Optimizing the Synthesis of Red- to Near-IR-Emitting CdS-Capped CdTe<i><sub>x</sub></i>Se<sub>1</sub><sub>-</sub><i><sub>x</sub></i> Alloyed Quantum Dots for Biomedical Imaging
Bibliographic record
Abstract
Advancements in biomedical imaging require the development of optical contrast agents at an emission region of low biological tissue absorbance, fluorescence, and scattering. This region occurs in the red to near-IR (>600 nm) wavelength window. Quantum dots (Qdots) are excellent candidates for such applications. However, there are major challenges with developing high optical quality far-red- to near-IR-emitting Qdots (i.e., poor reproducibility, low quantum yield, and lack of photostability). Our aim is to systematically study how to prepare alloyed CdTe x Se 1- x with these properties. We discovered that the precursor concentrations of Te-to-Se and growth time had major impacts on the Qdot's optical properties. We also learned that the capping of these alloyed Qdots were difficult with ZnS but feasible with CdS because of the ZnS's lattice mismatch with the CdTe x Se 1 - x . These systematic and basic studies led to the optimization of synthetic parameters for preparing Qdots with high quantum yield (>30%), narrow fluorescence full width at half-maxima (<50%), and stability against photobleaching (>10 min under 100W Hg lamp excitation with a 1.4 numerical aperture 60× objective) for biomedical imaging and detection. We further demonstrate the conjugation of biorecognition molecules onto the surface of these alloyed Qdots and characterize their use as contrast agents in multicolored and ultrasensitive imaging.
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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 | 0.001 | 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".