Functionalized Nanoporous Silica for the Removal of Heavy Metals from Biological Systems: Adsorption and Application
Why this work is in the frame
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Bibliographic record
Abstract
Surface-functionalized nanoporous silica, often referred to as self-assembled monolayers on mesoporous supports (SAMMS), has previously demonstrated the ability to serve as very effective heavy metal sorbents in a range of aquatic and environmental systems, suggesting that they may be advantageously utilized for biomedical applications such as chelation therapy. Herein we evaluate surface chemistries for heavy metal capture from biological fluids, various facets of the materials' biocompatibility, and the suitability of these materials as potential therapeutics. Of the materials tested, thiol-functionalized SAMMS proved most capable of removing selected heavy metals from biological solutions (i.e., blood, urine, etc.) Consequentially, thiol-functionalized SAMMS was further analyzed to assess the material's performance under a number of different biologically relevant conditions (i.e., variable pH and ionic strength) to gauge any potentially negative effects resulting from interaction with the sorbent, such as cellular toxicity or the removal of essential minerals. Additionally, cellular uptake studies demonstrated no cell membrane permeation by the silica-based materials generally highlighting their ability to remain cellularly inert and thus nontoxic. The results show that organic ligand functionalized nanoporous silica could be a valuable material for a range of detoxification therapies and potentially other biomedical applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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 it