Determination of trace elements in iron-manganese oxide coatings by laser ablation ICP-MS for environmental monitoring/mineral exploration
Bibliographic record
Abstract
Iron-manganese oxide coatings can form on a wide range of geologic samples and they have the inherent geochemical ability to adsorb elements and thus potentially act as tools for mineral exploration and environmental monitoring. In this study the concentrations of elements present in Fe-Mn oxide coatings were determined using Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LAM-ICP-MS). The potential Fe-Mn oxide coatings for environmental monitoring was assessed at three study sites located on the island of Newfoundland. Additionally, annual accretion at one study site was also examined to further understand coating accretion as a function of time. -- Useful analytical results were obtained using a data normalisation scheme which set the sum of MnO₂ + Fe₂O₃ in the coating to 100%. Quantification of an internal standard was investigated using an Electron Microprobe and Scanning Electron Microscope. However this was not successful because of sample heterogeneity and the thinness of the coating. The acquired samples underwent a simple sample preparation procedure, and were analysed using optimised operating conditions. -- For the mineral exploration and environmental monitoring aspect of this study, four study locations with a wide range of sample sites were selected; Tilt Cove, Betts Cove, Robinsons River, and Rennies River. Water samples were collected along with the Fe-Mn oxide coatings. The waters were analysed for dissolved oxygen, pH, conductivity, temperature, and 46 elements using ICP-MS. Multivariate statistics, in the form of Principal Component Factor Analysis (PCFA) was performed on the data. Graphical display of the Factor scores from the PCF A produced sample groupings that were related to both geologic and environmental inputs. For the water PCFA, variable loadings were related to the local geology and environmental conditions along with the affinity of the element. The loading of variables in each Factor for the Fe-Mn oxide coating data was related to the adsorption of the element either on the MnO₂ or Fe₂O₃ phase with most elements except Cr and Cu displaying preferential adsorption to MnO₂. Elemental Fe-Mn oxide coating concentrations were a function of the elements affinity (chalcophile, lithophile, or siderophile), pH of the environment, stream water concentration, and amount of the two oxide phases present. Even with these complications, LA-ICP-MS analysis of Fe-Mn oxides was able to identify areas of heavy metal pollution and suggest geologic inputs. -- Accretion of Fe-Mn oxide coatings was examined on an annual basis by placing artificial substrates (streak plates, cement, and polished pebbles) in Rennies River, St. John's, Newfoundland at seven sampling sites and allowing the coatings to collect for periods of time from three months to one year with samples acquired every 3 months. Water samples were also collected and measured for most of the same variables as the oxide coatings. For the Fe-Mn oxide coatings, two Factors resulted from the PCF A. A plot of the Factor scores showed four groups of samples based on location, and time of sampling. Coating concentrations did not match any of the expected trends based on stream chemistry or coating properties. This is explained by a model of coating accretion suggesting that high amounts of Fe₂O₃ and metals coprecipitate for the initial stage of coating formation and greater amounts were adsorbed in the later stages.
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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 | 0.001 | 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".