Development of nanostructured green divalent manganese‐coordinated polyurea
Bibliographic record
Abstract
Abstract The work presented here reports the greener synthesis of polyurea (GPUa) via in‐situ solvent‐free precipitation polymerization of toluene diisocyanate (TDI) (used as monomer) and water. The divalent manganese [Mn(II)] ions were incorporated into GPUa to form Mn(II)‐GPUa to enhance its applicability. The structure of GPUa and Mn(II)‐GPUa was validated by Fourier‐transform infrared and proton nuclear magnetic resonance spectral techniques. The morphology along with the elemental analysis of GPUa and Mn(II)‐GPUa was assessed by X‐ray diffraction analysis and field emission scanning electron microscopy along with energy‐dispersive X‐ray spectroscopy. Thermal degradation behavior and stability of GPUa and Mn(II)‐GPUa were investigated by thermo‐gravimetric, differential thermal, differential scanning calorimetry, and integral procedure decomposition temperature analysis. Furthermore, the batch adsorption method was used to examine the adsorption behavior of Mn(II)‐GPUa. The study's results indicated that Mn(II)‐GPUa might be used as an eco‐friendly, thermally stable material and adsorbent, making it suitable as an effective dye adsorbent for wastewater treatment. The current work is aligned with Green chemistry principles (Principles 1, 2, 3, 4, 5, 6, 8, and 12).
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 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) | 0.000 | 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 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".