Development of polyacrylamide composite hydrogel for removal of humic acid based on interaction studies
Bibliographic record
Abstract
In the field of wastewater treatment and environmental remediation, tried and true materials such as the hydrophilic polymer, polyacrylamide (PAM), are taking a backseat to emerging technologies like metal organic frameworks (MOFs), whose ultrahigh porosity and surface area make them a very exciting and attractive subject matter. However, there’s still much that isn’t known about how PAM interacts with major constituents of the environment, one of which is humic acid (HA), such interactions ultimately determine PAM’s behavior, transport, and remediation performance. Having a clearer picture of the interactions between PAM and HA is important because it can help optimize wastewater treatment and agricultural operations, as well as influence the development of efficient technologies in those industries. In this thesis work the fundamental molecular interactions between PAM and HA were determined to better understand the role and evolution of PAM in the environment. PAM was found to not have a strong affinity for HA and low adsorption capacities. The low performance of PAM gels, as predicted by the interaction studies, was then addressed by the integration of a metal organic framework (Zeolitic Imidazolate Framework 8, ZIF-8) into the gel matrix. The composite PAM-ZIF-8 hydrogel demonstrated good adsorption capacities compared to pure PAM hydrogels, and easy handling and application compared to pure ZIF-8 powder. The results of this work show that the PAM-ZIF-8 composite is a promising new material for the treatment of polluted waters that may be further developed and improved. The thesis demonstrates how a fundamental understanding of the inter-molecular and surface interactions of environmentally complex systems can be obtained at a micro and macroscopic scale, and further used to provide scientific guidance on the development of novel materials/devices for wastewater remediation.
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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".