Preconcentration and Separation of Mn(II) from Eenvironmental Water Samples on N,N-bis (Salicylidene) Cyclohexanediamine Functionalized Amberlite XAD-4 Resin and Its Spectrophotometric Assessment
Bibliographic record
Abstract
A new and sensitive preconcentration procedure was developed for the determination of trace amount of Mn(II) in environmental water samples. Amberlite XAD-4 resin was functionalized with N,N-bis(salicylidene)cyclohexanediamine (SCHD) to preconcentrate and separate Mn(II) at the trace level concentrations. Mn(II) ion in environmental water samples was determined by the formaldoxime spectrophotometric method. The influences of analytical parameters including pH and volume of sample, type and concentration of elution solution, flow rate of sample and elution solution, etc. were investigated on the recoveries of Mn(II) ion. The adsorption was achieved quantitatively for Mn(II) on XAD-4-SCHD resin at the pH range of 3–10 and 3 mL min–1 sample flow rate. Elution was performed with 0.5 mol L–1 nitric acid solution. Under the optimized conditions, calibration curve was found to be linear in the concentration range of 0.1–5 mg L–1 with a limit of detection of 0.65 μg L–1. The effect of foreign ions on the perconcentration and determination of Mn(II) was studied and no adversely affecting ion was observed even at high ionic media. The recovery value was above 90%, with an enrichment factor of 100. The per cent relative standard deviation (%) value was found to be 1.8 for twenty repetitive measurements containing 1 mg L–1 of Mn(II). Sorption capacity of the Mn(II) ion on the XAD-4-SCHD resin was found to be 29.217 μg. The accuracy of the presented preconcentration and determination method was checked by the analysis of TMDA-70.2 Ontario lake Water certified reference material and synthetic water sample. The method was applied for the analyses of tap water and industrial wastewater samples for Mn(II). Almost the similar results were observed for the Mn(II) determination in different environmental water samples using both the proposed and ICP-MS methods.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".