Interfacial Superassembly of Mesoporous Titania Nanopillar-Arrays/Alumina Oxide Heterochannels for Light- and pH-Responsive Smart Ion Transport
Bibliographic record
Abstract
Stimuli-responsive nanochannels have attracted extensive attention in various fields owing to their precise regulation ability of ionic transportation. However, the poor controllability and functionality as well as responding to only one type of external stimulus still impede the development of the smart nanochannels. Here, we demonstrate a novel heterogeneous membrane composed of ordered mesoporous titania nanopillar-arrays/anodic aluminum oxide (MTI/AAO) using an interfacial superassembly strategy, which can achieve intelligent light and pH multimodulation ion transport. The MTI/AAO membranes are generated through the self-assembly of templates, followed by interfacial superassembly of micelles on AAO, and then the nanostructure and phase transformation of titania. The presence of the MTI layer with anatase crystal endows the heterogeneous membrane with an excellent light-responsive current density of 219.2 μA·cm-2, which is much higher than that of a reported traditional light-responsive nanofluidic device. Furthermore, the MTI/AAO heterogeneous membranes with an asymmetric structure exhibit excellent rectification performance. Moreover, pH-regulated surface charge polarity leads to a reversal of current rectification polarity. This light and pH multiresponsive membrane realizes efficient, sensitive, and stable ion regulation, extending the traditional nanochannel from single modulation to smart multimodulation.
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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".