Toward mechanically robust and highly recyclable adsorbents using 3D printed scaffolds: A case study of encapsulated carrageenan hydrogel
Bibliographic record
Abstract
Biopolymeric hydrogels have emerged as promising materials for water treatment; however, they exhibit limitations in terms of mechanical robustness and durability. To address these shortcomings, we implemented several strategies, such as (i) incorporation of graphene oxide (GO) to expand the range of molecular interactions within the hydrogels, (ii) increasing the degree of carrageenan biopolymer crosslinking by elevating the temperature of the ionic crosslinking bath, thereby enhancing the mechanical robustness of the hydrogels, and lastly, (iii) encapsulation of the nanocomposite hydrogels within 3D printed scaffolds to enhance the hydrogel durability. This study introduces the first adsorbent based on encapsulated hydrogels for water treatment, demonstrating a remarkable increase in its reusability, surpassing previous reports by at least 400 %. Through the optimization of the 3D printed scaffold design, we achieved a 140 % increase in the mass of encapsulated hydrogel, and engineered the available surface area to enhance both the durability and the environmental performance of the hydrogels. The addition of GO increased the adsorption capacity to 166.1 mg/g and the storage modulus at 10 Hz to 12.48 kPa, representing a 55 % and 305 % enhancement compared to the neat hydrogel, respectively. Moreover, the higher degree of ionic crosslinking further increased the storage modulus of the hydrogel by 261 %. Increasing the degree of crosslinking resulted in a lower hydrogel swelling ratio, improved chemical stability, and increased the reusability of the hydrogel beads. Hydrogel encapsulation significantly increased the chemical stability and reusability of the adsorbents. More than 90 % of the initial mass of the encapsulated hydrogel remained intact after 20 regeneration cycles. The reported results present a promising avenue toward the industrial-scale application of sustainable and green hydrogels for water treatment.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".