Control of Cr<sup>6+</sup> Emissions from Gas Metal Arc Welding Using a Silica Precursor as a Shielding Gas Additive
Bibliographic record
Abstract
Hexavalent chromium (Cr(6+)) emitted from welding poses serious health risks to workers exposed to welding fumes. In this study, tetramethylsilane (TMS) was added to shielding gas to control hazardous air pollutants produced during stainless steel welding. The silica precursor acted as an oxidation inhibitor when it decomposed in the high-temperature welding arc, limiting Cr(6+) formation. Additionally, a film of amorphous SiO(2) was deposited on fume particles to insulate them from oxidation. Experiments were conducted following the American Welding Society (AWS) method for fume generation and sampling in an AWS fume hood. The results showed that total shielding gas flow rate impacted the effectiveness of the TMS process. Increasing shielding gas flow rate led to increased reductions in Cr(6+) concentration when TMS was used. When 4.2% of a 30-lpm shielding gas flow was used as TMS carrier gas, Cr(6+) concentration in gas metal arc welding (GMAW) fumes was reduced to below the 2006 Occupational Safety and Health Administration standard (5 μg m(-3)) and the efficiency was >90%. The process also increased fume particle size from a mode size of 20 nm under baseline conditions to 180-300 nm when TMS was added in all shielding gas flow rates tested. SiO(2) particles formed in the process scavenged nanosized fume particles through intercoagulation. Transmission electron microscopy imagery provided visual evidence of an amorphous film of SiO(2) on some fume particles along with the presence of amorphous SiO(2) agglomerates. These results demonstrate the ability of vapor phase silica precursors to increase welding fume particle size and minimize chromium oxidation, thereby preventing the formation of hexavalent chromium.
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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 | 0.000 | 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".