Measurement of Water Content in Metal Powders
Bibliographic record
Abstract
Abstract It is recognized that humidity affects the properties of metallic powders (e.g., flowability, density, composition). Although standards exist to evaluate the water content in various materials, there is presently no standard method validated or specifically adapted for the evaluation of the water content in metallic powders. This article evaluates the water content in titanium powders using different techniques (gravimetry, Karl Fischer titration, quantification of volatiles with a relative humidity sensor). The effect of measurement conditions, particle size, and types of powder were investigated. The results showed that the adsorption of water is a function of the relative humidity in the environment and the characteristics of the powders. The relative humidity in the laboratory has, however, limited effect if the exposure of the powder to the environment is limited prior to the measurements. Water adsorption/desorption is a function of the surface area of the powder. The measured values were underestimated as the methods used (i.e., heating at temperature lower than 275°C) did not allow recovering all the water from the powders (i.e., chemisorb water is strongly bonded to the surface and cannot be completely desorbed at temperatures lower than 275°C). Although some variability has been observed, the measurement of volatiles with the relative humidity sensor allowed discriminating powders with water contents ranging from 0 to 108 ppm. The amount of water measured using the gravimetric technique was not sensitive enough to precisely monitor the small amount of water adsorb/desorb on the powders investigated in this study. Additional studies are needed to assess the variability and reproducibility of the results and evaluate the techniques on other types of metallic powders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".