Waveguide photoreactor enhances solar fuels photon utilization towards maximal optoelectronic – photocatalytic synergy
Bibliographic record
Abstract
Abstract A conventional light management approach on a photo-catalyst is to concentrate photo-intensity to enhance the catalytic rate. We present a counter-intuitive approach where light intensity is distributed below the electronic photo-saturation limit under the principle of light maximization. By operating below the saturation point of the photo-intensity induced hydroxide growth under reactant gaseous H2+CO2 atmosphere, a coating of defect engineered In2O3-x(OH)y nanorod Reverse Water Gas Shift solar-fuel catalyst on an optical waveguide outperforms a coated plane by a factor of 2.2. Further, light distribution along the length of the waveguide increases optical pathlengths of the weakly absorptive green and yellow wavelengths, which increases CO product rate by a factor of 8.1-8.7 in the visible. Synergistically pairing with thinly doped silicon on the waveguide enhances the CO production rate by 27% over the visible. In addition, the persistent photoconductivity behavior of the In2O3-x(OH)y system enables CO production at a comparable rate for 2 h after turning off photo-illumination, enhancing yield with 44-62% over thermal only yield. The practical utility of persistent photocatalysis was demonstrated through outdoor solar concentrator tests, which after a day-and-night cycle showed CO yield increase of 19% over a day-light only period.
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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) | 0.001 | 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".