Laser-ablation vs. bulk tissue ICP-MS for conifer tissue elemental analysis
Bibliographic record
Abstract
Laser ablation inductively coupled plasma mass spectrometry (LA ICP-MS) has emerged as a robust tool for directly measuring trace elements in solid, intact samples. Laser ablation requires minimal sample preparation, whereas more conventional bulk sample analysis entails preliminary size reduction and acid digestion, and hence risks sample contamination and volatilization losses. LA ICP-MS may allow for rapid determination of elemental constitution in plants at low detection limits; however, application of LA ICP-MS on plant tissues is challenged by sample heterogeneity, as well as the lack of recognized standards for calibration and criteria for sample preparation. We analyzed needle samples from an adult jack pine ( Pinus banksiana L.) tree through LA ICP-MS using a widely available NIST SRM 610 quartz calibration standard, with 43 Ca as an internal standard element measured by electron probe microanalysis. LA ICP-MS analyses were run on intact needle samples and on pulverized, homogenized and pelletized samples (n = 21), and compared to needles dried, ground, and analyzed in triplicate with aqua regia acid digestion and conventional ICP-MS. Overall, the LA ICP-MS measures on intact pine samples accurately predicted the chemical composition of pine needle tissue obtained by the bulk sample acid-digestion and ICP-MS method for most elements, while preliminary pulverizing and pelletizing did not result in greater accuracy or reduced bias. LA ICP-MS analysis of many plant nutrients ( 31 P, 24 Mg, 66 Zn) from both intact and pelletized tree needle samples matched the values obtained from acid-digestion ICP-MS most closely, while measures of less-stable metals (such as 140 Ce, 139 La, and 57 Fe) deviated more from acid-digested samples. Values of 39 K estimates varied between methods, as storage of 39 K is preferentially located in needle mesophyll layers beyond laser depths. Large deviations were found for volatile elements, with significantly lower 208 Pb concentrations measured by LA ICP-MS than acid-digested samples. We conclude that LA ICP-MS of intact tissues is a viable tool for accurate, non-destructive analysis for most elements and is particularly suitable for analysis of small samples and for volatile elements not amenable to conventional methods. • LA ICP-MS on intact jack pine needles accurately measured most abundant elements in tree tissues. • Grinding, sieving and pelletizing needles prior to LA ICP-MS did not yield more accurate analysis for most elements. • LA ICP-MS values on intact needles were aligned with acid digestion ICP-MS values for nutrients and divergent for volatiles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".