Improving trace element measurement accuracy: Lysimeter cleaning and material effects
Bibliographic record
Abstract
Abstract Lysimeters are widely used to collect soil solutions with minimal disruption to the natural distribution of trace elements (TEs); however, sorption and release of TEs to and from lysimeter materials can introduce significant errors at trace concentrations. This study reports optimized cleaning protocols and evaluates TE sorption and release from three commonly used tension lysimeters: Rhizon MOM, SPE20 nylon, and SiC20. Cleaning involved sequential rinsing with acids and ultrapure water (UPW), followed by soaking and sonication in UPW. The sorption and release behaviors of lysimeters were evaluated before and after cleaning using soil leachate. Rhizon lysimeters cleaned more quickly than others due to lower residual TE concentrations. Blanks from Rhizon and SPE20 nylon lysimeters after cleaning generally showed low TE concentrations (1–100 ng L −1 ), except for Al, Fe, and Zn (>100 ng L −1 ), and Tl and Th (<1 ng L −1 ; Th <5 ng L −1 in nylon), demonstrating the effectiveness of cleaning protocols. In contrast, SiC lysimeters released high concentrations of Al, V, Fe, Ni, Zn, and Sr (>1 µg L −1 ) even after cleaning, and showed significant sorption and release of Al, Ag, Ba, Cd, Cs, Cu, Fe, Mn, Tl, V, and Zn. These results provide crucial information to assist researchers in selecting the right lysimeter and cleaning protocols depending on research objectives and desired detection limits.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".