected tier (±5–10% relative for minor elements). Pass tests 1–3 and the eXSpy side is done: any remaining wt% error lives in acquisition, standards, or the matrix correction — not in the fit. 4. Yes — this is the feedback loopWhat you've described is exactly a closed loop, and the "no way to know" worry dissolves once the loop's metric is named: Every box except TEAM is scriptable Python/Jython, so the whole loop can run unattended on exported files — which is precisely why getting raw Sources: NIST DTSA-II · CalcZAF (open-source, MIT) · eXSpy EDS user guide · Standards-Based Quantification in DTSA-II · Newbury & Ritchie 2015 (PMC4555346) --- · Branch |
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@claude so does exspy not use ZAF correction at all, just the raw k-ratio? And DTSA-II does, so it gives a more accurate wt%? Or is calcZAF the only one that uses those corrections? And if I understand correctly, exspy is more for compositional mapping while DTSA-II gives me a more accurate weight%? |
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Claude finished @ronnie-guymon's task in 58s —— View job Todo
Short answerAlmost exactly right, with one refinement. For bulk SEM samples: eXSpy applies no matrix correction — it stops at fitted intensities and k-ratios. DTSA-II applies the full φ(ρz)/ZAF correction, which is why its wt% is the accurate one. CalcZAF is not the only corrector — it's an alternative to DTSA-II's correction step, useful if you already have k-ratios and just want them converted to wt%. And yes: eXSpy = fitting + mapping engine, DTSA-II = the defendable wt%. 1. What each tool actually does
One clarification on eXSpy so the docs don't confuse you later: eXSpy does have a 2. Why DTSA-II's wt% is more accurateTwo independent reasons, and it's worth keeping them separate:
So "DTSA-II is more accurate" is true, but not because of some proprietary magic — it's because it performs both steps of the physics that a raw k-ratio omits. 3. Where CalcZAF fitsCalcZAF does only the last step: k-ratio in → matrix correction → wt% out. It exists so you can bring your own k-ratios (e.g. from your eXSpy fit) and still get a properly corrected composition without re-fitting in DTSA-II. That makes it the piece that would let a fully eXSpy-based pipeline produce corrected wt% — including, in principle, per-pixel: eXSpy k-ratio maps → CalcZAF (or scripted DTSA-II) correction → wt% maps. DTSA-II and CalcZAF use the same class of φ(ρz) models, so they're alternatives for the correction step, not a hierarchy. 4. So your mental model, refined
Practical consequence for CALIBER: the three-way comparison from the last reply stays as planned (TEAM vs. eXSpy vs. DTSA-II k-ratios), and when you eventually want wt% maps rather than point values, the route is eXSpy per-pixel k-ratios + a scripted correction (CalcZAF or headless DTSA-II/Jython) — that combination reproduces everything TEAM does, open-source. Sources: eXSpy EDS user guide · NIST DTSA-II · Standards-Based Quantification in DTSA-II · CalcZAF · Newbury & Ritchie 2015 (PMC4555346) · Branch |
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I need to get some raw data to try out DTSA-II and eXSpy/CalcZAF. @gage-erickson is there a time this week when you could come? I was thinking Thursday around 12:00 or 1:00. |
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I am available Wednesday. Would a time tomorrow work? |
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Sadly I won't be able to today, are you free at all next week? I'm free all day the 19th-21st (Wed-Fri) |
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@claude I think I will just do eXSpy for the elemental per-pixel map of the sample, and DTSA-II for the sample wt%. It seems like that is more accurate and less complicated that using CalcZAF. Is that right? Is there any reason I should use CalcZAF over DSTA-II? From Edison scientific, find what the best use case is for each of these three and their strengths, weaknesses, and computing/other costs |
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Claude finished @ronnie-guymon's task in 31m 2s —— View job Todo
Short answerYes — your plan is right, and the Edison Scientific literature review independently landed on the same split: eXSpy for per-pixel elemental maps, DTSA-II for the bulk-sample wt%. CalcZAF is not a competitor to DTSA-II for your main workflow; it's a specialized add-on you'd only reach for in three specific situations (below). For getting a defendable Mg wt% number from point spectra, DTSA-II alone is both more accurate and simpler — it does peak fitting + matrix correction in one validated package, whereas CalcZAF does only the correction step and would force you to supply k-ratios from somewhere else. The full Edison report (with citations and the raw research trajectory) is committed to the branch: answer.md, plus references.txt, contexts.json, and task_full.json. When CalcZAF would beat DTSA-II (the only three reasons)
