Ionic Selectivity Inside Solid-State Nanoscale Channels: Physics and Applications
Bibliographic record
Abstract
Abstract: Ionic selectivity across nanochannels is of great importance to biological activities as well as for designing novel molecular devices/sensors which has wide potential applications in nanotechnology. With the development of experimental and computational facilities and technologies, it becomes possible to study the ionic selectivity inside and across the nanoscale channels by both experiments and simulations. Because of the confinement effect, there are new physical phenomena in such nanotube that are not observed at macrofluidic or microfluidic size scales, including the anomalous hydration shell order, ionic selectivity based on the surface charge. These novel properties lay the foundation for the selectivity of the various types of ions. Several controlling methods for selecting ions are designed and developed, such as a controllable ion-selective nanopore (for selecting K+ and Na+) based on a single-walled carbon nanotube with specially arranged carbonyl oxygen atoms modified inside the nanopore, which was inspired by the structure of biological ionic channels; Nanopores in graphene sheets with different functional units have been applied to sieve cations of alkali metals or anions of group 17 element, respectively; The pore radius can also be determinant factor for choosing ions. Recently, the separation of the positive and negative ions has appealed more concerns. Inside of nanofluidic nanopores, negative and positive ions are manipulated analogously to semiconductor devices such as metal-oxide-semiconductor field effect transistors, where negative and positive charged carriers are manipulated. In this review, we examine some of the recent advances in the dynamics of the ionic selectivity inside solid state nanoscale channels.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".