Characterization of Gold Nanoparticle Uptake by Tomato Plants Using Enzymatic Extraction Followed by Single-Particle Inductively Coupled Plasma–Mass Spectrometry Analysis
Bibliographic record
Abstract
Plant uptake and accumulation of nanoparticles (NPs) represent an important pathway for potential human expose to NPs. Consequently, it is imperative to understand the uptake of accumulation of NPs in plant tissues and their unique physical and chemical properties within plant tissues. Current technologies are limited in revealing the unique characteristics of NPs after they enter plant tissues. An enzymatic digestion method, followed by single-particle inductively coupled plasma-mass spectrometry (SP-ICP-MS) analysis, was developed for simultaneous determination of gold NP (AuNP) size, size distribution, particle concentration, and dissolved Au concentration in tomato plant tissues. The experimental results showed that Macerozyme R-10 enzyme was capable of extracting AuNPs from tomato plants without causing dissolution or aggregation of AuNPs. The detection limit for quantification of AuNP size was 20 nm, and the AuNP particle concentration detection limit was 1000 NPs/mL. The particle concentration recoveries of spiked AuNPs were high (79-96%) in quality control samples. The developed SP-ICP-MS method was able to accurately measure AuNP size, size distribution, and particle concentration in the plant matrix. The dosing study indicated that tomato can uptake AuNPs as intact particles without alternating the AuNP properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".