Combining Selective Sequential Extractions, X-ray Absorption Spectroscopy, and Principal Component Analysis for Quantitative Zinc Speciation in Soil
Bibliographic record
Abstract
Selective sequential extractions (SSE) and, more recently, X-ray absorption fine-structure IXAFS) spectroscopy have been used to characterize the speciation of metal contaminants in soils and sediments. However, both methods have specific limitations when multiple metal species coexist in soils and sediments. In this study, we tested a combined approach, in which XAFS spectra were collected after each of 6 SSE steps, and then analyzed by multishell fitting, principal component analysis (PCA) and linear combination fits (LCF), to determine the Zn speciation in a smelter-contaminated, strongly acidic soil. In the topsoil, Zn was predominately found in the smelter-emitted minerals franklinite (60%) and sphalerite (30%) and as aqueous or outer-sphere Zn2+ (10%). In the subsoil, aqueous or outer-sphere Zn2+ prevailed (55%), but 45% of Zn was incorporated by hydroxy-Al interlayers of phyllosilicates. Formation of such Zn-bearing hydroxy-interlayers, which has been observed here for the first time, may be an important mechanism to reduce the solubility of Zn in those soils, which are too acidic to retain Zn by formation of inner-sphere sorption complexes, layered double hydroxides or phyllosilicates. The stepwise removal of Zn fractions by SSE significantly improved the identification of species by XAFS and PCA and their subsequent quantification by LCF. While SSE alone provided excellent estimates of the amount of mobile Zn species, it failed to identify and quantify Zn associated with mineral phases because of nonspecific dissolution and the precipitation of Zn oxalate. The systematic combination of chemical extraction, spectroscopy, and advanced statistical analysis allowed us to identify and quantify both mobile and recalcitrant species with high reliability and precision.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".