Transition metal sulfides meet electrospinning: versatile synthesis, distinct properties and prospective applications
Bibliographic record
Abstract
One-dimensional (1D) electrospun nanomaterials have attracted significant attention due to their unique structures and outstanding chemical and physical properties such as large specific surface area, distinct electronic and mass transport, and mechanical flexibility. Over the past years, the integration of metal sulfides with electrospun nanomaterials has emerged as an exciting research topic owing to the synergistic effects between the two components, leading to novel and interesting properties in energy, optics and catalysis research fields for example. In this review, we focus on the recent development of the preparation of electrospun nanomaterials integrated with functional metal sulfides with distinct nanostructures. These functional materials have been prepared via two efficient strategies, namely direct electrospinning and post-synthesis modification of electrospun nanomaterials. In this review, we systematically present the chemical and physical properties of the electrospun nanomaterials integrated with metal sulfides and their application in electronic and optoelectronic devices, sensing, catalysis, energy conversion and storage, thermal shielding, adsorption and separation, and biomedical technology. Additionally, challenges and further research opportunities in the preparation and application of these novel functional materials are also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".