Ultrathin Multi‐Doped Molybdenum Oxide Nanodots as a Tunable Selective Biocatalyst
Bibliographic record
Abstract
Abstract The reactive oxygen species (ROS) serve a significant role in cancer therapy due to their oxidative capabilities to modulate cellular functions from homeostasis to apoptosis. While conventional noble metal nanoparticles exhibit superior biocatalytic efficacy in ROS induction, their indistinctive toxicity toward cells and organisms limit their potential for targeted cancer therapy. Here, ultrathin biocompatiable molybdenum oxides (MoO x ) nanodots are explored that simultaneously incorporate hydrogen (H + ) and ammonia (NH 4 + ) dopants, subsequently their electronic band structures can be modulated by both the relative contents of H + and NH 4 + dopants for efficient generation of ROS. An ultrafast and repeatable dye degredation capability in the absence of light is find in MoO x doped with low H + and high NH 4 + , in which hydroxyl radicals (·OH) is identified as the agent stimulating this ROS‐driven process through scavenger analysis. More importantly, the selective biocatalytic potential of such a multi‐doped MoO x is demonstrated by the comprehensive assay analysis, revealing a three‐fold greater cytotoxicity toward HeLa cancer cells within 24 h compared with those of HEK293T healthy control. The finding shines a light on the targeted cancer therapies that spare healthy cells, showing the potential of multi‐doped metal oxide as a biocompatiable alternatives to noble metals in selective cytotoxicity against tumor cells.
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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) | 0.000 | 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 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".