Reviewing Perovskite Oxide-Based Materials for the Effective Treatment of Antibiotic-Polluted Environments: Challenges, Trends, and New Insights
Bibliographic record
Abstract
Society confronts the pressing environmental challenges posed by the pervasive presence of toxic pollutants in aquatic ecosystems. The repercussions of contaminant release extend far and wide, endangering marine life and human well-being. While various techniques such as bioremediation, filtration, and adsorption have been employed for wastewater treatment, they grapple with cost effectiveness and overall efficiency issues. Advanced oxidative processes, including photocatalysis and Fenton, have emerged as viable solutions in response to the emerging contaminants. However, the efficacy of photocatalysis largely hinges on the choice of catalyst. Their distinctive attributes, such as chemical defects and exceptional stability, make perovskite oxides a promising catalyst. These materials can be synthesized through diverse methods, rendering them versatile and adaptable for widespread applications. Ongoing research endeavors are diligently focused on enhancing the performance of perovskite oxides, optimizing their integration into catalytic processes, and exploring innovative approaches for material immobilization. This comprehensive review seeks to elucidate the most pivotal advances in perovskite oxides and their composites within the wastewater treatment domain. Additionally, it sheds light on burgeoning research trends and multifaceted challenges confronting this field, which present insights into techniques for treating the antibiotic-contaminated environment, delving into innovative strategies, green technologies, challenges, and emerging trends.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".