Recyclable Graphene Oxide-Supported Titanium Dioxide Photocatalysts with Tunable Properties
Bibliographic record
Abstract
A modular synthesis technique was developed for producing graphene-supported titanium dioxide photocatalysts. The modular synthesis allowed for simple tuning of the ratio of particle loading on the graphene oxide (GO) surface as well as good photocatalytic activity of the composite and quick, efficient magnetic separability. GO flakes were used as a support for titanium dioxide nanoparticles and SiO2 insulated nano-sized magnetite aggregates. Different composition ratios were tested, resulting in a catalyst formulation with photocatalytic activity exceeding that of a commercial photocatalyst by a factor of 1.2 as well as excellent recyclability, with the capability to degrade 3 mg/L methylene blue in aqueous solution over 10 consecutive trials with minimal loss in photocatalytic efficiency. Recovery of the catalyst was achieved by simply exposing the nanocomposite to a magnetic field for ∼1 minute. Furthermore, it was found that the catalyst could be regenerated to its initial efficiency through simple UV treatment to provide additional re-use. To highlight the importance of the nanocomposite to the current water treatment industry, we showed rapid degradation of pharmaceutical compounds caffeine and carbamazepine within 60 min. The nanocomposite shows activity exceeding that of commercial photocatalyst P25 with the added benefit of being fully recoverable, reusable, and easy to produce. Overall, a simple technique for producing and tuning an effective magnetically recyclable nanocomposite was developed which should allow easy scalability and industrial production, a factor critical for the implementation of nano-based water treatment techniques.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".