None of these are needed for your first goal (accurate point-spectrum Mg wt%), so skipping CalcZAF for now is the right call. Note its practical downsides from the review: Windows-only, GUI-centric with limited batch capability, and it does no peak fitting — it can never stand alone. Edison's tool-by-tool comparison
Two findings worth highlighting for your Mg problem specifically: the review confirms DTSA-II's ±5% relative tier covers minor constituents like your 0.3–0.5 wt% Mg (Newbury & Ritchie 2019/2024), and it flags the MAC-database sensitivity of Mg Kα as the one place a CalcZAF cross-check adds information DTSA-II won't give you. Bottom line for your workflowProceed exactly as you planned: eXSpy for the Key commands run: pip install edison-client # v0.16.1, installed cleanly
# submitted LITERATURE_HIGH task b81eeb1f-741e-475a-92d0-5349f057ec0a; polled every 4 min → success in ~28 min
git commit 5505f54 # Edison artifacts committed to outputs/edison_eds_tools_comparison/Sources (from the Edison report): Newbury & Ritchie 2024, low-beam-energy EDS accuracy · Newbury & Ritchie 2019, EDS at 50 · Goldstein et al., SEM/EDS k-ratio procedure · Goldstein et al., Compositional Mapping · Rickerby 1995, φ(ρz) model comparison · Korolyuk et al. 2009, correction-model accuracy --- · Branch |
Co-authored-by: Ronnie Guymon <244881888+ronnie-guymon@users.noreply.github.com> Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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I can do Wednesday the 19th |
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We have an EDS session scheduled for Wednesday the 19th at 11:30am, after which we will spend time using claude and getting familiar with the software. |
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@claude what are all the files of raw data I should try and get from my EDS session from team so I can use eXSpy, calZAF, and DTSA-II |
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Claude finished @ronnie-guymon's task in 54s —— View job Todo
Short answerThree file families cover all three programs: Session export checklist (in priority order)
The notebook is a "file" tooTEAM doesn't record everything the quant chain needs. Write down, per acquisition:
TEAM-specific gotchas (from your training session)
Who consumes what (so nothing gets over-collected)
Bottom line for today at 11:30: come home with the calibration-check --- · Branch |
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@claude how big should the .msa files be that will be exported and used for everything? voltage of 5kv (over double the energy of SI kalpha), 3.2 nA beam current, dead time of 28-29, Input CPS of ~21500, amp time 7.68, working distance of 10.1, mag of 6120x |
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Claude finished @ronnie-guymon's task in 1m 5s —— View job Todo
Short answerThe file size of an The number that actually matters: counts → live timeAt your input rate of ~21,500 CPS:
(Stored counts ≈ input CPS × live time; real time ≈ live time ÷ (1 − dead-time fraction).) Practical recipe for today:
Two sanity checks while you're at the scope:
For contrast: the --- · Branch |
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@claude I'm trying to figure out how to export the files we need, how do I even navigate there? it is kinda confusing to navigate and the microscopy advisors are not currrently here |
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Claude finished @ronnie-guymon's task in 2m 58s —— View job Todo
Short answerTEAM's mental model is the key to not getting lost: everything you collect is auto-saved into the project tree (the "Project Content" tab on the side of the window), and all exporting is done by double-clicking an item in that tree to display it, then using small icons that only appear when you hover over the displayed spectrum/image. There is no central "File → Export" menu — that's why it feels unnavigable. Below is the click-path for each file type, based on a detailed university operating guide for TEAM (UF Tescan MIRA3 + EDAX TEAM procedure). 1. Point spectra (.msa and .csv)
2. Reference images (TIFF)Double-click the analysis area in the project tree so the SEM image displays, then hover over the image → click the folder icon (top-left corner of the image). That saves the image (with analysis spots/regions marked) to your designated folder. 3. Where "Send to Folder" actually puts thingsExports go to your designated user folder, set when your TEAM user/project was created — not somewhere you choose per-export. If you don't know where it points, don't hunt through menus: after exporting one spectrum, open Windows File Explorer on the TEAM PC and search the C: and D: drives for files modified in the last hour (sort by "Date modified"), or just search 4. The map trio (.spd / .spc / .ipr)These are not behind any export button — they're written into TEAM's project data directory on disk when you collect a map (RosettaSciIO EDAX docs). The reliable way to find them without an advisor:
