Copper Removal Using Electrosterically Stabilized Nanocrystalline Cellulose
Bibliographic record
Abstract
Removal of heavy metal ions such as copper using an efficient and low-cost method with low ecological footprint is a critical process in wastewater treatment, which can be achieved in a liquid phase using nanoadsorbents such as inorganic nanoparticles. Recently, attention has turned toward developing sustainable and environmentally friendly nanoadsorbents to remove heavy metal ions from aqueous media. Electrosterically stabilized nanocrystalline cellulose (ENCC), which can be prepared from wood fibers through periodate/chlorite oxidation, has been shown to have a high charge content and colloidal stability. Here, we show that ENCC scavenges copper ions by different mechanisms depending on the ion concentration. When the Cu(II) concentration is low (C0≲200 ppm), agglomerates of starlike ENCC particles appear, which are broken into individual starlike entities by shear and Brownian motion, as evidenced by photometric dispersion analysis, dynamic light scattering, and transmission electron microscopy. On the other hand, at higher copper concentrations, the aggregate morphology changes from starlike to raftlike, which is probably due to the collapse of protruding dicarboxylic cellulose (DCC) chains and ENCC charge neutralization by copper adsorption. Such raftlike structures result from head-to-head and lateral aggregation of neutralized ENCCs as confirmed by transmission electron microscopy. As opposed to starlike aggregates, the raftlike structures grow gradually and are prone to sedimentation at copper concentrations C0≳500 ppm, which eliminates a costly separation step in wastewater treatment processes. Moreover, a copper removal capacity of ∼185 mg g(-1) was achieved thanks to the highly charged DCC polyanions protruding from ENCC. These properties along with the biorenewability make ENCC a promising candidate for wastewater treatment, in which fast, facile, and low-cost removal of heavy metal ions is desired most.
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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".