Nanofer ZVI: Morphology, Particle Characteristics, Kinetics, and Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nanofer zerovalent iron (nanofer ZVI) is a new and innovative nanomaterial capable of removing organic as well as inorganic contaminants in water. It displays a decrease in agglomeration, when it is coated with tetraethyl orthosilicate (TEOS). TEOS imparts an increase in reactivity and stability to nanofer ZVI. Characteristics of nanofer ZVI were determined using scanning electron microscope/electron dispersive spectroscope (SEM/EDS), transmission electron microscope (TEM), and X‐ray diffraction (XRD). Nanoparticle size varied from 20 to 100 nm and its surface area was in the range of 25–30 m 2 g −1 . The present study examined its structure before and after kinetic experiments. Kinetic experiments indicated that adsorption of heavy metals [Pb (II), Cd (II), and Cu (II)] and TCE is very rapid during the initial step which is followed by a much slower second step. Removal rates of 99.7% for Pb (II), 99.2% for Cd (II), 99.9% for Cu (II), and 99.9% for TCE were achieved in less than 180 minutes. Lagergren models (LM), liquid film diffusion model (LFDM), and interparticle diffusion model (IDM) were used to understand the removal mechanism associated with nanofer ZVI. In this study, interactions of nanofer ZVI with individual metals as well as TCE are examined.
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.
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 it