Quantitative Assessment of Molecular Transport through Sub-3 nm Silica Nanochannels by Scanning Electrochemical Microscopy
Bibliographic record
Abstract
Scanning electrochemical microscopy (SECM) has been proved to be a powerful technique to study molecular transport across ionic channels in biomembranes and artificial nanoporous membranes. In this work SECM was used to study the dynamics of molecular transport across the ultrathin silica nanoporous membrane consisting of sub-3 nm diameter perpendicular channels. We focused on the quantitative assessment of permselectivity and permeability of the membrane and the effect of radial electrical double layer (EDL) on them. By SECM imaging, it was phenomenologically observed that the membrane with negatively charged surface exhibited permselectivity to anionic molecule, for instance hexacyanoruthenate(II) (Ru(CN)64–). And the permselective transport of Ru(CN)64– was obviously more favored at a higher concentration of KCl. Precise membrane permeability to Ru(CN)64– was quantitatively determined by overlapping experimental SECM approach curves with the ones generated by finite element simulations. The high permeability up to 35 μm s–1 was ascribed to the straight channel structure and ultrahigh channel density of 4 × 1012 cm–2. Moreover, the permeability was varied from 35 μm s–1 to 2.5 μm s–1 when decreasing the concentration of KCl from 1.0 to 0.01 M, corroborating the electrostatic origin of membrane permselectivity. On the other hand, the simulated concentration profiles at both sides of the membrane suggested that the molecular transport across the membrane was mainly driven by the large transmembrane concentration gradient while the tip-induced transport was relatively negligible. These results help to quantitatively understand the molecular transport selectivity and dynamics across nanoporous membranes and to rationally design artificial molecular sieving membranes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".