Visualization of critical metals in marine nodules by rapid and high-resolution LA-ICP-TOFMS mapping
Bibliographic record
Abstract
Polymetallic nodules are one of the most ubiquitous critical metal reservoirs in the deep sea during early diagenesis. Trace elements including REY (Rare earth elements + Y), can provide a wealth of information to document the source-to-sink (STS) processes and critical metals enrichment regimes. Element imaging is an important “see-through” technique to display the distribution of multi-elements to understand the respective concentration relationship. However, traditional elemental mapping of macro (several millimeters) and micro (tens of micrometers) areas has remained a major challenge due to the long analytical time and low lateral resolution. Here, we apply inductively coupled plasma time-of-flight mass spectrometry (ICP-TOFMS), coupled to laser ablation (LA) with a low-dispersion two-volume sample cell and a dual concentric injector at both the macro- and micro-scales. LA-ICP-TOFMS mapping was applied to an entire polymetallic nodule (size of 10 * 9 mm2 @ 40 µm2 spot size, 1300 µm/s scan speed and 15 Hz frequency) and microdomains with microlayers (size of 700 * 350 µm2 @ 1 µm2, 200 µm/s and 200 Hz), respectively. The intra-nodule distribution patterns of elements are mainly controlled by the occurrence minerals. For example, the distribution patterns of some metals (e.g., Cu, Co, Ni) are similar to that of Mn, indicating these metals host in Mn-phase minerals. There is a trend of decreasing and then increasing of these elements from the inner to the outer layer, suggesting that the redox environment may have undergone a process from oxic to suboxic and then to oxic during the growth process. Our results demonstrate the feasibility of LA-ICP-TOFMS analysis on soft and porous materials, whose advantages include a shorter time and higher lateral resolution compared with traditional LA-ICP-QMS, expanding the geochemical mapping techniques for deep-sea early diagenetic sediments samples.
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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 | 0.001 | 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".