Soft X‐ray Detectors Based on SnS Nanosheets for the Water Window Region
Bibliographic record
Abstract
Abstract The structural characteristics of biological specimens, such as wet proteins and fixed living cells, can be conveniently probed in their host aqueous media using soft X‐rays in the water window region (200–600 eV). Conventional X‐ray detectors in this area exhibit low spatial resolution, have limited sensitivity, and require complex fabrication procedures. Here, many of these limitations are overcome by introducing a direct soft X‐ray detector based on ultrathin tin mono‐sulfide (SnS) nanosheets. The distinguishing characteristic of SnS is its high photon absorption efficiency in the soft X‐ray region. This factor enables the fabricated soft X‐ray detectors to exhibit excellent sensitivity values on the order of at peak energies of ≈ 600 eV. The peak signal is found to be sensitive to the number of stacked SnS layers, with thicker SnS nanosheet assemblies yielding a peak response at higher energies and with peak sensitives of over 2.5 at 1 V. Detailed current–voltage and temporal characteristics of these detectors are also presented. These results showcase the excellent performance of SnS nanosheet‐based soft X‐ray detectors compared to existing direct soft X‐ray detectors, including that of the emerging organic–inorganic perovskite class of 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) | 0.002 | 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".