Flexible and Ultrasoft Inorganic 1D Semiconductor and Heterostructure Systems Based on SnIP
Bibliographic record
Abstract
Low dimensionality and high flexibility are key demands for flexible electronic semiconductor devices. SnIP, the first atomic‐scale double helical semiconductor combines structural anisotropy and robustness with exceptional electronic properties. The benefit of the double helix, combined with a diverse structure on the nanoscale, ranging from strong covalent bonding to weak van der Waals interactions, and the large structure and property anisotropy offer substantial potential for applications in energy conversion and water splitting. It represents the next logical step in downscaling the inorganic semiconductors from classical 3D systems, via 2D semiconductors like MXenes or transition metal dichalcogenides, to the first downsizeable, polymer‐like atomic‐scale 1D semiconductor SnIP. SnIP shows intriguing mechanical properties featuring a bulk modulus three times lower than any IV, III‐V, or II‐VI semiconductor. In situ bending tests substantiate that pure SnIP fibers can be bent without an effect on their bonding properties. Organic and inorganic hybrids are prepared illustrating that SnIP is a candidate to fabricate flexible 1D composites for energy conversion and water splitting applications. SnIP@C3N4 hybrid forms an unusual soft material core–shell topology with graphenic carbon nitride wrapping around SnIP. A 1D van der Waals heterostructure is formed capable of performing effective water splitting.
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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) | 0.003 | 0.001 |
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".