Rational Design of Zeolites to Remove Siloxane-Related Pollutants with High Adsorption Loading and Enhanced Adsorption Energy
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Though siloxanes and their derivatives have been widely used, they are emerging and persistent pollutants in water systems. Developing high-performance and low-cost adsorbents to remove siloxane-related pollutants is an essential strategy for removing these contaminants. Through Grand Canonical Monte Carlo (GCMC) simulations, we computed and evaluated the adsorption performances of 246 experimentally available zeolite frameworks toward three silanols, namely, trimethylsilanol (TMS), dimethylsilanediol (DMSD), monomethylsilanetriol (MMST), and the coexisting contaminant in siloxane-impacted environments, dimethylsulfone (DMSO 2 ), and obtained the best sorbents for each pollutant. To seek multifunctional zeolites, we first screened out the top 10 zeolite frameworks based on the loading values, among which the framework RWY showed the best performance. We further demonstrated that introducing dopants can enhance adsorption performance by taking RWY as an example. This work not only identified the most promising zeolite frameworks for removing linear siloxanes and derivatives, but also provided a relatively efficient and practical computational approach for screening sorbent materials for other emerging pollutants, balancing accuracy with tractable computational cost.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".