An In-House X-ray Fluorescence Spectrometer Development for <i>In Vivo</i> Analysis of Plants
Bibliographic record
Abstract
X-ray fluorescence spectroscopy (XRF) is an analytical technique employed to determine the elemental composition of diverse materials. Due to its nondestructive nature and direct analysis that requires little or no sample preparation, it has been particularly useful for investigating the mineral composition of plants and soil. However, commercially available XRF benchtop equipment often restricts this type of experiment in plant science due to the volume of the sample chamber and the source–detector geometry. To overcome this problem, we developed an XRF setup that prioritizes in vivo -based experiments. The equipment is equipped with a 4 W Ag X-ray tube and a silicon drift detector. The detection limits are comparable to those of commercial instruments and suitable for evaluating plant tissues. Finally, a case study using tomato plants as a model species and rubidium (Rb + ) and strontium (Sr 2+ ) as tracers for potassium (K + ) and calcium (Ca 2+ ), respectively, demonstrated their feasibility for long-term in vivo analysis. Therefore, the present XRF system stands out as a viable and cost-effective tool for assessing the absorption and transport of minerals in plant tissues probed by time-resolved in vivo X-ray spectroscopy.
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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 | 0.001 | 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) | 0.010 | 0.005 |
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".