Switchable biomaterials for wastewater treatment
Bibliographic record
Abstract
The development of novel switchable biomaterials has gained significant attention due to their potential in wastewater treatment, offering sustainable and cost-effective solutions for contaminant removal. This thesis presents a comprehensive review of the diverse applications of switchable biomaterials, including chitosan, polylactic acid (PLA), cellulose, biochar, rubber, resin, and crude fibers. These materials exhibit stimulus-responsive functionalities that facilitate recyclability and pollutant recovery, making them promising candidates for environmental remediation. Despite substantial advancements, challenges such as stability, recyclability, and performance optimization remain. Future research should focus on improving these aspects while exploring novel hybrid materials to enhance their applicability in real-world scenarios. Based on the research gaps identified by the literature review, the thesis work further focuses on the development and optimization of a spiropyran-assisted cellulose aerogel (CNF-SP) aerogel with UV-induced switchable wettability, and the evaluation of its performance as an effective sorbent for oil spill cleanup. Beyond oil spill remediation, the switchable properties of the aerogel hold great potential for broader wastewater treatment applications, particularly in selectively adsorbing hydrophobic and hydrophilic contaminants. The aerogel initially exhibited strong hydrophobicity (124°) and showed UV-induced switchable wettability due to the photo-response structure of spiropyran. Upon UV irradiation, the hydrophobicity of the aerogel could be switched to hydrophilicity (31°), while visible light irradiation could restore its hydrophobicity. The three-dimensional (3D) porous structure of the CNF-SP aerogel combined with the hydrophobic properties of spiropyranol led to its great oil adsorption performance (27-30 g/g of oil adsorption ratio). To systematically optimize the material, the central composite design (CCD) was applied, as it allows for efficient exploration of the interaction effects among multiple factors. The raw materials, including carboxymethyl cellulose, carboxyethyl spiropyran, polyvinyl alcohol, and nano zinc oxide, were specifically chosen due to their roles in enhancing mechanical stability, responsiveness, and adsorption capacity. The optimized CNF-SP aerogel demonstrated a high oil sorption efficiency, particularly in acid and cold environments. Moreover, the switchable function indicated that the aerogel exhibited reusability and renewability, with the added benefit of UV-induced oil recovery. However, potential limitations, such as the scalability of the synthesis process and real-world deployment challenges, remain key concerns that require further investigation. Through the development of the CNF-SP aerogel, this thesis directly addresses challenges in oil spill remediation by offering a material capable of adapting to diverse environmental conditions while ensuring high oil adsorption efficiency and sustainability. The study underscores the transformative potential of switchable biomaterials in mitigating the environmental impact of water pollution, reaffirming their role as a critical advancement in the field of wastewater 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".