Regulate Ion Transport in Subnanochannel Membranes by Ion-Pairing
Bibliographic record
Abstract
The ability of biological ion channels to respond to environmental stimuli, regulate ion permeation rates, and selectively transport specific ions is essential for sustaining physiological functions and holds immense potential for various practical applications. In this study, we report a highly selective ion separation membrane capable of responding to ionic stimuli, thereby regulating the permeation rate of the target ions. This membrane is constructed from two-dimensional MXene nanosheets functionalized with γ-poly(glutamic acid) (γ-PGA) molecules. Its biomimetic ion channel structure provides spatial confinements, as well as ion recognition and response sites. Remarkably, the membrane demonstrates the ability to respond to stimulus ions, achieving regulation of target ion permeation rates by over 2 orders of magnitude and achieving a K + /Mg 2+ selectivity exceeding 10. 3 Unlike traditional nanochannel membranes, where ion transport is predominantly driven by ion-channel interactions, this membrane operates through an ion–ion interaction-dominated mechanism. The introduction of stimulus ions dynamically alters ion-pair formation within the subnanochannels, thereby modulating the permeation rates of target ions. This study provides a fresh perspective on ion transport mechanisms in nanoconfined environments, reflecting conditions closer to those in real-world systems. It underscores the pivotal role of ion–ion interactions in regulating ion transport and offers valuable insights into the design of next-generation ion separation membranes with tailored responsiveness.
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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 | 0.001 |
| 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".