Comparison of quantitative mineralogy and sequential leaching for characterization of Ni in workplace dust collected at a stainless steel operation
Bibliographic record
Abstract
Based on epidemiological records of workers at Ni operations, regulatory guidelines commonly target specific Ni compounds for setting exposure limits. Thus, reliable methods of Ni speciation in airborne dust samples are required for effective monitoring of workplace exposure. Zatka sequential leaching has been routinely performed industry-wide since the 1990s for characterization of Ni in dust samples; however, limitations related to leaching kinetics have been identified, and optimization of the methodology is required to improve accuracy of data. In this study, Ni characterization of dust collected from a stainless steel operation was performed using Zatka sequential leaching (original and modified protocols) and quantitative mineralogy (QEMSCAN), a method novel to the field of industrial hygiene. Mineral analysis was also performed on bulk material collected from selected work areas at the plant. The results are compared with the objective of identifying opportunities to optimize the methods for characterizing dust that is unique to stainless steel manufacturing. The quantitative mineralogical analysis determined that the Ni dust is composed of oxidic Ni (chromite and trevorite, >80% of the Ni in most samples) and metallic Ni (Ni-Fe alloy), and the results were validated against chemical assays and alternate methods of mineral characterization. In contrast, the original Zatka method erroneously identified soluble Ni as a major Ni contributor, whereas the modified Zatka method identified sulfidic Ni. The mineralogy identified Ni-barren dust and grain sizes and liberation of individual Ni compounds as potential factors that can affect leaching selectivity. Clearly, for any sequential leaching method to be useful for these workplaces, they should be optimized by including reference materials that are representative of Ni substances present at stainless steel operations (chromite, trevorite, and Ni-Fe alloy). Improving methods of sequential leaching is important because the resolution of quantitative mineralogical techniques diminishes at <3 μm (respirable dust fraction). We recommend that quantitative mineralogy be performed in parallel with methods of sequential leaching to provide a robust system of characterization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".