Ionotronics Based on Horizontally Aligned Carbon Nanotubes
Bibliographic record
Abstract
Abstract Controlled ion transport through ion channels of cell membranes regulates signal transduction processes in biological systems and has also inspired the thriving development of ionic electronics (ionotronics or iontronics) and biocomputing. However, for constructing highly integrated ionic electronic circuits, the integration of natural membrane‐spanning ion channel proteins or artificial nanomembrane‐based ionic diodes into planar chips is still challenging due to the vertically arranged architecture of conventional nanomembrane‐based artificial ionic diodes. Here, a new design of ionic diode is reported, which allows chip‐scale integration of ionotronics, based on horizontally aligned nanochannels made from multiwalled carbon nanotubes (MWCNTs). The rectification of ion transport through the MWCNT nanochannels is enabled by decoration of oppositely charged polyelectrolytes on the channel entrances. Advanced ionic electronic circuits including ionic logic gates, ionic current rectifiers, and ionic bipolar junction transistors (IBJT) are demonstrated on planar nanofluidic chips by stacking a series of ionic diodes fabricated from the same bundles of MWCNTs. The horizontal arrangement and facile chip‐scale fabrication of the MWCNT ionic diodes may enable new designs of complex but monolithic ionotronic systems. The MWCNT ionic diode may also prove to be an excellent platform for investigation of electrokinetic ion transport in 1D carbon materials.
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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".