Sustainable Electrochemical Synthesis of Porous g-C<sub>3</sub>N<sub>4</sub> Nanosheets via 3D-Printed Platinized Electrodes for Enhanced Photocatalytic Activity
Bibliographic record
Abstract
Graphitic carbon nitride (g-C 3 N 4 ), a polymeric metal-free catalyst, is extensively used to degrade industrial toxic waste that contaminates the aqueous system. However, commonly synthesized bulk g-C 3 N 4 is prone to agglomeration, leading to low surface area with fewer effective photoactive centers, limiting its potential toward the facile separation of photo-excitons and resulting in low photocatalytic activity. This study introduces an innovative electrochemical synthesis of in situ exfoliated porous g-C 3 N 4 nanosheets (GCN NSs) featuring a large surface area with effective separation of photo-excitons, leading to the facile production of reactive oxygen species (ROS). The GCN NSs are uniformly dispersed in an alkaline solution grown via a newly designed electrochemical process using 3D-printed platinumized titanium mesh as both anode and cathode under rigorous stirring for 40 min. The morphological study, along with surface area determination, reveals that the as-grown carbonaceous matrix is highly exfoliated with an inherent nanoporous architecture, having a high surface area of 163.73 m 2 g –1 with an average pore diameter of 8.311 nm. The electrochemically synthesized GCN NSs demonstrate excellent charge transfer kinetics with low charge transfer resistance and superior photocatalytic activity of 98% degradation efficiency against various organic dyes (concentration of 10 ppm) under simulated solar irradiation for 120 min with 5 mg of catalyst. Kinetic studies of the photodegradation process indicate that the reaction follows pseudo-first-order kinetics, with the rate constant of 3.59 × 10 –2 min –1, which is approximately 1.8 times higher as compared to the recent findings. A plausible mechanistic understanding reveals that photogenerated holes and hydroxyl radicals ( • OH) are the primary species for the overall photodegradation process. The stability test depicts that the photocatalyst maintains its efficiency over five consecutive runs with a minimum loss of 7%. This research offers valuable insights into the design and synthesis of advanced photocatalysts with optimized architectures for enhanced industrial waste management.
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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) | 0.000 | 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 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".