An Electrospinning Approach to the Design of Gas-phase Heterogeneous Photocatalytic Systems
Bibliographic record
Abstract
This thesis is focused on solar fuels systems that use gas-phase heterogeneous (GPH) photocatalysis to convert CO2, the primary greenhouse gas driving climate change, into value added products. A solar fuels system’s ability to capture light energy in chemical bonds through photocatalytic reactions is crucial to its performance but can be very difficult to isolate from other factors. This work aims to use the highly versatile technique of electrospinning to gain better control over the parameters of a solar fuels system, with the intention to inform improved lab-scale characterization of photocatalytic materials, as well as to instruct system design approaches for scaling up such a system to a commercial scale. An iterative approach is applied to fabricating highly tunable photocatalytic layers, ultimately producing a repeatable and scalable support structure for photocatalysts that offers the flexibility and control required at multiple stages in the technology’s development.
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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 | 0.001 |
| 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".