Development of Acrylamide/Acrylic acid Superabsorbent Polymer (SAP) Particles for Water Applications
Bibliographic record
Abstract
Water contamination, particularly from bacterial sources, poses significant risks to public health and environmental safety. Low bacterial concentrations in water samples often go undetected by conventional methods, increasing these risks and highlighting the need for efficient sample enrichment techniques. This research investigates the use of superabsorbent polymer microparticles (SAP-MPs) and their nanocomposites with MXene nanosheets for enriching bacterial samples. The study first focuses on developing innovative microfluidic platforms for real-time characterization of SAP-MPs. Subsequently, advanced MXene/SAP-MP nanocomposites are introduced for enhanced bacterial enrichment and water analysis. The study is structured into four objectives. The first objective involves the design and fabrication of a novel microfluidic device for high-resolution, real-time characterization of SAP-MPs. This device enables detailed single particle analysis of swelling behaviors, including volume swelling ratio (VSR) and swelling rate (SR). The second objective investigates the effect of particle size, crosslinker concentration, acrylic acid concentration, and neutralization degree on the swelling behavior of SAP-MP. Results revealed a ~40% and ~300% reduction in equilibrium VSR (VSReq) and SR with increased crosslinker concentration, respectively, while increasing acrylic acid concentrations enhanced VSReq and SR by ~200%. A ~300% increase in VSReq was observed with smaller particle sizes, marking the first single-particle-scale study of this phenomenon. The third objective demonstrates the synthesis of MXene/SAP-MP nanocomposites using the Breathing-In-Breathing-Out (BI-BO) method, achieving successful integration of MXene nanosheets without compromising swelling behavior. Finally, the fourth objective evaluates bacterial enrichment performance, revealing a 10-fold enrichment efficiency and 90% recovery efficiency under optimized conditions. This research advances the fundamental characterization of SAPs and their applications, including water treatment, biosensing, and environmental monitoring.
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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".