Threshold Concentration of Surfactant Agent in Feedstocks Used for Low-Pressure Powder Injection Molding
Bibliographic record
Abstract
Abstract In this work, 32 different feedstocks formulated from four different stainless steel powders and two wax-based binder systems (paraffin wax and beeswax), for which the proportion of two different surfactant agents was varied from 0 to 5 vol.% (stearic acid and oleic acid), were obtained. The viscosity of the feedstocks according to the shear rate was measured using a rotational rheometer. The moldability of the mixtures was assessed using the injected length following real-scale injections. The results show that the influence of surfactant agents on viscosity depends on the main binder constituent used in the feedstock formulation. The moldability of paraffin wax–based feedstocks was significantly affected by the proportion of the surfactant agents, whereas that of beeswax-based feedstocks was not affected by the presence of stearic acid or oleic acid. It was confirmed that as little as 0.20 vol.% of stearic acid or oleic acid in paraffin wax–based feedstock is enough to produce the surfactant effect, leading to a significant increase in the moldability of feedstocks (e.g., fourfold longer injected length). This threshold concentration of surfactant agent in paraffin wax–based feedstocks was also established with different powder grades, shapes, and sizes. For feedstocks containing paraffin wax and surfactant agents, the presence of a discontinuity in the viscosity profiles was confirmed not to be an experimental artifact because the phenomenon was reproduced using three different measurement approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".