Environmental behavior, toxic potencies, and risks of liquid crystal monomers: A critical review
Bibliographic record
Abstract
Liquid crystal monomers (LCMs), prized for their unique optical properties, are ubiquitous in a range of electronic products. However, their growing use and disposal have led to a continuous influx of LCMs into the environment as contaminants. This review synthesizes information on the sources, environmental distribution, migration, transformation, toxicity, and risks associated with LCMs. It also introduces predictions of adverse outcomes related to protein binding potential, grounded in the Adverse Outcome Pathway framework. It was pointed out for the first time that the fundamental causes of LCM contamination were informal recycling and dismantling patterns, coupled with obsolete liquid crystal processing technologies. The significant variability among different types of LCMs in distribution patterns, environmental persistence, bioaccumulation, mobility, and toxicity were emphasized. Notably, fluorinated LCMs, especially fluorobiphenyls, which posed the greatest comprehensive risk, were prone to accumulate in atmospheric dust. Our molecular docking results showed that monomers containing cyano groups, which had greater direct toxicity, carcinogenic, and mutagenic risk, also exhibited strong binding affinity, underscoring the need for priority control strategies. Additionally, this review delved into LCM exposure pathways and the heightened toxicity during degradation and metabolism. It emphasizes the importance of risk assessments for LCMs and identifies key scientific questions that require further investigation. The insights provided a scientific foundation for preventing environmental risks and promoting green chemical alternatives related to LCMs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".