What can ToF-SIMS do for wood-polymer composite analysis? A first investigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The potential of time-of-flight secondary ion mass spectrometry (ToF-SIMS) is explored as a unique analytical tool to complement current analyses in wood polymer composites (WPC) research. ToF-SIMS is examined due to its chemical imaging abilities with both high spatial resolution for imaging and high depth resolution going from the surface into the bulk of the material, as well as its low detection limits. The ToF-SIMS method is introduced and preliminary data are discussed, demonstrating ToF-SIMS analyses of commercial WPCs before and after weathering. Controlled weathering exposed samples to rain, ultraviolet radiation, and freeze-thaw cycles, both alone and in combination. The surfaces of the samples were analyzed using ToF-SIMS at five different stages of the weathering process. Topography was also analyzed using scanning electron microscopy and the durability of the samples was measured at the end of weathering using three-point flexural strength testing. Analysis of the ToF-SIMS spectra using multivariate statistical methods demonstrated that ToF-SIMS distinguished samples that underwent various weathering conditions. ToF-SIMS images of WPC samples illustrated the spatial heterogeneity of the chemical components detected, and assisted with understanding changes observed in comparisons of the mass spectra. A depth profile indicated that some of the nitrogen-containing species observed in the spectra of the WPC were isolated to the surface of the sample. Throughout the discussion of this first analysis of WPC with ToF-SIMS, a focus is placed on the opportunities that exist for ToF-SIMS analysis of WPCs, along with the challenges that will need to be overcome for reliable interpretation of future data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it