AuPd bimetal immobilized on amine‐functionalized SBA‐15 for hydrogen generation from formic acid: The effect of the ratio of toluene to DMF
Bibliographic record
Abstract
Abstract The volume ratio of toluene to N,N‐dimethylformamide (DMF) was adjusted during the amine functionalization of SBA‐15 to change the amine content of SBA‐15. XPS, FTIR, and TGA analyses indicated that under the experimental synthetic conditions the number of amine groups varies with the ratio of toluene/DMF, and the highest content of amine could be obtained with a volume ratio of toluene/DMF = 3:2. The hydrogen production experiment of formic acid decomposition showed that the hydrogen production efficiency over the Au‐Pd‐SBA‐15‐NH2 catalysts increased with the increase in the surface amine content of the Au‐Pd‐SBA‐15‐NH2. The optimal Au‐Pd‐SBA‐15‐NH2‐TD (toluene/DMF = 3:2) catalyst proved to have the smallest Au‐Pd bimetal nanoparticle size and exhibited a turnover frequency (TOF) = 631 hours−1 and an activation energy (Ea) = 20.8 kJ · mol−1 at 25°C. The catalytic performance of hydrogen generation from the formic acid was improved due to the synergistic effect between the amine‐functionalized SBA‐15 and the Au‐Pd bimetal (metal‐support interactions).
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".