Bibliographic record
Abstract
Aluminum and zinc were deposited using a twin-wire arc thermal spray torch onto smooth (Ra ~0.20 µm) and rough (Ra ~ 1.60 µm) samples of polytetrafluoroethylene (PTFE/Teflon®) and ultra-high molecular weight polyethylene (UHMW PE/ HDPE). Aluminum coatings did not adhere to the HDPE samples, however coatings roughly 300 to 400 µm thick were obtained on PTFE. Zinc adhered well to both surfaces. Adhesion tests and SEM imaging were performed to determine the strength and adhesion mechanism of the coatings. Increasing surface roughness enhanced coating adhesion strength. Additionally, deposition onto polymer substrates that were heated close to their glass-transition/softening temperatures resulted in increased adhesion strengths. SEM imaging suggested that mechanical interlocking increased with the metal onto a rough surface. Heating PTFE resulted in softening, increasing Interlocking, and the PE surface, with lower softening temperature, eroded by the impact of hot aluminum particles which removed the surface roughness and prevented adhesion.
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.
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) | 0.005 | 0.001 |
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".