Electrokinetic Assembly of Selenium and Silver Nanowires into Macroscopic Fibers
Bibliographic record
Abstract
Solution-phase synthesized nanowires with high aspect ratios can be a challenge to assemble into desired structures. As synthesized, these nanostructures readily bend and entangle with each other to form larger aggregates. This manuscript reports a general procedure for directing the assembly of semiconducting and metallic nanowires into fibers that can easily span distances >1 cm. Dispersions of these nanostructures in a low dielectric solution are organized by electrokinetic techniques into fibers that can be isolated from solution. Theoretical studies suggest that the assembled fibers adopt an orientation along electric field lines in the solution. The number of assembled fibers is a function of the duration of the assembly process, the magnitude of the electric potential, and the initial concentration of nanowires dispersed in solution. These findings offer a general method for the assembly of nanowires into macroscopic fibers of tunable dimensions. Fibers of selenium nanowires isolated from solution can reversibly bend in response to a source of electrostatic charges positioned in close proximity to the free-standing fiber. These flexible selenium fibers also exhibit a photoconductive response when illuminated with white light.
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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".