Surface chemistry characterization of AA2014 aluminum alloy powder through triboelectric charging
Bibliographic record
Abstract
Traditional characterization techniques for powders primarily focus on bulk properties, often neglecting the critical role of surface chemistry variations that influence the performance in applications such as additive manufacturing. The method presented in this work addresses this gap by utilizing triboelectric charging concept to gain a comprehensive understanding of powder surface state under varying environmental conditions. In particular, the study investigates the detection of surface chemistry variations of AA2014 powder caused by an exposure to various relative humidity (RH) levels through a change in triboelectric charging behavior. The surface variations are analyzed in parallel with X-ray photoelectron spectroscopy (XPS). The findings reveal a direct correlation between elevated RH and increased hydroxide species content at the surface of the powder. The triboelectric charging experiments demonstrated a significant RH-dependent variations of charge accumulation, with higher humidity levels leading to reduced static charge buildup on the powder particles. The charge accumulation behavior in the powder was fitted with the compressed exponential relaxation model. The results showed that each surface chemical species exhibits a distinct correlation between charging rate and charge accumulation, confirming the method's effectivity to detect subtle variations in surface chemistry. The variations in the exponent of the fitted model were shown to be characteristics to the surface scale of the powder particles. • The paper presents a method to detect surface chemistry variations via triboelectric charging. • Hydroxide species formed on powder surface with the increase in relative humidity levels. • Higher humidity reduces static charge buildup in triboelectric charging tests. • The developed model detects humidity changes on powder surfaces through triboelectric charging. • The method is expected to improve additive manufacturing powder quality control.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".