Recent advances in layered double hydroxides for pharmaceutical wastewater treatment: A critical review
Bibliographic record
Abstract
In recent decades, pharmaceuticals, lauded for saving millions of lives, have surfaced as a new class of environmental contaminants. These compounds, originating primarily from hospital and industrial settings, often resist traditional treatment technologies and can persist in the environment for extended periods. The scarcity of water resources underscores the urgent need for innovative strategies for the effective management of pharmaceutical wastewater. Recently, layered double hydroxides (LDHs) have garnered considerable attention for their application in the remediation of pharmaceutical wastewater. This review explores the recent advancements in LDH-based adsorbents and membranes for pharmaceutical wastewater treatment. LDHs demonstrate superior adsorption capabilities due to their intercalation properties and structural versatility, effectively removing pharmaceutical contaminants such as antibiotics and anti-inflammatory drugs. Moreover, LDH-modified membranes enhance separation efficiency by improving permeability, selectivity, and fouling resistance. Advanced analytical techniques, including machine learning and synchrotron radiation, have provided deeper insights into the LDH mechanisms. However, challenges such as metal leaching, low mechanical durability, and limited scalability remain critical hurdles. Future research should focus on optimizing LDH stability, integrating adsorption with membrane separation techniques, and exploring hybrid treatment strategies. The recovery of valuable pharmaceuticals through LDH-based systems also presents a sustainable approach to wastewater management. This review highlights the potential of LDHs in pharmaceutical wastewater treatment while identifying key points for further development to enhance their practicality and large-scale application.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".