Environmental behavior, toxic potencies, and risks of liquid crystal monomers: A critical review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.017 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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 it