Physicochemical and Spectroscopic Evaluation of Adsorption potentials of Activated Charcoal from stem parts of Mangifera indica, Persea gratissima and Psidium guajava for Pharmaceutical medicine
Bibliographic record
Abstract
Activated charcoal which comes in synonyms of; carbon, charcoal, medicoal and Norit is very useful in the field of pharmaceutical medicine and in industries. The medicoal samples were sourced for this study from Nigerian plants, namely; Mangifera indica (Mangoe tree), Psidium guajara (Guava tree) and Persea gratissima (Avocado tree). Samples were prepared by carbonization and thermal activation prior to characterization evidence such as percentage of carbonized residues, ash values, fixed carbon contents, pH of aqueous solution, and limits of heavy metal presence. Liquid phase study was conducted by adoption of Freundlich adsorption model. Following this, data were generated for adsorption profile of methylene Blue (MB) on the various samples of activated charcoal inclusive of the commercial grade (standard). Ultra violet spectroscopy was used to measure the respective adsorption capacities. The results were statistically significant using a one-tail (ANOVA) at confidence level of P < 0.05. Persea gratissima ranked the highest in performance out of the three test samples with MB adsorption (9.95 mg/g) and compared favourably with the standard (9.96mg/g). Psidium guajava exhibited the least performance (1.04mg/g). All samples showed desirable aqueous pH of range (6-8). The research has shown that low cost and easily accessible, locally sourced activated charcoal is a recommendable alternative to the commercially available products.
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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) | 0.000 | 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".