Addressing Challenges of Membrane Clogging in AF4-UV-ICPMS Analysis for Size Determination of Trace Elements in Acidic, Organic-Rich Peat Bog Waters
Bibliographic record
Abstract
The analysis of colloid-associated trace elements (TEs) in acidic, organic-rich waters (pH 3.8–5.8) using AF4-UV-ICPMS often necessitates the use of neutral or weakly alkaline carriers (pH 7–8.6). Employing acidic mobile phases has been deemed impractical due to the substantial sorption of colloids onto a 0.3 kDa polyethersulfone (PES) membrane and greatly reduced separation performance. This has greatly restricted the determination of potentially bioavailable forms of TEs (i.e., < 1 kDa) in acidic, organic-rich waters. To address this issue, porewaters from Sphagnum moss and peat were investigated. Membrane clogging was more pronounced in peat porewaters, where a higher deposition rate of dissolved organic matter onto the membrane was observed compared to that in moss waters. This adsorption is driven by membrane–colloid interactions, with colloids in peat porewaters exhibiting weaker electrostatic repulsion due to their higher positive ζ-potentials. Considering the actual pore size and clogging tolerance of the membrane, it is advisable to employ a 5 kDa PES membrane for peat porewaters, while a 1 kDa PES membrane suits moss waters better. Employing the optimal method enables the separation of TEs within the 0.5–20 kDa size range. Operating within a metal-free, ultraclean laboratory, TEs are detectable at the ng·L –1 level. By enabling precise and accurate separation of dissolved TEs into their size species in these peat bog waters at an appropriate pH, this method addresses diverse size profiles. This information is crucial for comprehending the chemical forms, transformations, mobility, and potential bioavailability of TEs in peat bogs.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".