Solid state NMR studies of photoluminescent cadmium chalcogenide nanoparticles
Bibliographic record
Abstract
Solid state (113)Cd, (77)Se, (13)C and (31)P NMR have been used to study a number of Cd chalcogenide nanoparticles synthesized in tri-n-octyl-phosphine (TOP) with different compositions and architectures. The pure CdSe and CdTe nanoparticles show a dramatic, size-sensitive broadening of the (113)Cd NMR line, which can be explained in terms of a chemical shift distribution arising from multiple Cd environments. From (13)C NMR, it has been discovered that TOP, or its derivatives such as TOPO (trioctylphosphine oxide), is rapidly moving about the surface of the nanoparticles, indicating that it is relatively weakly bound as compared to other materials used as surface ligands, such as hexadecylamine. (31)P NMR of the nanoparticles shows at least five species arising from coordination of the ligands to different surface sites. (113)Cd NMR of CdSeTe alloy and layered nanoparticles has provided crucial information which, in conjunction with results from other techniques (especially optical characterization), has made it possible to develop a detailed picture of the composition and structure of these materials: (i) a true CdSeTe homogeneous alloy nanoparticle, (ii) a nanoparticle segregated into an alloy core region rich in Te, with a CdSeTe (close to 1 : 1 Se : Te) alloy shell and (iii) a CdSe/CdTe/CdSe layered nanoparticle in which the CdTe layer contains a small amount of Se and which forms a Quantum Dot Quantum Well (QDQW) system. The results demonstrate that solid state NMR is a vital tool in the arsenal of characterisation techniques available for nanomaterials.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".