Synthesis of BaCO<sub>3</sub> particles tailored by carboxylated cellulose fibers
Bibliographic record
Abstract
Abstract The present work deals with the use of carboxylated cellulose fibers, thereafter called Cellulose Nano Fibers (CNF), as eco-friendly materials and sustainable resources for remediation and valuation, of metal ions present in contaminated water. Barium divalent cation (Ba2+) is among the most common heavy metal pollutants found in water such as arsenic, cadmium, chromium, copper, nickel, lead and mercury. Thus, CNFs containing carboxylate groups were first prepared by the oxidation of the cellulose fibers using the NaOCl-NaBr-TEMPO system, and thereafter, they were used as adsorbent for Ba2+ ions removal from BaCl2 aqueous solutions. Such Ba2+ removal was evidenced by the decrease of both the CNF surface charge and the CNF absorbance, upon the addition of the metal divalent cation, BaCl2 to the CNF aqueous dispersions. It is shown that the interaction occurring between the fiber carboxylic groups and the positively charged metal ions, Ba2+, occurs via electrostatic interactions and leads to the CNFs –Ba2+ complex formation. In the second part of this work, we investigated the effect of the CNFs on the crystallogenesis of barium carbonate particles, BaCO3. Thus, the CNFs aqueous solution was first added to barium chloride BaCl2 solution, and the mixture was stirred for a period of time (complexation time), prior the addition of sodium carbonate (Na2CO3) solution. The precipitation of barium carbonate particles in the presence of CNFs occurred at ambient temperature, in a water-jacketed Pyrex glass vessel. Finally, the resulting Barium carbonate-CNFs composites were examined by various methods such as Scanning Electronic Microscopy (SEM), and X-ray Diffraction. From the SEM data, two BaCO3 morphologies were observed, dendritic and bent or rods particles, respectively, in the absence and in the presence of the CNFs.
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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".