High-Resolution Imaging and X-Ray Microanalysis of Oxide at Low Energy using Scanning Electron Microscope and Triple Beam FIB Microscope
Bibliographic record
Abstract
Information on the presence and concentration of oxygen has always been of great interest to materials scientists. With the increasing demand for advanced materials like high-temperature materials, wear-resistant materials, ceramics, etc which usually contain light elements like oxygen, boron, etc, high resolution analysis of light elements is becoming more and more necessary[2–4]. Accurate X-ray micro-analysis of light elements (Z<10) can be a challenging task due to various reasons like these light elements have only one X-rays line i.e. Kα line with energy below 0.7 KeV, low yield count, back ground, line overlap, high absorption rate, etc. [2,3,5,6]. For this research Hitachi NX5000 FIB-SEM and STEM SU 9000 equipped with Oxford Ultim Extreme windowless EDS detector was used. SU 9000 is a state-of-the-art SEM with cold field emission gun capable of giving us a resolution of 0.4nm at 30KeV but its greatest strength lies in its ability to operate at low voltages (resolution of 1.2nm at 1KeV). Adding to its capability is the Oxford Ultim Extreme windowless EDS detector whose 100mm2 sensor area provides 5 times more solid angle than conventional EDS detector (shown in fig. 1). This detector is designed to offer high spatial resolution at low voltages (1-3KeV) and thanks to its windowless feature has high sensitivity for light elements at low voltages (2.1x for oxygen). To investigate the oxide layer, a lift-out sample was prepared using Hitachi NX5000 FIB-SEM equipped with a Ga ion FIB gun. The different stages of preparation shown in fig.2. The lift-out sample was then analysed using STEM SU9000 at an accelerating voltage of 2.5 KeV. Fig.3 shows the analysis result. An oxide layer of thickness 20nm at the outer layer of the sample was observed. Through the spectrum collected during EDS mapping a 2D quantification map was created using standardless quantification process and oxygen contraction ranging from 8-40% was found. This level of detection capability will enable us to tackle other issues related to low atomic number elements such as quantification. Solid angle comparison Steps taken during preparation of sample using NX5000 FIB SEM Oxide layer detection and standard less quantification.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".