Detection of Gold Nanoparticles in Hydrothermal Fluids
Bibliographic record
Abstract
The transport and deposition of gold from colloidal suspensions in hydrothermal fluids has been a persistent theme in ore deposits research. Studies of active geothermal systems show that a complete model of gold transport must include both dissolved and particulate forms. However, samples of the hydrothermal fluids are commonly spiked with aqua regia after collection in order to put any solids back into solution, thus preventing a quantitative assessment of the particle load. Although attempts have been made to filter the solids, gold nanoparticles (Au NPs) will mostly pass the 0.2-µm filters that are in common use, and a simple technique for analyzing suspended particles in the liquids has been lacking. In this study, we demonstrate how time-resolved acquisition of mass 197 in a conventional inductively coupled plasma-mass spectrometer (ICP-MS) can be used to detect and measure Au NPs in the filtered liquids, with an example of well-characterized fluids from the Reykjanes geothermal field on Iceland. The technique allows for precise monitoring of the solution as it is introduced into the plasma with the capability of identifying individual particles carried in suspension. Results show that Au particles passing the 0.2-µm filters are abundant in the studied samples, and measurements of the individual particles can be used to determine their size. The experiment highlights the potential of emerging ICP-MS techniques, including very fast data acquisition and multielement analysis of single particles in timeof-flight mode, for characterization of NPs in hydrothermal fluids.
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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".