Ultrasonic dissolution for ICP-MS determination of trace elements in lightly loaded airborne PM filters
Bibliographic record
Abstract
A simple single-step ultrasonic dissolution procedure for low mass (<1 mg) particulate matter (PM) filter samples using HNO3–HF acid solution is proposed for multi-element determination using ICP-MS. The PM-loaded PTFE filter samples are inserted directly into disposable centrifuge tubes for acid extraction using ultrasonic digestion (UD). Potential interferences owing to contamination and element loss are minimized. Key factors influencing element recoveries are investigated, including digestion solution composition, acid concentration, temperature, and matrix interferences. Optimized conditions for UD include an acid mixture consisting of 4.0 mL HNO3 and 0.1 mL HF with ultrasonication proceeding at 90°C for one hour, followed by 10-fold dilution. Recoveries of 80–120% are achieved for almost all of the 20 elements tested (Be, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Mo, Ag, Cd, Sb, Ba, Pb and U) in four standard reference materials with contrasting matrices: NIST 1648 (urban PM), NIST 2584 (indoor dust), NIST 2710 (soil), and NIST 1633b (coal fly ash). The exception is Cr in NIST 1648 for which recovery is low (30%) using this method. Element concentrations obtained for PM-loaded filter samples using the proposed UD + ICP-MS method agree with results obtained using energy dispersive X-ray fluorescence (paired t-test p > 0.2; 95% CI).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".