Multimodal I-III-VI<sub>2</sub> quantum dots for in vivo near infrared fluorescence and magnetic resonance imaging
Bibliographic record
Abstract
Semiconductor nanocrystals, or quantum dots (QDs) have the potential to significantly impact the performance of near infrared fluorescence imaging for biomedical research, diagnostics and optically assisted surgery. For example, QDs could be applied to detection of the sentinel lymph node, the status of which is a key prognostic factor for treatment of many types of cancer. Unfortunately, QDs emitting in the near infrared have been until recently composed of toxic compounds (Cd, Pb, Hg, Te, As...). The potential long term release of these toxic elements in the body has thus been a major obstacle to the QD clinical use, but would also represent an important hurdle for large scale optoelectronic applications. Here we present the synthesis and optical characterization of core and core-shell QDs based on CuInS2 and CuInSe2 that do not contain any toxic heavy metals. Their emission is tunable in the whole near infrared imaging window from 600 nm to 1100 nm. Growth of a ZnS shell around the CuInS2 and CuInSe2 cores increases the fluorescence quantum yield up to 60 % in organic solvents. Structural, composition and optical characterization of these QDs suggest that photoluminescence occurs from defect states inside the QD core. Core/shell QDs maintain their fluorescence properties after solubilisation in water and for long periods of time in vivo. We demonstrate their use for in vivo imaging and detection of regional lymph nodes in mice. Interestingly, CuInS2 -based QDs show a much reduced in vivo toxicity compared to CdTeSe/CdZnS QDs. In addition, we present the synthesis of novel multimodal I-III-VI2 QDs based on doping of near infrared CuInS2/ZnS QDs with paramagnetic Mn2+ ions. We characterize the structural and optical properties of these novel probes. The level of Mn doping is examined under varying synthesis conditions using elemental analysis and electron paramagnetic resonance. SQUID magnetic measurements show the super-paramagnetic behaviour of these nano-particles. These Mn-doped CuInS2/ZnS QDs constitute promising compact nano-probes for multimodal magnetic resonance imaging and near infrared fluorescence and could be useful for whole body pre-clinical MRI mapping and intra-operative optical tracking.
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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".