Functional Bio-inorganic Hybrids from Silicon Quantum Dots and Biological Molecules
Bibliographic record
Abstract
Quantum dots (QDs) are semiconductor nanoparticles that exhibit photoluminescent properties useful for applications in the field of diagnostics and medicine. Successful implementation of these QDs for bio-imaging and bio/chemical sensing typically involves conjugation to biologically active molecules for recognition and signal generation. Unfortunately, traditional and widely studied QDs are based upon heavy metals and other toxic elements ( e.g., Cd- and Pb-based QDs), which precludes their safe use in actual biological systems. Silicon quantum dots (SiQDs) offer the same advantages as these heavy-metal-based QDs with the added benefits of nontoxicity and abundance. The preparation of functional bio-inorganic hybrids from SiQDs and biomolecules has lagged significantly compared to their traditional toxic counterparts because of the challenges associated with the synthesis of water-soluble SiQDs and their relative instability in aqueous environments. Advances in SiQD synthesis and surface functionalization, however, have made possible the preparation of functional bio-inorganic hybrids from SiQDs and biological molecules through different bioconjugation reactions. In this contribution, we review the various bioconjugate reactions by which SiQDs have been linked to biomolecules and implemented as platforms for bio-imaging and bio/chemical sensing. We also highlight the challenges that need to be addressed and overcome for these materials to reach their full potential. Lastly, we give prospective applications where this unique class of nontoxic and biocompatible materials can be of great utility in the future.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".