Modification of tamarind fruit shell powder with in situ generated copper nanoparticles by single step hydrothermal method
Bibliographic record
Abstract
Tamarind fruit shell powder (TFSP) with particle size of < 50 µm (obtained from cleaned tamarind fruit shells) was modified with in situ generated copper nanoparticles (CuNPs) by simple one step hydrothermal method. The modified TFSP was characterized by scanning electron microscope (SEM), Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis (TGA) and antibacterial tests. The generated stable CuNPs on the surface of the modified TFSP were spherical in shape with an average size of 88 nm. The FT-IR spectroscopy analysis indicated the involvement of the functional groups of the TFSP in the generation and stabilization of the CuNPs. The XRD analysis indicated the presence of both CuNPs and Cu2O nanoparticles in the modified TFSP. The thermal analysis indicated the presence of 5.6 wt% of copper nanoparticles as calculated from the difference of residual char content between the unmodified and modified TFSP. The modified TFSP with in situ generated CuNPs exhibited obvious antibacterial activity against both the Gram negative and Gram positive bacteria and hence can be considered as low cost filler in the preparation of antibacterial polymer hybrid nanocomposites for packaging and medical applications.
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".