Determining Size Distributions and Composition of Particles Suspended in Water: A New SEM–EDS Protocol with Validation and Comparison to Other Methods
Bibliographic record
Abstract
Abstract Knowledge of particle size distributions (PSDs) in seawater is important for understanding several facets of marine science, such as the behavior of light scattering in seawater, phytoplankton dynamics, and biogeochemical cycling. Here, a method has been developed to quantify the size distribution of particle suspensions and characterize their chemical composition utilizing a scanning electron microscope (SEM) coupled with an energy dispersive spectrometer (EDS) and applying image analysis techniques, including automatic thresholding. The method was validated by verifying the PSD and chemical composition of the Arizona Test Dust (ATD), which has a well-documented size distribution and chemical composition. Size distributions of ATD particles containing specific elements important in the marine environment, such as silicon, iron, calcium, aluminum, and potassium, were quantified. PSDs determined with the technique in field samples from coastal Long Island Sound and the remote South Pacific were compared with other sizing methods, including electroresistivity and laser diffractometry. Most accurate results for PSD determinations occurred when the particle mass loading on the filter was between 0.04 and 0.1 mg cm−2. With this in mind, immediate feedback in the field can be provided to prepare appropriate filtration sample volumes due to a linear relationship between the beam attenuation coefficient at 650 nm (c650) and the total suspended matter (TSM). Overall, the method presents two defining advantages in 1) minimizing user bias, because the majority of the analysis is automated, and 2) providing an elemental distribution in the context of a particle size distribution.
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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".