Light-Driven Energy and Charge Transfer Processes between Additives within Electrospun Nanofibres
Bibliographic record
Abstract
Electrospinning is a cost-effective and efficient method of producing polymeric nanofibre films. The resulting nanofibres can be produced in a variety of structures, including monoaxial, coaxial (core@shell), and Janus (side-by-side). The resulting fibres can also act as a matrix for various light-harvesting components such as dye molecules, nanoparticles, and quantum dots. The addition of these light-harvesting materials allows for various photo-driven processes to occur within the films. This review discusses the process of electrospinning as well as the effect of spinning parameters on resulting fibres. Building on this, we discuss energy transfer processes that have been explored in nanofibre films, such as Förster resonance energy transfer (FRET), metal-enhanced fluorescence (MEF), and upconversion. A charge transfer process, photoinduced electron transfer (PET), is also discussed. This review highlights various candidate molecules that have been used for photo-responsive processes in electrospun films.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".