The design and application of sequential extractions for mercury, Part 1. Optimization of HNO <sub>3</sub> extraction for all non-sulphide forms of Hg
Bibliographic record
Abstract
The final step in a sequential extraction procedure for Hg in geological samples usually involves a strong acid attack such as aqua regia. It is assumed that the Hg component measured in this step represents HgS (cinnabar), the common and highly insoluble form of Hg in nature. This paper describes the optimization of an HNO 3 -based dissolution of all non-sulphide forms of Hg while minimizing the solubilization of HgS. Previous work indicated that 12M (75% v/v) HNO 3 would fulfil that objective but its application in this work to samples containing very fine-grained HgS showed that this concentration was too strong as it partially dissolved HgS. Thus, HgS control samples were used to study the effect of HNO 3 concentration, duration of extraction and sample-to-volume ratio on the chemical breakdown of HgS. The recommended procedure to separate HgS from other forms of Hg is a two-hour extraction of 1 g of sample in 20 ml of 40% HNO 3 with constant agitation followed by a 10-ml rinse with 40% HNO 3 . This then ensures that all the cinnabar, fine- and coarse-grained, reports to the subsequent aqua regia step.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".