Highly Stable Bio-templated InP/ZnSe/ZnS Quantum Dots for Specific Monitoring of Bacterial Membrane Proteins
Bibliographic record
Abstract
Despite their unique optical and electrical characteristics, traditional semiconductor quantum dots (QDs) made of heavy metals or carbon are not compatible with many biomedical applications. Cytotoxicity and environmental concerns are key limiting factors that prevent their widescale transition from laboratory research to real-world medical applications. Recently, advanced InP/ZnSe/ZnS QDs have emerged as excellent alternatives to traditional QDs due to their lower toxicity and optical properties; however, they fall short of traditional QDs with respect to their versatility for bioconjugation (i.e., surface chemistry limitations causing unstability in aqueous environments). In this work, we construct a road map for generating, for the first time, highly efficient bio-templated InP/ZnSe/ZnS-aptamers (QDAPT) with long-term stability and high selectivity for applications in targetting bacterial membrane proteins. Our QDAPTs show fast binding reaction kinetics (less than 5 minutes), high brightness, and high shelf-life stability (3 months) after biotemplation in aqueous solvents. We also demonstrate the detection of bacterial membrane proteins on common surfaces using a hand-held imaging device, which attests to the great potential of this system for incorporation into future biomedical technologies.
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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".