Evidence for the Augmented Cd(II) Biosorption by Cd(II) resistant strain Candida tropicalis XTA1874 From Contaminated Aqueous Medium
Bibliographic record
Abstract
Abstract Cadmium is one of the most dreadful heavy metals and is becoming a major toxicant in ground water with increasing concentration above the WHO Guidelines in drinking water (0.003ppm). The conventional physico-chemical remediation techniques are not favorable in removing the contaminants such as Cd(II) fruitfully. Bio-based techniques used in Cd(II) removal from contaminated water provide cheap, eco-friendly and efficient means of environmental remediation. The present study is aimed at determining the optimized nutritional conditions for the optimal growth and Cd(II) remediation capacity for a developed Cd(II) resistant yeast strain named Candida tropicalis XTA 1874 isolated from contaminated water-body in West Bengal. By analyzing the optimization conditions, a synthetic medium was developed and the composition has been given in the main text. The biosorption process followed pseudo second order kinetics proving chemisorption as the rate limiting step in the adsorption process. The equilibrium biosorption of Cd(II) by the strain was fitted to the Langmuir model and hence the biosorption was a monolayer process both before and after optimization. The strain showed much better Cd(II) adsorption capacity under the optimized nutritional conditions (qmax=847.476±0.008 mg/g). The above findings amply demonstrated the strain to be an effective biosorbent in removing Cd(II) from contaminated water.
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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".