Chromatographic property classification of narrowly distributed ZnS quantum dots
Bibliographic record
Abstract
Although optimized synthesis methods for nanoparticles (NPs) on small scale can lead to narrow particle size distributions (PSDs) and thus defined optical properties, in particular during scale-up, an additional classification step must be applied to adjust the particle properties according to the needs of the later application. NP chromatography is a promising separation method, which can be potentially transferred to preparative and industrial scale. Herein, we demonstrate that remarkable classification of ZnS quantum dots (QDs) with respect to the fundamental band gap energy is achieved by chromatography although the PSD of the feed material is already very narrow (1.5-3.0 nm). We investigated the interactions of ZnS QDs with stationary and mobile phase materials in order to select a proper material couple so that irreversible NP adhesion, agglomeration, decomposition or dissolution of the ZnS QDs during the chromatographic experiments are avoided and highly reproducible chromatograms are obtained. Using a fraction collector, the already narrowly size distributed feed material was separated into coarse and fine fractions with distinct band gap energies. For characterization of the chromatographic fractionation, quantities known from particle technology, i.e. separation efficiency, cut size and yield, were adapted to the band gap energy distributions accessible from UV/Vis spectroscopy. The optimization of process conditions (flow rate, temperature, switching time of the fraction collector) allows fine-tuning of the property classification and therefore of the optical properties within the narrow distribution of the ZnS QDs. Our study shows the strength and high potential of chromatography for preparative and continuous separation of NPs even in case of narrow size-distributed sub-10 nm semiconductor QDs.
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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 | 0.000 |
| 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".