Studies on Novel Zerovalent Iron Decorated Cellulose Nanofibers for Defluoridation
Bibliographic record
Abstract
Water is an essential and important component of the universe and it plays a pivotal role in the proper functioning of the earth's ecosystems. In spite of the several water bodies, potable drinking water is not readily available for millions of people around the world. The main cause of this scenario could be associated with rapid industrialization and population growth. Over seven hundred inorganic and organic micropollutants in water have been reported. Some of these micropollutants are highly carcinogenic and toxic while some have very long residence times in the environment and are neither biotransformable nor biodegradable. Several techniques are available to remove these contaminants from potable water and industrial wastewater which include conventional coagulation, chemical precipitation, ion-exchange, reverse osmosis, electrolysis, electrodialysis, and adsorption. Among these, Adsorption, is a cost effective technique that removes both organic and inorganic contaminants from water. The most widely adsorbents for water treatment are activated carbon, activated alumina (1), and biosorbents like cellulose, chitosan (2). It is well known that cellulose is a natural biodegradable polysaccharide is bestowed with very good features to promote diverse applications. This naturally occurring polysaccharide, (C6H10O5)n, has anhydroglucose rings as the repeat unit with a -1,4 glucosidic bond. The excellent properties attributed to this polysaccharide such as biodegradability, good stability, intramolecular hydrogen bonding, etc. prompted us to explore its potential for the effective interaction with contaminants (3). Indeed conversion of natural macro fibers to nano fibers would result in increased surface area and increased sorption. The combination of nanotechnology and chemical modification is an alternative approach offering a new technological platform to improve the binding efficiency of biobased adsorbents. As the size of biosorbents is reduced to nanoscale, the huge increase in the specific area is expected to provide an enhanced density of binding sites on the adsorbent Further, doping of nano particles of zerovalent iron, will further improve the sorption capacity of the sorbent towards the contaminants. It is well known that nano zerovalent iron exhibits high metal scavenging properties owing to the reactive nature of iron present in the zerovalent state. Recently, zerovalent iron (ZVI) has been found useful for the rapid adsorption of As(III) and As(V) (4) and its reactivity was found to increase in smaller dimensions i.e., nanoscale zero-valent iron (NZVI). However, because of their advanced dispersive properties, these nano adsorbents with small dimensions are difficult to recycle and may lead to loss of the adsorbent and bring about secondary pollution to the environment (5, 6). To overcome these difficulties, embedding of nanoparticles in the membranes with high surface area would lead to practical environmental applications.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".