5. End-of-session checklist (5 minutes before you leave)
Your earlier question: how big should the .msa files be?Small — roughly 50–200 KB each. An .msa is plain ASCII: a few dozen header lines plus one line per energy channel (typically 2048–4096 channels). So don't judge acquisition quality by file size; every .msa is about the same size regardless of counts. Quality is the total counts number in TEAM (or the Sources: UF EDS-with-EDAX-TEAM operating procedure · RosettaSciIO EDAX format docs · EDAX Genesis utilities manual (MSA export heritage) --- · Branch |
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We also noticed a large amount of what we think is charging, like a crazy amount compared to what we had previously seen. It was like a box made of dark lines was carved around the area we were sampling, I assumed it was from charging by scanning multiple times over an area with a high beam current over light elements. We will upload an image of that here too. @claude any ideas of what could be causing it? |
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Claude finished @ronnie-guymon's task in 1m 21s —— View job Todo
Short answerAlmost certainly not charging — that dark box "carved" around the scanned area is the classic signature of electron-beam-induced carbon contamination (hydrocarbon deposition). The beam cracks residual hydrocarbons on the surface and in the chamber into amorphous carbon wherever it dwells, so the rastered rectangle gets a carbon layer — and the edges of the scanned region grow the thickest deposit, because hydrocarbons migrate along the surface toward the irradiated zone and get cracked right at the border before they can diffuse in. That's why it looks like a box made of dark lines outlining your map area, rather than a uniformly dark patch. This edge-amplified frame is well documented (EBICD study — edge thicker than center via surface diffusion; NIST quantification & mitigation paper, M&M 2023). Why it's this and not charging
Where the hydrocarbons come from
Why it matters for your Mg quantification (not just cosmetics)The carbon layer grows during acquisition on exactly the spot you're quantifying, and at 5 kV your analysis lines are soft: Mg Kα (1.25 keV) and Al Kα (1.49 keV) are measurably absorbed by even a thin carbon film, and the beam loses energy passing through it. Practically: the Mg signal slowly drops during a long acquisition, the C Kα peak grows, and the k-ratio gets biased low — worst for the longest acquisitions, which are precisely your high-count quant spectra. A growing C peak between the start and end of a long acquisition is your tell-tale diagnostic. What to do about it
One caveat to keep honest: if the image shows bright streaking, sample drift, or contrast that flickered while scanning, some genuine charging could be stacked on top (e.g. if the part sits on oxide or the mount path to ground is poor through tape). Upload the image and I can say which signatures are present — but a stable dark box outline around the scan area is contamination, not charge. Sources: Electron-beam-induced carbon deposition — edge-enhanced growth via surface diffusion (ScienceDirect) · Electron-Beam-Induced Carbon Contamination: Quantification and Mitigation (Microscopy & Microanalysis, NIST) · Mitigating e-beam-induced hydrocarbon deposition (JVST B) |
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@claude that makes sense. I think the biggest thing we will make sure to do is to not increase the beam current or zoom in on our spot until right as we are about to do our analysis and mapping, but we can always look into more extreme methods if that starts to cause too much carbon deposition. I'm pretty sure the last part of our polishing SOP already includes the ultrasonic IPA cleaning. |
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Claude finished @ronnie-guymon's task in 46s —— View job Todo
AssessmentYour plan is sound, and having ultrasonic IPA as the last polishing step already removes the biggest hydrocarbon source. Three refinements to make it work in practice:
Net: your two levers (clean sample + minimal pre-dwell) are the standard first-line defense and are probably enough for a conductive metal at your currents. The C Kα start/end comparison tells you objectively whether to escalate — and it comes free out of the --- · Branch |
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Here's the image of the carbon deposition visible as rectangles on the sample where we were zoomed in to around 6000-7000x with 3.2 nA 
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2 participants
Adds
docs/eds_daq.mdexplaining the EDS data-acquisition chain — SDD detector, digital pulse processing (fast/slow channels), process time, dead time, pile-up rejection, MCA binning, and counting statistics — and how each stage determines the acquisition parameters we set.Addresses issue #1.
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