Excellent Low-Temperature Formaldehyde Decomposition Performance over Pt Nanoparticles Directly Loaded on Cellulose Triacetate
Bibliographic record
Abstract
Cellulose triacetate (CTA) was first applied as the catalytic support to load Pt nanoparticles for low-temperature formaldehyde (HCHO) decomposition. The room-temperature HCHO decomposition rate of the obtained catalyst (Pt/CTA) is 13.4 times and 4.3 times as high as that of the microcrystalline cellulose-supported Pt catalyst and Pt/TiO 2 under the parallel preparation condition, respectively. With facile shaping, the CTA microsphere-supported Pt catalyst and the CTA film-supported Pt catalyst could also exhibit similar HCHO decomposition performance to that of the powdery one. Structural analyses showed that Pt nanoparticles (∼2.3 nm) could densely disperse on the small-area surface of Pt/CTA and provide abundant active sites. Moreover, only the HCHO molecules could slightly adsorb onto CTA, while other HCHO decomposition-related species absolutely could not. This is beneficial to the coordination of various steps of HCHO decomposition and the transfer of reaction species to vicinal active sites of Pt/CTA. HCHO-diffuse reflectance infrared Fourier transformed spectroscopy studies demonstrated that no species were accumulated on the Pt/CTA catalyst. Both the good Pt dispersion and unique adsorption properties of CTA were responsible for the excellent low-temperature HCHO decomposition performance of Pt/CTA.
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 | 0.000 | 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".