TiO2@C core-shell nanoparticles formed by polymeric nano-encapsulation
Bibliographic record
Abstract
TiO2 semiconducting nanoparticles are known to be photocatalysts of moderate activity due to their high band-gap and high rate of electron-hole recombination. The formation of a shell of carbon around the core of TiO2, i.e., the formation of TiO2@C nanoparticles, is believed to partly alleviate these problems. It is usually achieved by a hydrothermal treatment in a presence of a sugar derivative. We present here a novel method for the formation of highly uniform C shell around TiO2 nanoparticles. For this purpose, TiO2 nanoparticles were dispersed in water using an oligomeric dispersant prepared by Reversible Addition-Fragmentation chain Transfer (RAFT) polymerization. Then the nanoparticles were engaged into an emulsion polymerization of acrylonitrile, resulting in the formation of a shell of polyacrylonitrile (PAN) around each TiO2 nanoparticles. Upon pyrolysis, the PAN was transformed into carbon, resulting in the formation of TiO2@C nanoparticles. The structure of the resulting particles was elucidated by X-Ray diffraction, FTIR, UV-VIS and Raman spectroscopy as well as TEM microscopy. Preliminary results about the use of the TiO2@C particles as photocatalysts for the splitting of water are presented. They indicate that the presence of the C shell is responsible for a significant enhancement of the photocurrent.
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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".