Size fractionation of dissolved (<0.45 µm) trace elements from extracted soil with water and CaCl2 using AF4-UV-ICPMS to predict their bioavailability
Bibliographic record
Abstract
Dissolved (<0.45 µm) trace elements (TEs) represent the sum of free ions, simple complexes and colloid-associated forms which have different mobility and bioavailability in soils. The distribution of TEs amongst these chemical forms was directly quantified in soil extracts using asymmetric flow field-flow fractionation (AF4) coupled to ultraviolet–visible absorbance spectrophotometry (UV) and inductively coupled plasma mass spectrometry (ICP-MS). The soil extracts were obtained using single extraction method with water and 0.01 M CaCl2, respectively. The yields of dissolved TEs extracted from the soils were profoundly impacted by extractants. Using AF4-UV-ICPMS, we show that dissolved species of Ba, Cr, Li, Mn and Mo were primarily present as “truly dissolved”/mainly ionic species (<1 kDa), e.g., hydrated cations, simple complexes or oxyanions, and therefore, likely represented the most bioavailable fraction. The distribution of these TEs amongst dissolved forms was unaffected by the different extractants. However, their dissolved concentrations were profoundly affected. Distributions of Al, As, Co, Cu, Fe, Ni, Pb, Th, Tl, U, V and Zn among the various chemical forms significantly differed with water and CaCl2 extractants. In water extracts, a greater proportion of these elements was associated with colloidal forms having sizes from 1 kDa to 0.45 µm, i.e., dissolved organic matter (DOM) or/and inorganic colloids. Water not only released greater colloid-complexed concentrations of TEs, like Al, As, Fe, Pb, Th, Tl, U and V, but also liberated greater amounts associated with ionic and small forms. Extractants like water and CaCl2 are useful for recovering bioavailable TEs from soils. However, the dissolved TEs extracted using water or CaCl2 represented TE concentrations and forms with different bioavailability. The AF4-UV-ICPMS technique is useful for directly quantifying TEs existing as mainly ionic species and those bound with DOM and inorganic colloids, and thus offers clear insight into their bioavailability in soils. This method also facilitates a better understanding of the effects of extractants on estimating TE bioavailability.
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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 | 0.001 |
| 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".