Controlling photoinduced electron transfer from PbS@CdS core@shell quantum dots to metal oxide nanostructured thin films
Bibliographic record
Abstract
N-type metal oxide solar cells sensitized by infrared absorbing PbS quantum dots (QDs) represent a promising alternative to traditional photovoltaic devices. However, colloidal PbS QDs capped with pure organic ligand shells suffer from surface oxidation that affects the long term stability of the cells. Application of a passivating CdS shell guarantees the increased long term stability of PbS QDs, but can negatively affect photoinduced charge transfer from the QD to the oxide and the resulting photoconversion efficiency (PCE). For this reason, the characterization of electron injection rates in these systems is very important, yet has never been reported. Here we investigate the photoelectron transfer rate from PbS@CdS core@shell QDs to wide bandgap semiconducting mesoporous films using photoluminescence (PL) lifetime spectroscopy. The different electron affinity of the oxides (SiO2, TiO2 and SnO2), the core size and the shell thickness allow us to fine tune the electron injection rate by determining the width and height of the energy barrier for tunneling from the core to the oxide. Theoretical modeling using the semi-classical approximation provides an estimate for the escape time of an electron from the QD 1S state, in good agreement with experiments. The results demonstrate the possibility of obtaining fast charge injection in near infrared (NIR) QDs stabilized by an external shell (injection rates in the range of 110-250 ns for TiO2 films and in the range of 100-170 ns for SnO2 films for PbS cores with diameters in the 3-4.2 nm range and shell thickness around 0.3 nm), with the aim of providing viable solutions to the stability issues typical of NIR QDs capped with pure organic ligand shells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".