EDX Elemental Mapping of Trace Amounts of Ir on the Surface of Pt Cubic Nanoparticles for Ammonia Electro-Oxidation
Bibliographic record
Abstract
Advances in the field of nanomaterials necessitate corresponding advancements in elemental analysis. Energy dispersive spectroscopy (EDS) performed in scanning electron microscopes is a critical technique that enables determination of spatial compositional distributions. Elements such as Pt and Ir find frequent application in fuel cell technology [1], and understanding their functioning requires knowledge of their elemental spatial distribution. However, when these elements are used together, with trace amounts of one, element mapping is exceptionally challenging with EDS. This is due to their dominant M emission peaks being only 70 eV apart, whereas the typical best energy resolution for EDS is 120 eV. Ir and Pt L-edges have a greater separation of nearer 270 eV, but their intensities have only 10% of the M-edges. To minimize the electron dose received by the sample and the acquisition time, we show that it is possible to distinguish Pt-Ir concentrations by analyzing the base regions of the combined Pt/Ir Mα-β peaks, along with observing the presence of much weaker signature of the M3N4 peak in the 2.2 to 2.3 keV range. From our calibration data, this peak appears stronger in Ir than in Pt. These differences appear to not be reliably determined using common EDS data analysis software [2]. We show this by creating synthetic EDS spectra composed from calibration spectra from isolated Pt and Ir samples in the same scanning electron microscope, at the same beam conditions, as in Figure 1(a). Comparing these synthetic model spectra to peak normalized spectra collected from the inner-most and outer-most regions of Pt/Ir nanoparticles (Figure 1(c)), shows that the Ir concentration increases towards the outermost edges Figure 1(b). The presence of Ir is further corroborated by electrochemical studies on the ammonia electro-oxidation reaction (AOR), results of which are shown in Figure 2. These measurements reveal characteristic properties of a Pt-Ir alloy, such as a negative shift in overpotential and increased resilience to poisoning during the AOR, even with small quantities of Ir deposited on the surface of cubic Pt(100) nanoparticles [3, 4]. Having access to information on the spatial distribution of Ir on and within Pt by this relatively simple analysis technique, provides access to important information to further optimize the process of creating Pt/Ir nanoparticles and understand their surface chemistry. (a) Synthetic peak normalized EDS spectra in the region of Mα/β peak for Pt-Ir of various concentrations, created by summing calibration spectra. (b) Peak normalized sum spectra from the inner-most (‘core’) and outermost regions (‘shell’). (c) BF-STEM image collected at 20 kV in the SU9000 SEM/STEM of a collection of Pt-Ir nanoparticles, overlaid with color masks indicating the inner- most and outer- most regions used to produce the sum spectra shown in part (b). Electrochemical measurements illustrating the distinction between Pt nanoparticles and Pt-Ir nanoparticles. Left: cyclic voltammetry in 1.0 NH4, 1.0 M NaOH, scan rate of 50 mV/s. Right: potentiostatic measurements at 0.25 V vs SCE, 1.0 M NH4, 1.0 M NaOH.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".