Investigation of ligand effects on exciton recombination in PbS nanoparticles
Bibliographic record
Abstract
Multiple exciton generation (MEG) and exciton recombination were studied by femtosecond transient absorption spectroscopy in PbS nanoparticles (NPs) capped with oleic acid (PbS–OLA) and 2,3-dimercaptopropane sulfonate (PbS–DMPS) ligands. Analysis of the transient absorption data showed that the PbS–DMPS nanoparticles exhibit increased rates of multi- and single-exciton recombination compared with the PbS–OLA nanoparticles; however, the MEG yield for both sets of particles was the same within experimental error. The origin of the differences in the exciton recombination decay rates is unknown, but it is speculated that it may be due to the ionic functionality of DMPS or to the different ligating atoms of the OLA and DMPS ligands. The PbS–DMPS nanoparticles were highly sensitive to the presence of oxygen, which caused a dramatic increase in nonradiative decay pathways, which can be mistaken for multiexciton absorbance and decay. Removal of oxygen eliminated the nonradiative decay pathways. Overall, this study showed that the dynamics of MEG can be modified by changing the NP ligand shell, a result that may be useful in the development of NP-based thin-film solar devices.
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 | 0.001 |
| 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".