Photocatalytic Fuel Cell Incorporated With Persulphate Activation for Electricity Production by Diluted Palm Oil Mill Effluent Treatment
Bibliographic record
Abstract
In this study, a new and effective photocatalytic fuel cell incorporated with peroxydisulfate activation (PDS/PFC) system was devised to treat diluted palm oil mill effluent (POME) and electricity production.This designed system contained ZnO nanorod array (NRA)/Zn photoanode and copper oxide/Cu cathode.Compared to the PDS/photocatalytic (PC) activation and PFC alone, the PDS/PFC system revealed exceptional performance.Using 0.5 mM PDS, the PDS/PFC system exhibited the remarkable chemical oxygen demand (COD) removal efficiency of 78.4% and maximum power density (Pmax) of 6.981 mW cm -2 .The boosted photoeletrocatalytic activity can be attributed to the addition of PDS to extend the active species reactions from the interface of electrodes to the whole POME solution.Moreover, the PDS can serve as an effective electron acceptor to suppress the charge carrier recombination.The best PDS concentration was also scrutinized in the developed PDS/PFC system.The radical scavenging tests were also carried out to testify the existence of active species in the mineralization reaction.The comprehensive photoelectrocatalytic mechanism was finally elucidated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".