Gallic Acid Derived Palladium(0) Nanoparticles: An <i>In Situ</i> Formed “Green and Recyclable” Catalyst for Suzuki‐Miyaura Coupling in Water
Bibliographic record
Abstract
Abstract Herein, we report a facile methodology for the Suzuki‐Miyaura coupling in water. The method involved use of gallic acid (a natural and abundant phytochemical) reduced palladium(0) nanoparticles (PdNPs) formed in situ. The catalyst acts efficiently at low loading with short reaction time, further can be reused effectively till four recycles. Importantly, a wide range of functional groups were found to be compatible with the given reaction conditions. The use of gallic acid influenced both rate of the catalytic reaction and size distribution of the PdNPs. The size, morphology and distribution of the in situ‐formed nanoparticles were determined by UV‐Vis, Transmission Electron Microscopy (TEM), X‐ray diffraction (XRD) patterns, Scanning electron microscopy (SEM), and energy dispersive X‐ray spectroscopy (EDS) analysis, which showed a uniform gallic acid coated aggregation of palladium particles. Since gallic acid is a non‐toxic phytochemical, the present method highlights an efficient alternative for the utilization of natural feedstock.
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 | 0.001 |
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".