Recovery of Trace Metal Isotopes in Seawater Samples Using Multifunctional Neem (<i>Azadirachta indica</i>) Biosorbent: A Comparison with Monofunctional NOBIAS–Chelate–PA1 Resin
Bibliographic record
Abstract
Neem ( Azadirachta indica ) tree leaves (NL) represents a potential alternative to costly conventional materials for recovery of trace metals from seawater. Metal uptake increased in the following order: hard metals ( 44 Ca, 24 Mg, and 88 Sr) < borderline metals ( 55 Mn, 59 Co, 61 Ni, 63 Cu, and 66 Zn), and soft metals ( 208 Pb and 111 Cd) based on covalent bond formation with biological ligands. Improved removal of major ions was achieved using NL instead of the monofunctional (−N(CH 2 COOH) 2 ) NOBIAS–Chelate–PA1 resin. However, major ion (1.5–500.8 mg/kg) and heavy metal concentrations were much higher (1.0 × 10 2 to 1.0 × 10 5 μg/kg) in fresh NL compared to the metal concentrations (0.02–98.2 ng/kg) in seawater. Therefore, before use, the freeze-dried NL was subjected to a rigorous acid cleaning process using HCl, HNO 3, and buffer (NH 4 COOCH 3 ) to remove heavy metals and major ions. Trace metal recovery levels ascertained using inductively coupled plasma-mass spectrometry (ICP-MS) were in the range of 91–115%, with 99.999% of the matrix ions removed. For each trace metal isotope, the procedure blank was <10% of the mean concentration of the seawater, and the precision was 5–8% RSD. The use of reference seawater (NASS-6) was extremely valuable in validating the metal-free condition of the NL and for data quality control. The current adsorption capacities of NL are relatively low; however, biological ligands have excellent potential for rapid separation of trace metals from the seawater matrix.
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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".