Biofunctionalisation of core shell colloidal quantum dots for the tracking of synaptic receptors
Bibliographic record
Abstract
Functional bioconjugated colloidal quantum dots (cQDs) have already been used to track single molecules like synaptic receptors[1]. There are two main steps to transform the synthesized hydrophobic cQDs into ready-to-use biomolecule trackers, which are (1) the ligand exchange or encapsulation leading to an hydrophilic cQD and (2) the subsequent conjugation with a protein which specifically recognises the target molecule. It is possible to buy commercial hydrophilic cQDs coupled with binding proteins like antibodies. However, their main drawback is the large size of the polymer capsule making the cQD soluble in water, which can become problematic for some applications. For example, in the tracking of synaptic receptors, the size of the synaptic cliff (distance between presynaptic and postsynaptic neurons) is thought to be between 30 to 40 nm and penetration of cQD-labeled receptors could be impeded if they are too large. An alternative approach to give cQDs an affinity with water without dramatically increasing their size is to proceed to a ligand exchange using dihydrolipoic acid (DHLA) as the new ligand. This technique has successfully been applied in the past and is well described in the literature[2]. One must be aware that the new ligand is likely to deteriorate the photoluminescence (PL) properties of cQDs, such as quantum efficiency and photostability[3]. Here we perform a ligand exchange using DHLA on well-passivated low-strain CdSe/CdS/Cd0.5ZnS0.5/ZnS cQDs and demonstrate that this manipulation does not modify dramatically the photoluminescence properties of new hydrophilic cQDs. Shown on figure 1, PL of cQDs after photoactivation is almost constant during more than 4 hours of 488nm wavelenght laser
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".