Use of molecular simulation for evaluating adsorption equilibrium of inhalation anaesthetic agents on metal–organic frameworks
Bibliographic record
Abstract
Abstract Metal–organic frameworks (MOFs) were studied as alternatives to zeolites and activated carbon for adsorptive removal of wasted inhalation anaesthetic agents (IAA). Monte Carlo simulation was used to predict equilibrium adsorption isotherms of IAA on selected MOFs. Rather than generic forcefields (FFs), the all‐atom FF parameters published by Arcario were used for IAA modelling. Continuous fractional component Monte Carlo (CFCMC) proved crucial for speedy simulation of large molecules. We found that allocating 70% probability to the CFlambdaSwap move gave optimum fits between simulation and experiment. The simulations provided us with an insight into the adsorption mechanisms of IAA in these structures. Heats of adsorption, Brauner‐Emmet‐Teller (BET) surface area, and total pore volume were deduced to be the crucial parameters for low, medium, and high range of relative pressures in the isotherm. Therefore, the chromium atoms in MIL‐101‐Cr are better adsorbers of IAA than MIL‐100‐Al at lower pressures despite the similarities in terms of the type of linkers and topology. Our simulation results corroborated the earlier published studies on the self‐association behaviour of sevoflurane molecules based on the experimental isotherms reported for MOF‐177‐Zn. Finally, the high polarity of IAA is thought to explain good low‐pressure simulation/experiment data agreement for the MOFs possessing coordinatively unsaturated sites (CUS) despite using generic DREIDING FF for the framework atoms. Our in‐house parsing code helped realize that the grand‐canonical Monte‐Carlo simulation speed is not the same for all pressure points but decreases for higher pressure points. This can be explained by increased density of the adsorbates making successful trial moves less probable.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".