Photocatalytic Thermodynamic Efficiency Factors. Practical Limits in Photocatalytic Reactors
Bibliographic record
Abstract
The photocatalytic thermodynamic efficiency factor (PTEF) is a parameter that can be used in photocatalytic reactors to establish photon energy utilization as the ratio of the energy used to generate OH • free radicals and the energy absorbed by the TiO 2 photocatalyst. The PTEF evaluation requires the assessment of the total rate of OH • free radicals at any given time during the photoconversion of organic species. A key parameter in this assessment is the availability of the complete spectrum of measurable chemical species including various intermediates. Quantification of different chemical species and their evolution with irradiation time allow via stoichiometric relationships the calculation of the OH • radicals consumed in the photocatalytic reactor. PTEFs and quantum yields (QY) were reported recently for phenol photocatalytic conversion in water media (free of iron ions) displaying 71% and 19% maximum QYs and PTEFs, respectively. 19 In the present study, the QY and PTEF are reviewed further, considering the photoconversion of phenol in water media enhanced by iron ions. It is shown using the more realistic RN2 model that the maximum QYs and PTEFs reach up to 85% and 23% levels, respectively. These encouraging efficiency factors demonstrate the favorable prospects of photocatalysis and Photo-CREC Water reactors operated under optimum photocatalyst loading conditions (0.14 g/L), with only a small fraction of the total absorbed photons potentially lost in photon recombination.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".