Sequential Superassembly of Nanofiber Arrays to Carbonaceous Ordered Mesoporous Nanowires and Their Heterostructure Membranes for Osmotic Energy Conversion
Bibliographic record
Abstract
The capture of sustainable energy from a salinity gradient, in particular, using renewable biomass-derived functional materials, has attracted significant attention. In order to convert osmotic energy to electricity, many membrane materials with nanofluidic channels have been developed. However, the high cost, complex preparation process, and low output power density still restrict the practical application of traditional membranes. Herein, we report the synthesis of highly flexible and mechanically robust nanofiber-arrays-based carbonaceous ordered mesoporous nanowires (CMWs) through a simple and straightforward soft-templating hydrothermal carbonization approach. This sequential superassembly strategy shows a high yield and great versatility in controlling the dimensions of CMWs with the aspect ratio changes from about 3 to 39. Furthermore, these CMWs can be used as novel building blocks to construct functional hybrid membranes on macroporous alumina. This nanofluidic membrane with asymmetric geometry and charge polarity exhibits low resistance and high-performance energy conversion. This work opens a solution-based route for the one-pot preparation of CMWs and functional heterostructure membranes for various applications.
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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".