Effect of Molybdenum Trioxide in the Behavior of Poly(vinyl alcohol) Nanocomposites Systems Focusing New Systems for Protection against COVID-19
Bibliographic record
Abstract
The purpose of this work was to study the molecular dynamics, morphology, mechanical and thermal performance of nanomaterials formed by poly(vinyl alcohol) and molybdenum trioxide (PVA/MoO3) obtained through solution casting method, focusing new materials with therapeutic applications since the molybdenum trioxide exhibit an excellent antibacterial activity and could be a pathway to prevent viruses. The obtaining materials were characterized by conventional techniques as X-ray diffraction, thermogravimetric and dynamical-mechanical analysis. The unconventional low-field NMR relaxometry was used to evaluate the molecular dynamic and morphology of these systems. The results obtained showed that the MoO3 addition into PVA matrix promote an increase on the thermal stability at higher temperatures and a progressive increase on the rigidity of the PVA systems. Also changes in the molecular mobility of nanomaterials determined through the proton spin-lattice relaxation time showed that low proportion of molybdenum trioxide increased the intercalation of the poly(vinyl alcohol) chains between oxide lamellae while higher quantity of molybdenum trioxide caused an inverse effect on the oxide lamellae delamination. From those results the nanomaterials presented a mixed structural organization as intercalated and exfoliated morphologies. According to these first results, the nanocomposites obtained promise to be antimicrobial and antiviral agent to prevent COVID-19 and similar viruses.
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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".