Photoelectrochemical Response of Tin Iodide Phosphide (SnIP) Composites with MoSe <sub>2</sub> , MoS <sub>2</sub> , and h‐BN
Bibliographic record
Abstract
Abstract For a future world fuelled by green energy it is invaluable to develop, test and maximise the catalytic efficiency of new effective water‐splitting materials. In this paper, we further explore the catalytic activity of double‐helical tin iodide phosphide (SnIP), as it features bandgaps in the ideal region for this process. We found that its photoelectrochemical response can be multiplied by forming composites of SnIP with selected 2D materials, focusing on hexagonal boron nitride and the transition metal dichalcogenides (TMDs) MoSe 2 and MoS 2 . These nanocomposites were analysed with Powder‐X‐ray diffraction (P‐XRD), Raman, and UV/VIS bandgap determination. Their photo activity was assessed under simulated solar light through chrono amperometry and linear sweep voltammetry (CA, LSV). The high anisotropy of the involved materials enables efficient charge separation at the 1D/2D interfaces, increasing photoelectrochemical response four‐fold.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".