Competition between Förster Resonance Energy Transfer and Electron Transfer in Stoichiometrically Assembled Semiconductor Quantum Dot–Fullerene Conjugates
Bibliographic record
Abstract
Understanding how semiconductor quantum dots (QDs) engage in photoinduced energy transfer with carbon allotropes is necessary for enhanced performance in solar cells and other optoelectronic devices along with the potential to create new types of (bio)sensors. Here, we systematically investigate energy transfer interactions between C60 fullerenes and four different QDs, composed of CdSe/ZnS (type I) and CdSe/CdS/ZnS (quasi type II), with emission maxima ranging from 530 to 630 nm. C60-pyrrolidine tris-acid was first coupled to the N-terminus of a hexahistidine-terminated peptide via carbodiimide chemistry to yield a C60-labeled peptide (pepC60). This peptide provided the critical means to achieve ratiometric self-assembly of the QD-(pepC60) nanoheterostructures by exploiting metal affinity coordination to the QD surface. Controlled QD-(pepC60)N bioconjugates were prepared by discretely increasing the ratio (N) of pepC60 assembled per QD in mixtures of dimethyl sulfoxide and buffer; this mixed organic/aqueous approach helped alleviate issues of C60 solubility. An extensive set of control experiments were initially performed to verify the specific and ratiometric nature of QD-(pepC60)N assembly. Photoinitiated energy transfer in these hybrid organic-inorganic systems was then interrogated using steady-state and time-resolved fluorescence along with ultrafast transient absorption spectroscopy. Coordination of pepC60 to the QD results in QD PL quenching that directly tracks with the number of peptides displayed around the QD. A detailed photophysical analysis suggests a competition between electron transfer and Förster resonance energy transfer from the QD to the C60 that is dependent upon a complex interplay of pepC60 ratio per QD, the presence of underlying spectral overlap, and contributions from QD size. These results highlight several important factors that must be considered when designing QD-donor/C60-acceptor systems for potential optoelectronic and biosensing applications.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".