<font>Fe–Al</font> NANO-OXIDE PREPARED BY SOL–GEL METHOD USING PRECURSOR OF <font>HCl</font> DIGESTED LIQUID FRACTION OF LATERITE: ARSENIC ADSORPTION PERFORMANCE
Bibliographic record
Abstract
Nanoparticle oxide of Fe–Al with surface area 68.9 m2/g and pore volume of 0.10–0.11 mL/g is synthesized using Fe–Al precursor obtained from HCl digested liquid fraction of laterite. Acid digestion of laterite is performed using solid acid ratio of 50 g raw laterite to 200 mL 6 N HCl . The liquid fraction is filtered through Whatman filter of grade 1 and 200 mL filtrate consists mainly of ~ 0.5 mol/L Fe and ~ 0.11 mol/L Al ions. Sol–gel method is used to prepare nano-oxide of Fe–Al . SEM, HRTEM and surface area analyzer are used for textural characterization of the nanoparticles. HRTEM micrograph indicates that sizes of prepared nanoparticles of Fe–Al oxide are in the range of 50 nm to 100 nm. In batch mode operation, 1.5 g/L adsorbent concentration is found to be capable to reduce the arsenic concentration of contaminated groundwater (collected from Dhobdhobi, Mallikpur, 24 Paraganas (s), West Bengal, India) from ~ 440 to ~ 11 μg/L. The Langmuir maximum capacities of As(V) and As(III) from synthetic solution and arsenic (as total) from contaminated groundwater on Fe–Al nano-oxide are obtained as 20.74 mg/g, 6.13 mg/g and 6.8 mg/g, respectively.
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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".