Multiway data analysis approach toward understanding of photoluminescence and energy transfer in carbon nanodots
Bibliographic record
Abstract
Abstract In this study, dilution analysis and anion exchange chromatography (AEC) were employed to provide insights into the photoluminescence (PL) of carbon nanodots (CNDs). A stepwise dilution process revealed that some of the fluorophores with higher energy emission were quenched in the high concentration solution and appeared in the dilute solutions. AEC fractionation led to seven sorts of CND fractions with similar surface charges. The fractionation for this CND mixture showed that excitation wavelength dependence was lower for separated CND particles. The wavelength dependence of excitation spectra could be due to energy exchange between particles that was reduced in diluted solutions and separated fractions. Multivariate analysis of AEC's data demonstrated that there were five distinct fluorophores, which formed the total CND emission. It is interesting that none of these fluorophores had a clear contribution to the surface charge of the CND particles. Further characterization through FTIR spectroscopy and 1H NMR revealed that optical properties of CNDs did not follow the surface functional groups in CNDs. This situation means that the optical behaviour of particles and their fluorophores differed depending on the surface functional groups.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".