Solvothermal synthesis of lanthanide-functionalized graphene oxide nanocomposites
Bibliographic record
Abstract
We propose a facile approach to the preparation of graphene oxide (GO) composites with lanthanide (Ln) oxide/hydroxide nanoparticles (Ln = La, Eu, Gd, Tb) under relatively mild conditions by two different procedures of solvothermal synthesis. The mechanism of GO-Ln nanocomposite formation is thought to involve the initial coordination of Ln3+ ions to the oxygen-containing groups of GO as nucleation sites, followed by f Ln2O3 and Ln(OH)3 nanoparticle growth. The nanocomposites obtained preserve the intrinsic planar honeycomb-like structures of graphene as proven by the typical G and D bands in the Raman spectra. Fourier-transform infrared and X-ray photoelectron spectroscopy confirm the interaction between oxygen-containing groups of GO and Ln ions. The size and distribution of Ln oxide/hydroxide nanoparticles on GO sheets, estimated from scanning and transmission electron microscopy images, vary broadly for the different lanthanides. The size can span from sub-nm dimensions for Eu oxide to more than 10 μm for Eu hydroxide nanoparticles. The most homogeneous distribution of Ln oxide/hydroxide nanoparticles was found in La-containing composites. Thermogravimetric analysis demonstrated that all the GO-Ln nanocomposites are thermally less stable, by up to 30 °C than pristine GO.
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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".