Toxic Metals Chelation by 18-Crown-6 Ethers in Multiple Solutions andQuantification by Spectroscopic Techniques
Bibliographic record
Abstract
Toxic metals exposure is a significant problem for military personnel, with increasingly prevalent embedded fragments due to improvised explosive devices.Current biomonitoring for military personnel with embedded fragments is centralized, limiting capacity and availability.Importantly, monitoring using this approach begins long after peak exposure, indicating a need for portable, multiplexed toxic metals detection that can be carried out closer to the time of exposure with increased frequency.Small molecule chelators such as crown ethers are known to selectively bind metal cations in solution.Crown ethers possess selective chelation of multiple metal ions and is dependent on molecular structure, solution properties, and other parameters.This selectivity extends to multiple ions and depends on not only molecular structure, but also the solution properties.The goal of this study is to assess the potential for metal sensing in solution as a function of crown ether structure and solution properties with future use for toxic metal sensing from embedded fragments as a potential translational objective.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".