A Facile Strategy to Prepare Small Water Clusters via Interacting with Functional Molecules
Bibliographic record
Abstract
Although small water clusters (SWCs) are important in many research fields, efficient methods of preparing SWCs are still rarely reported, which is mainly due to the lack of related materials and understanding of the molecular interaction mechanisms. In this study, a series of functional molecules were added in water to obtain small water cluster systems. The decreasing rate of the half-peak width in a sodium dodecyl sulfate (SDS)–water system reaches ≈20% at 0.05 mM from 17O nuclear magnetic resonance (NMR) results. Based on density functional theory (DFT) and molecular dynamics (MD) simulation calculation, it can be concluded that functional molecules with stronger negative electrostatic potential (ESP) and higher hydrophilicity have a stronger ability to destroy big water clusters. Notably, the concentrations of our selected molecule systems are one to two magnitudes lower than that of previous reports. This study provides a promising way to optimize aqueous systems in various fields such as oilfield development, protein stability, and metal anti-corrosion.
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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 | 0.000 |
| 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) | 0.001 | 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".