Emerging MOF-based antibiotic detection methods in water environments: Recent advances, challenges, and prospects
Bibliographic record
Abstract
The misuse and improper disposal of antibiotics lead to the emergence and dissemination of antibiotic-resistant bacteria and antibiotic resistance genes, posing serious threats to water environmental safety and human health. Thus, developing efficient detection methods for residual antibiotics in water environments is crucial for pollution control and public health safety. Traditional methods for detecting antibiotics including chromatographic methods, microbiological methods, and immunoassays, suffering from issues such as complex procedures, the requirement for skilled operators, and high costs, greatly limiting their applicability in in-situ monitoring. Recently, rapid detection methods, including colorimetric, fluorescence, biosensors, and paper-based detection, have received widespread attention, overcoming the aforementioned limitations and being widely adopted. Metal-organic frameworks (MOFs) possess distinct advantages, such as enrichment adsorption, catalytic degradation, and self-generated fluorescence, making them highly promising in the field of rapid antibiotic detection. Herein, the detection principles and recent advances in rapid antibiotic detection methods based on MOFs are summarized, and the unique strengths and potential of MOFs in the field of rapid antibiotic detection are highlighted. Notably, recent progress and challenges in high-throughput computing (HTC) and machine learning (ML) for screening MOFs for specific applications is discussed, and strategies for their use in MOF-based rapid antibiotic detection methods are proposed. This comprehensive review may further guide the development and optimization of antibiotic detection methods utilizing MOFs and promote their practical applications for sensing environmental antibiotics. • Recent advances of current antibiotic detection methods are listed. • The application potential of MOFs in rapid antibiotic detection are clarified. • Challenges and future trends for MOF-based antibiotic detection are highlighted. • The potential of machine learning for MOF-based antibiotic detection is emphasized.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".