Multicomponent nanoarchitectures for the design of optical sensing and diagnostic tools
Bibliographic record
Abstract
Simultaneous integration of multifunctional properties from different components into a hybrid nanostructure with hierarchical organization is attractive to construct new materials sought for diverse useful applications. This review highlights recent advances in the fabrication of multicomponent organic-conjugated inorganic nanoarchitectures and their potential uses in optical sensing and diagnostic tools. The similarity of the particle sizes, between inorganic hybrids and biomolecules, is the reason they can integrate into new bioconjugated nanocomposites. These multifunctional properties enable such materials to function as dual diagnostic and therapeutic agents in imaging-guided therapy. Deoxyribonucleic acid (DNA)-templated replica approaches for fabricating DNA-functionalized plasmonic nanoarchitectures are discussed to show how incorporation of metal clusters onto helical DNA structures occurs. The resulting helix plasmonic assemblies response enhanced plasmonic properties and circular dichroism signals to external environments, means they can function as highly selective bioprobes. Nanocrystal superlattices are prepared by assembling the uniform colloids by guiding the external magnetic field and solvent evaporation. The highly organized superlattices with long-range ordering exhibit optical properties tuned by external stimuli and, consequently they can be useful for desirable optical sensors and photoswitchable patterns. The efforts discussed in this review are expected to present the structural diversity of promising multifunctional nanoarchitectures for the design of efficient optical sensing and diagnostic tools.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".