Additive Manufacturing of Channels and Surface Features Using Wire-Arc Spray
Bibliographic record
Abstract
Abstract Developing a cost-effective fabrication method for devices containing metal channels with surface features on the submillimeter scale is essential for the development of novel, high efficiency micro-reactors and heat sinks. Traditional methods are limited by their high cost, low geometric accuracy, high energy consumption, and long processing times. This study presents a low-cost additive manufacturing method using twin wire arc spray to make surface features at the sub-millimeter scale. Water-soluble polyvinyl alcohol (PVA) paste is first placed onto a mold containing a negative of the desired surface features and allowed to cure. The cured PVA is removed from the negative and metal sprayed onto its surface. The deposited metal film was backed by epoxy for added rigidity. The PVA paste was then dissolved in a water bath, resulting in a metal surface with the surface features of the mold. Surface features with length scales as small as 200 μm were reproduced. Coating delamination was prevented by minimizing the temperature of the substrate during spraying by increasing the standoff distance and scanning speed of the spray torch.
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) | 0.001 | 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".