Functionalized Nanoporous Silica for the Removal of Heavy Metals from Biological Systems: Adsorption and Application
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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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) | 0.000 | 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) | 0.001 | 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".