Speciation, Preconcentration and Determination of Inorganic Chromium Species in Spring, Drinking, and Waste Water Samples
Bibliographic record
Abstract
In general, chromium is existed in stable oxidation states, such as Cr(III) and Cr(VI) in the environment. While a certain amount of Cr(III) is required for human metabolism, Cr(VI) is known to have toxic and carcinogenic effects for all living species. Chromium compounds are widely used in different fields, such as electroplating, dyeing, photographic and textile industries. Owing to this reason, it and its compounds can easily interfere with the water resources. Because of differences in toxicity, it is important to develop a sensitive analytical method for the speciation, preconcentration and determination of chromium species in the water resources. In this work, speciation and preconcentration of Cr(III) and Cr(VI) were performed using Amberlite CG-120 resin before detection step by flame atomic absorption spectrometry. The experimental conditions for model solutions were optimized so that Cr(III) was adsorbed in a column while Cr(VI) was not. To perform this, some experimental parameters, such as pH, eluent’ type/concentration/flow rate, sample solution flow rate and adsorbent amount were optimized. Furthermore, Cr(VI) was reduced to Cr(III) to determine total chromium. Then, Cr(VI) was calculated by the differences between total chromium and Cr(III). The limit of detection (3 s) and preconcentration factor were found to be 0.3 μg L–1 and 600, respectively. This proposed method was successfully applied for the determination of inorganic chromium species in the different spring waters supplied from Isparta province (Eyüpler Village), Burdur province (Ağlasun County), the commercial drinking water samples purchased from local market in Burdur province, and the waste water supplied from Isparta province (Suleyman Demirel Organized Industrial Region) at Turkey. The accuracy of the method was successfully checked by certified reference material (TMDA-70.2 Ontario Lake water) at 95% confidence level.
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".