Minimizing damage during FIB sample preparation of soft materials
Bibliographic record
Abstract
Summary Although focused ion beam (FIB) microscopy has been used successfully for milling patterns and creating ultra‐thin electron and soft X‐ray transparent sections of polymers and other soft materials, little has been documented regarding FIB‐induced damage of these materials beyond qualitative evaluations of microstructure. In this study, we sought to identify steps in the FIB preparation process that can cause changes in chemical composition and bonding in soft materials. The impact of various parameters in the FIB‐scanning electron microscope (SEM) sample preparation process, such as final milling voltage, temperature, ion beam overlap and mechanical stability of soft samples, was evaluated using two test‐case materials systems: polyacrylamide, a low melting‐point polymer, and Wyodak lignite coal, a refractory organic material. We evaluated changes in carbon bonding in the samples using X‐ray absorption near‐edge structure spectroscopy (XANES) at the carbon K edge and compared these samples with thin sections that had been prepared mechanically using ultramicrotomy. Minor chemical changes were induced in the coal samples during FIB‐SEM preparation, and little effect was observed by changing ion‐beam parameters. However, polyacrylamide was particularly sensitive to irradiation by the electron beam, which drastically altered the chemistry of the sample, with the primary damage occurring as an increase in the amount of aromatic carbon bonding (C=C). Changes in temperature, final milling voltage and beam overlap led to small improvements in the quality of the specimens. We outline a series of best practices for preparing electron and soft X‐ray transparent samples, with respect to preserving chemical structure and mechanical stability of soft materials using the FIB.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".